U.S. patent application number 16/848710 was filed with the patent office on 2021-06-10 for generative modeling methods and systems for simulating sensor measurements.
The applicant listed for this patent is MEDTRONIC MINIMED, INC.. Invention is credited to Peter Ajemba, Bahman Engheta, Elaine Gee, Jeffrey Nishida, Keith Nogueira, Andrea Varsavsky.
Application Number | 20210174960 16/848710 |
Document ID | / |
Family ID | 1000004800239 |
Filed Date | 2021-06-10 |
United States Patent
Application |
20210174960 |
Kind Code |
A1 |
Gee; Elaine ; et
al. |
June 10, 2021 |
GENERATIVE MODELING METHODS AND SYSTEMS FOR SIMULATING SENSOR
MEASUREMENTS
Abstract
Medical devices and related systems and methods are provided. A
method of estimating a physiological condition involves obtaining
reference measurement data for the physiological condition,
obtaining first measurement data corresponding to the reference
measurement data from one or more instances of the first sensing
arrangement, determining a generative model associated with the
first sensing arrangement based on relationships between the first
measurement data and the reference measurement data, obtaining
second reference measurement data for the physiological condition,
generating simulated measurement data corresponding to the second
reference measurement data by applying the generative model to the
second reference measurement data, and determining an estimation
model for the physiological condition based on relationships
between the simulated measurement data and the second reference
measurement data, wherein the estimation model is applied to
subsequent measurement output provided by an instance of the first
sensing arrangement to obtain an estimated value for the
physiological condition.
Inventors: |
Gee; Elaine; (Windsor,
CA) ; Ajemba; Peter; (Canyon Country, CA) ;
Engheta; Bahman; (Santa Monica, CA) ; Nishida;
Jeffrey; (Chicago, IL) ; Varsavsky; Andrea;
(Santa Monica, CA) ; Nogueira; Keith; (Mission
Hills, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
MEDTRONIC MINIMED, INC. |
Northridge |
CA |
US |
|
|
Family ID: |
1000004800239 |
Appl. No.: |
16/848710 |
Filed: |
April 14, 2020 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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62945800 |
Dec 9, 2019 |
|
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06N 3/088 20130101;
A61B 5/7275 20130101; A61M 2230/201 20130101; A61B 2560/0228
20130101; G16H 70/60 20180101; A61B 5/14532 20130101; G06N 3/0454
20130101; G16H 20/17 20180101; A61M 2205/50 20130101; A61B 5/1451
20130101; A61B 5/7267 20130101; G16H 50/50 20180101; G16H 40/67
20180101; G16H 50/30 20180101; A61M 5/1723 20130101; G16H 50/20
20180101; A61M 2205/70 20130101 |
International
Class: |
G16H 50/20 20060101
G16H050/20; A61B 5/145 20060101 A61B005/145; A61M 5/172 20060101
A61M005/172; A61B 5/00 20060101 A61B005/00; G16H 50/30 20060101
G16H050/30; G16H 70/60 20060101 G16H070/60; G16H 40/67 20060101
G16H040/67; G16H 50/50 20060101 G16H050/50; G16H 20/17 20060101
G16H020/17; G06N 3/08 20060101 G06N003/08; G06N 3/04 20060101
G06N003/04 |
Claims
1. A method of estimating a physiological condition using a first
sensing arrangement influenced by the physiological condition, the
method comprising: obtaining, by a computing device, reference
measurement data for the physiological condition; obtaining, by the
computing device, first measurement data corresponding to the
reference measurement data from one or more instances of the first
sensing arrangement; determining, by the computing device, a
generative model associated with the first sensing arrangement
based on relationships between the first measurement data and the
reference measurement data; obtaining, by the computing device,
second reference measurement data for the physiological condition;
generating, by the computing device, simulated measurement data
corresponding to the second reference measurement data by applying
the generative model to the second reference measurement data; and
determining, by the computing device, an estimation model for the
physiological condition based at least in part on relationships
between the simulated measurement data and the second reference
measurement data, wherein the estimation model is applied to
subsequent measurement output provided by an instance of the first
sensing arrangement to obtain an estimated value for the
physiological condition.
2. The method of claim 1, the physiological condition comprising a
glucose level, wherein the reference measurement data comprises
calibration measurement data obtained from instances of a blood
glucose meter.
3. The method of claim 2, wherein the calibration measurement data
comprises reference calibration factor values, reference blood
glucose values or other reference values and corresponding
calibration timestamps.
4. The method of claim 2, the first sensing arrangement comprising
a glucose sensing arrangement, wherein obtaining the first
measurement data corresponding to the reference measurement data
comprises obtaining the first measurement data from the one or more
instances of the glucose sensing arrangement temporally associated
with calibration data points corresponding to the calibration
measurement data obtained from instances of the blood glucose
meter.
5. The method of claim 4, wherein obtaining the second reference
measurement data for the physiological condition comprises
obtaining second calibration measurement data from second instances
of the blood glucose meter.
6. The method of claim 5, wherein generating the simulated
measurement data comprises applying the generative model to the
second calibration measurement data.
7. The method of claim 2, wherein obtaining the second reference
measurement data for the physiological condition comprises
obtaining second calibration measurement data from second instances
of the blood glucose meter.
8. The method of claim 7, wherein generating the simulated
measurement data comprises applying the generative model to the
second calibration measurement data.
9. The method of claim 8, wherein the second calibration
measurement data comprises a reference blood glucose value and a
time after insertion associated with respective calibration data
points and the simulated measurement data comprises simulated
output measurements for the first sensing arrangement for the time
after insertion as a function of the reference blood glucose value
and the time after insertion.
10. The method of claim 1, further comprising determining a
delivery command for operating an actuation arrangement of an
infusion device based at least in part on the estimated value for
the physiological condition.
11. A method of estimating a glucose level using a glucose sensing
arrangement, the method comprising: obtaining a generative model
associated with the glucose sensing arrangement; obtaining
reference glucose measurement data from a blood glucose meter;
applying the generative model to the reference glucose measurement
data to obtain simulated measurement data for the glucose sensing
arrangement; determining an estimation model for the glucose
sensing arrangement based at least in part on relationships between
the simulated measurement data and the reference glucose
measurement data; and applying the estimation model to one or more
output measurements from a sensing element of the glucose sensing
arrangement to obtain an estimated value for the glucose level
using the glucose sensing arrangement.
12. The method of claim 11, further comprising developing the
generative model for predicting output measurements of the glucose
sensing arrangement based on relationships between measurement data
obtained from the glucose sensing arrangement and temporally
associated calibration data.
13. The method of claim 12, wherein developing the generative model
comprises developing the generative model for predicting output
measurements of the glucose sensing arrangement as a function of a
calibration factor of the glucose sensing arrangement.
14. The method of claim 12, wherein developing the generative model
comprises developing the generative model for predicting output
measurements of the glucose sensing arrangement as a function of an
age of the glucose sensing arrangement.
15. The method of claim 12, wherein developing the generative model
comprises: training a first neural network as a generator model
using respective pairs of calibration factor values and a time
after insertion value of the temporally associated calibration data
as conditional inputs to the generator model with temporally
associated measurement data as corresponding outputs to be produced
by the generator model; and training a second neural network as a
discriminator model using the measurement data and simulated data
outputs from the generator model as inputs to the discriminator
model, wherein: the generator model provides time-dependent
synthetic values for the simulated measurement data as a function
of sensor ages and reference glucose measurement data; and the
discriminator model provides indication of whether the input
time-dependent signals are likely realistic.
16. The method of claim 15, further comprising training the
generator model in concert with the discriminator model such that
the generator model provides the time-dependent synthetic values
that are optimized based on the output of the discriminator
model.
17. The method of claim 11, wherein: applying the generative model
to the reference glucose measurement data to obtain simulated
measurement data for the glucose sensing arrangement comprises
applying the generative model to historical calibration data points
previously obtained in connection with a different glucose sensing
arrangement to generate the simulated measurement data representing
a probable electrochemical behavior of the glucose sensing
arrangement to the respective conditions that resulted in the
historical calibration data points; the historical calibration data
points comprise respective combinations of a respective calibration
factor value and a respective sensor age for an instance of the
different glucose sensing arrangement; the glucose sensing
arrangement comprises a new glucose sensor; and the different
glucose sensing arrangement comprises a legacy glucose sensor.
18. The method of claim 11, wherein: determining the estimation
model comprises training a glucose estimation model using the
simulated measurement data as an input of the glucose estimation
model and using the corresponding reference glucose measurement
data as an output of the glucose estimation model; and the
estimated value for the glucose level is calculated as a function
of one or more output measurements from the sensing element using
the glucose estimation model.
19. A system comprising: a database to store historical calibration
data corresponding to instances of a first sensing arrangement, the
historical calibration data comprising respective sets of a
reference measurement value, a calibration factor value, and a
timestamp value; and a server coupled to the database and a network
to obtain the historical calibration data from the database,
determine a generative model based on relationships between
measurement data obtained from a second sensing arrangement and
temporally associated calibration data, apply the generative model
associated with the second sensing arrangement to the historical
calibration data to obtain respective sets of simulated measurement
data for the second sensing arrangement as a function of the
respective sets of the calibration factor value and the timestamp
value, determine an estimation model for the second sensing
arrangement based at least in part on relationships between the
respective sets of simulated measurement data and the corresponding
respective reference measurement values, and provide the estimation
model associated with the second sensing arrangement to a computing
device via the network, wherein the computing device applies the
estimation model to one or more output measurements from a sensing
element of an instance of the second sensing arrangement to obtain
an estimated calibrated measurement value.
20. The system of claim 19, wherein: the first sensing arrangement
comprises a legacy glucose sensor; the second sensing arrangement
comprises a new glucose sensor; the estimation model comprises a
glucose estimation model; and the estimated calibrated measurement
value comprises an estimated blood glucose value.
Description
CROSS-REFERENCE TO RELATED APPLICATION(S)
[0001] This application claims the benefit of U.S. Provisional
Patent Application Ser. No. 62/945,800, filed Dec. 9, 2019, the
contents of which are incorporated by reference herein in its
entirety.
TECHNICAL FIELD
[0002] Embodiments of the subject matter described herein relate
generally to medical devices, and more particularly, embodiments of
the subject matter relate to calibrating sensing devices for use
with medical devices, such as fluid infusion devices.
BACKGROUND
[0003] Infusion pump devices and systems are relatively well known
in the medical arts, for use in delivering or dispensing an agent,
such as insulin or another prescribed medication, to a patient.
Control schemes have been developed to allow insulin infusion pumps
to monitor and regulate a patient's blood glucose level in a
substantially continuous and autonomous manner. Rather than
continuously sampling and monitoring a user's blood glucose level,
which may compromise battery life, intermittently sensed glucose
data samples are often utilized for purposes of continuous glucose
monitoring (CGM) or determining operating commands for the infusion
pump.
[0004] Many continuous glucose monitoring (CGM) sensors measure the
glucose in the interstitial fluid (ISF). Typically, to achieve the
desired level of accuracy and reliability and reduce the impact of
noise and other spurious signals, the sensor data is calibrated
using a known good blood glucose value, often obtained via a
so-called "fingerstick measurement" using blood glucose meters that
measures the blood glucose in the capillaries. However, performing
such calibration measurements increases the patient burden and
perceived complexity, and can be inconvenient, uncomfortable, or
otherwise disfavored by patients. Moreover, ISF glucose
measurements lag behind the blood glucose measurements based on the
time it takes glucose to diffuse from the capillary to the
interstitial space where it is measured by the CGM sensor, which
requires signal processing (e.g., filtering) or other techniques to
compensate for physiological lag. Additionally, various factors can
lead to transient changes in the sensor output, which may influence
the accuracy of the calibration. Degradation of sensor performance
over time or manufacturing variations may further compound these
problems.
[0005] To decrease the patient burden associated with obtaining
reference measurements and improve the user experience, machine
learning or artificial intelligence techniques have been utilized
to develop models for generating calibrated measurements. However,
to avoid compromising accuracy or reliability, these approaches
often require relatively large data sets to achieve the desired
model performance. Obtaining large data sets often entails
increased time and costs associated with data collection, which is
a significant clinical burden when utilizing such modeling
techniques in newly developed sensing devices that is a challenge
to condensed development cycles or relatively limited clinical
trials (e.g., a limited number of patients and/or a limited trial
duration). Accordingly, it is desirable to provide support for
model-based calibration in situations where limited data is
available.
BRIEF SUMMARY
[0006] Medical devices and related systems and operating methods
are provided for generating simulated measurement data. The
simulated measurement data may be utilized to develop glucose
estimation models or other predictive models for a physiological
condition based on measurement signals output by a given sensing
arrangement. One exemplary method of estimating a physiological
condition using a first sensing arrangement influenced by the
physiological condition involves determining, by a computing
device, a translation model based at least in part on relationships
between first measurement data corresponding to instances of the
first sensing arrangement and second measurement data corresponding
to instances of a second sensing arrangement, wherein the second
sensing arrangement is different from the first sensing
arrangement, obtaining, by the computing device, third measurement
data associated with the second sensing arrangement, determining,
by the computing device, simulated measurement data for the first
sensing arrangement by applying the translation model to the third
measurement data, and determining, by the computing device, an
estimation model for the physiological condition using the
simulated measurement data, wherein the estimation model is applied
to subsequent measurement output provided by an instance of the
first sensing arrangement to obtain an estimated value for the
physiological condition.
[0007] In another embodiment, a method of estimating a glucose
level using a first glucose sensing arrangement involves obtaining
a translation model for output of the first glucose sensing
arrangement, the translation model being based at least in part on
relationships between first sensor measurement data obtained from
instances of the first glucose sensing arrangement and second
sensor measurement data corresponding to instances of a second
glucose sensing arrangement, the second glucose sensing arrangement
having a type or configuration different from the first glucose
sensing arrangement, obtaining historical sensor measurement data
associated with the second glucose sensing arrangement, obtaining
historical reference measurement data corresponding to the
historical sensor measurement data, determining simulated sensor
measurement data for the first glucose sensing arrangement by
applying the translation model to the historical sensor measurement
data, deriving a glucose estimation model for the first glucose
sensing arrangement using the simulated sensor measurement data and
the historical reference measurement data, and thereafter inputting
measurement outputs from an instance of the first glucose sensing
arrangement to the glucose estimation model to obtain an estimated
sensor glucose measurement value.
[0008] In another embodiment, a system is provided that includes a
database to store first sensor measurement data corresponding to
instances of a first sensing arrangement influenced by a
physiological condition and second sensor measurement data
corresponding to instances of a second sensing arrangement
influenced by the physiological condition, wherein a type or
configuration of the second sensing arrangement is different from
the first sensing arrangement, and a server coupled to the database
and a network to determine a translation model associated with the
first sensing arrangement based at least in part on relationships
between the first sensor measurement data and a first subset of the
second sensor measurement data, determine simulated measurement
data for the first sensing arrangement by applying the translation
model to a second subset of the second sensor measurement data,
determine an estimation model for the physiological condition using
the simulated measurement data, and provide the estimation model
associated with the first sensing arrangement to a computing device
via the network.
[0009] In another exemplary embodiment, a method of estimating a
physiological condition using a first sensing arrangement is
provided. The method involves obtaining, by a processing system of
the first sensing arrangement, a sensor translation model
associated with a relationship between the first sensing
arrangement and a second sensing arrangement, wherein the second
sensing arrangement is different from the first sensing
arrangement, obtaining, by the processing system, one or more
measurements from a sensing element coupled to the processing
system of the first sensing arrangement, determining, by the
processing system, simulated measurement data for the second
sensing arrangement by applying the sensor translation model to the
one or more measurements from the sensing element of the first
sensing arrangement, and determining, by the processing system, an
estimated value for the physiological condition by applying an
estimation model for the physiological condition associated with
the second sensing arrangement to the simulated measurement
data.
[0010] In another embodiment, a method of estimating a glucose
level using a first glucose sensing arrangement involves obtaining
a translation model for output of the first glucose sensing
arrangement, the translation model being based at least in part on
relationships between first sensor measurement data obtained from
instances of the first glucose sensing arrangement and second
sensor measurement data corresponding to instances of a second
glucose sensing arrangement, the second glucose sensing arrangement
having a type or configuration different from the first glucose
sensing arrangement, obtaining one or more measurements from a
sensing element coupled to a processing system of the first glucose
sensing arrangement, determining simulated measurement data in a
measurement domain associated with the second glucose sensing
arrangement by applying the translation model to the one or more
measurements, and determining an estimated value for the glucose
level by applying a sensor glucose estimation model associated with
the second glucose sensing arrangement to the simulated measurement
data.
[0011] In another embodiment, an apparatus for a glucose sensor is
provided. The glucose sensor includes a data storage element to
maintain a sensor translation model associated with a relationship
with a different glucose sensor and a glucose estimation model
associated with the different glucose sensor, an output interface,
a glucose sensing element to obtain one or more measurements
influenced by a glucose level of a patient, and a processing system
coupled to the glucose sensing element and the data storage element
to determine simulated measurement data by applying the sensor
translation model to the one or more measurements, determine an
estimated value for the glucose level of the patient by applying
the glucose estimation model to the simulated measurement data, and
provide the estimated value to the output interface.
[0012] In another exemplary embodiment, a method of estimating a
physiological condition using a first sensing arrangement
influenced by the physiological condition involves obtaining, by a
computing device, reference measurement data for the physiological
condition, obtaining, by the computing device, first measurement
data corresponding to the reference measurement data from one or
more instances of the first sensing arrangement, determining, by
the computing device, a generative model associated with the first
sensing arrangement based on relationships between the first
measurement data and the reference measurement data, obtaining, by
the computing device, second reference measurement data for the
physiological condition, generating, by the computing device,
simulated measurement data corresponding to the second reference
measurement data by applying the generative model to the second
reference measurement data, and determining, by the computing
device, an estimation model for the physiological condition based
at least in part on relationships between the simulated measurement
data and the second reference measurement data, wherein the
estimation model is applied to subsequent measurement output
provided by an instance of the first sensing arrangement to obtain
an estimated value for the physiological condition.
[0013] In another embodiment, a method of estimating a glucose
level using a glucose sensing arrangement involves obtaining a
generative model associated with the glucose sensing arrangement,
obtaining reference glucose measurement data from one or more
instances of a blood glucose meter, applying the generative model
to the reference glucose measurement data to obtain simulated
measurement data for the glucose sensing arrangement, determining
an estimation model for the glucose sensing arrangement based at
least in part on relationships between the simulated measurement
data and the reference glucose measurement data, and applying the
estimation model to one or more output measurements from a sensing
element of the glucose sensing arrangement to obtain an estimated
value for the glucose level using the glucose sensing
arrangement.
[0014] In another embodiment, a system is provided that includes a
database to store historical calibration data corresponding to
instances of a first sensing arrangement, the historical
calibration data comprising respective sets of a reference
measurement value, a calibration factor value, and a timestamp
value, and a server coupled to the database and a network to obtain
the historical calibration data from the database, determine a
generative model based on relationships between measurement data
obtained from a second sensing arrangement and temporally
associated calibration data, apply the generative model associated
with the second sensing arrangement to the historical calibration
data to obtain respective sets of simulated measurement data for
the second sensing arrangement as a function of the respective sets
of the calibration factor value and the timestamp value, determine
an estimation model for the second sensing arrangement based at
least in part on relationships between the respective sets of
simulated measurement data and the corresponding respective
reference measurement values, and provide the estimation model
associated with the second sensing arrangement to a computing
device via the network, wherein the computing device applies the
estimation model to one or more output measurements from a sensing
element of an instance of the second sensing arrangement to obtain
an estimated calibrated measurement value.
[0015] This summary is provided to introduce a selection of
concepts in a simplified form that are further described below in
the detailed description. This summary is not intended to identify
key features or essential features of the claimed subject matter,
nor is it intended to be used as an aid in determining the scope of
the claimed subject matter.
BRIEF DESCRIPTION OF THE DRAWINGS
[0016] A more complete understanding of the subject matter may be
derived by referring to the detailed description and claims when
considered in conjunction with the following figures, wherein like
reference numbers refer to similar elements throughout the figures,
which may be illustrated for simplicity and clarity and are not
necessarily drawn to scale.
[0017] FIG. 1 depicts an exemplary embodiment of data management
system;
[0018] FIG. 2 is a block diagram of an exemplary embodiment of a
sensing arrangement suitable for use in the data management system
of FIG. 1;
[0019] FIG. 3 is a flow diagram of an exemplary sensor translation
process suitable for use with the data management system of FIG. 1
in one or more exemplary embodiments;
[0020] FIG. 4 is a graph depicting relationships between observed
sensor measurement data and simulated sensor measurement data in
connection with an exemplary embodiment of the sensor translation
process of FIG. 3;
[0021] FIG. 5 is a flow diagram of an exemplary real-time
translation process suitable for use with the data management
system of FIG. 1 in one or more exemplary embodiments;
[0022] FIG. 6 is a flow diagram of an exemplary generative modeling
process suitable for use with the data management system of FIG. 1
in one or more exemplary embodiments; and
[0023] FIG. 7 depicts an exemplary embodiment of patient monitoring
system suitable for use with one or more of the sensor translation
process of FIG. 3, the real-time translation process of FIG. 5, and
the generative modeling process of FIG. 6 in one or more exemplary
embodiments.
DETAILED DESCRIPTION
[0024] The following detailed description is merely illustrative in
nature and is not intended to limit the embodiments of the subject
matter or the application and uses of such embodiments. As used
herein, the word "exemplary" means "serving as an example,
instance, or illustration." Any implementation described herein as
exemplary is not necessarily to be construed as preferred or
advantageous over other implementations. Furthermore, there is no
intention to be bound by any expressed or implied theory presented
in the preceding technical field, background, brief summary or the
following detailed description.
[0025] Exemplary embodiments of the subject matter described herein
generally relate to calibrating sensing elements and related
sensing arrangements and devices that provide an output that is
indicative of and/or influenced by one or more characteristics or
conditions that are sensed, measured, detected, or otherwise
quantified by the sensing element. While the subject matter
described herein is not necessarily limited to any particular type
of sensing application, exemplary embodiments are described herein
primarily in the context of a sensing element that generates or
otherwise provides electrical signals indicative of and/or
influenced by a physiological condition in a body of a human user
or patient, such as, for example, interstitial glucose sensing
elements that provide electrochemical signals indicative of and/or
influenced by a glucose level in an interstitial fluid
compartment.
[0026] As described in greater detail below, observed measurement
data obtained using different instances of a sensing arrangement or
sensing element is utilized to characterize the electrochemical
behavior of the particular sensing arrangement or sensing element
and generate a corresponding model of the electrochemical response
by the particular sensing arrangement or sensing element. The
electrochemical response model is then utilized to generate
simulated measurement data for different representative instances
of the particular sensing arrangement or sensing element. The
simulated measurement data is utilized, either individually or in
combination with the observed measurement data, to generate a
corresponding model for converting the electrical signals output by
respective instances of the sensing arrangement or sensing element
into an estimated calibrated measurement value. In this regard, by
increasing the size of the data set being analyzed and modeled by
including the simulated measurement data, the accuracy or other
performance characteristics of the resultant model is improved. As
described in greater detail below, in exemplary embodiments, a
glucose estimation model for mapping one or more electrical signals
output by an interstitial glucose sensing element into an estimated
calibrated interstitial glucose measurement value is developed,
derived, or otherwise determined using simulated measurement data,
thereby improving the accuracy, reliability and/or other
performance characteristics of the glucose estimation model to
facilitate obtaining effectively calibrated measurement values for
the glucose level of a patient in a manner that reduces reliance on
so-called "fingerstick measurement" or other reference
measurements.
[0027] It should be noted that the subject matter described herein
is not necessarily limited to electrochemical signals, and in
practice, could be implemented in an equivalent manner in the
context of other types of sensors and other multi-dimensional
and/or time-dependent signals (e.g., optical signals, electrical
signals, or the like). Additionally, for purposes of explanation,
exemplary embodiments of the subject matter are described herein as
being implemented in conjunction with medical devices, such as
portable electronic medical devices. Although many different
applications are possible, the following description may focus on
glucose sensing devices, continuous glucose monitoring (CGM)
devices, or the like. That said, the subject matter may be
implemented in an equivalent manner in the context of other medical
devices, such as a fluid infusion device (or infusion pump) as part
of an infusion system deployment, injection pens (e.g., smart
injection pens), and the like. For the sake of brevity,
conventional techniques related to glucose sensing, glucose
monitoring, infusion system operation, insulin pump and/or infusion
set operation, and other functional aspects of the systems (and the
individual operating components of the systems) may not be
described in detail here. It should be noted that the subject
matter described herein can be utilized more generally in the
context of overall diabetes management or other physiological
conditions independent of or without the use of an infusion device
or other medical device (e.g., when oral medication is utilized),
and the subject matter described herein is not limited to any
particular type of medication. In this regard, the subject matter
is not limited to medical applications and could be implemented in
any device or application that includes or incorporates a sensing
element.
[0028] FIG. 1 depicts an exemplary embodiment of a data management
system 100 suitable for implementing the subject matter described
herein. The data management system 100 includes, without
limitation, a computing device 102 coupled to a database 104, with
the computing device 102 also being communicatively coupled to any
number of additional electronic devices 106, 108 over a
communications network 110, such as, for example, the Internet, a
cellular network, a wide area network (WAN), or the like. It should
be appreciated that FIG. 1 depicts a simplified representation of a
data management system 100 for purposes of explanation and is not
intended to limit the subject matter described herein in any
way.
[0029] In exemplary embodiments described herein, the electronic
devices 106, 108 are realized as sensing devices. That said, in
other embodiments, the computing device 102 may support
communications with other medical devices (e.g., an infusion
device, a monitoring device, and/or the like) and/or any number of
non-medical client electronic devices, such as, for example, a
mobile phone, a smartphone, a tablet computer, a smart watch, or
other similar mobile electronic device, or any sort of electronic
device capable of communicating with the computing device 102 via
the network 110, such as a laptop or notebook computer, a desktop
computer, a computer cluster, or the like. In this regard, although
FIG. 1 depicts the sensing devices 106, 108 communicating directly
with the computing device 102 over the network 110, in alternative
embodiments, the sensing devices 106, 108 may communicate
indirectly with the computing device 102 via one or more
intervening electronic devices (e.g., a patient's phone).
[0030] In one or more exemplary embodiments, sensing devices 106,
108 transmit, upload, or otherwise provide data or information to
the computing device 102 for processing at the computing device 102
and/or storage in the database 104. For example, as described in
greater detail below, a sensing device 106, 108 may include a
sensing element that is inserted into the body of a patient or
otherwise worn by the patient to obtain measurement data indicative
of a physiological condition in the body of the patient, with the
sensing device 106, 108 periodically uploading or otherwise
transmitting the measurement data to the computing device 102. In
one or more embodiments, the sensing element is an interstitial
glucose sensing element inserted into the body of a patient to
obtain measurement data indicative of a glucose level of the
interstitial fluid compartment in the body of the patient.
[0031] The computing device 102 generally represents a server or
other remote device configured to receive data or other information
from the sensing devices 106, 108, store or otherwise manage data
in the database 104, and analyze or otherwise monitor data received
from the sensing devices 106, 108 and/or stored in the database
104. In practice, the computing device 102 may reside at a location
that is physically distinct and/or separate from the electronic
devices 106, 108, such as, for example, at a facility that is owned
and/or operated by or otherwise affiliated with a manufacturer of
the sensing devices 106, 108 or other medical devices utilized in
connection with the data management system 100. For purposes of
explanation, but without limitation, the computing device 102 may
alternatively be referred to herein as a server, a remote server,
or variants thereof
[0032] The server 102 generally includes a processing system and a
data storage element (or memory) capable of storing programming
instructions for execution by the processing system, that, when
read and executed, cause processing system to create, generate, or
otherwise facilitate the applications or software to perform or
otherwise support the processes, tasks, operations, and/or
functions described herein. Depending on the embodiment, the
processing system may be implemented using any suitable processing
system and/or device, such as, for example, one or more processors,
central processing units (CPUs), graphics processing units (GPUs)
controllers, microprocessors, microcontrollers, processing cores
and/or other hardware computing resources configured to support the
operation of the processing system described herein. Similarly, the
data storage element or memory may be realized as a random access
memory (RAM), read only memory (ROM), flash memory, magnetic or
optical mass storage, or any other suitable non-transitory short or
long term data storage or other computer-readable media, and/or any
suitable combination thereof. In some embodiments, the server 102
may be implemented using a cluster of actual and/or virtual servers
operating in conjunction with each other in a conventional manner
(e.g., using load balancing, cluster management, and/or the like)
or otherwise configured to provide a "cloud-based" virtual server
system.
[0033] In exemplary embodiments, the database 104 is utilized to
store or otherwise maintain patient data for a plurality of
different patients. For example, the database 104 may store or
otherwise maintain reference blood glucose measurements (e.g., a
fingerstick or metered blood glucose value) for different patients
in association with the contemporaneous or current measurement
parameters output by the respective sensing device 106, 108
associated with a respective patient at or around the time of the
respective blood glucose measurement. Additionally, the database
104 may maintain personal information associated with the different
patients, including the respective patient's age, gender, height,
weight, body mass index (BMI), demographic data, and/or other
parameters characterizing the respective patient.
[0034] In the illustrated embodiment, the database 104 maintains
measurement data 112 associated with previous uses of different
instances of a particular make and/or model of sensing device 106,
alternatively referred to herein as a template sensing device (or
template sensor). In some embodiments, the make and/or model of
template sensing device 106 corresponds to an established or legacy
sensor design or legacy sensor configuration for which the database
104 already maintains modeling data 120 for converting measurement
outputs into a calibrated measurement value, and accordingly, for
purposes of explanation but without limitation, the template
sensing device 106 may alternatively be referred to herein as a
legacy sensing arrangement, legacy sensing device, legacy sensor or
a variant thereof. In this regard, the template sensor measurement
data 112 may include measurement outputs provided by the legacy
sensing device 106 (e.g., sampled electrochemical signal values)
that are indicative of a patient's glucose level along with
contemporaneous or corresponding reference blood glucose
measurements (e.g., a fingerstick or metered blood glucose value)
for the patient during the time period corresponding to the sensor
measurement outputs. For example, historical template sensor
measurement data 112 may include data obtained from different
patients during a prior clinical trial, where each patient's
reference blood glucose measurements (and corresponding calibration
timestamps) and measurement outputs provided by their respective
instance of the legacy sensing device 106 during the trial period
are maintained in association with one another (e.g., using one or
more identifiers assigned to the respective patient).
[0035] In the illustrated embodiment, the database 104 also
maintains measurement data 114 associated with uses of different
instances of a different make and/or model of sensing device 108,
that is, a make and/or model that is different from that of the
template sensor 106. In some embodiments, the make and/or model of
sensing device 108 corresponds to a new sensor design or new sensor
configuration (e.g., a new sensing arrangement) for which the
database 104 did not previously maintain modeling data 120 for
converting measurement outputs into a calibrated measurement value.
For purposes of explanation but without limitation, the sensing
device 108 may alternatively be referred to herein as a target
sensing device (or target sensor). In this regard, the target
sensor measurement data 114 may include data obtained from
different patients during a current, recent, or ongoing clinical
trial, where each patient's reference blood glucose measurements
(and corresponding calibration timestamps) and measurement outputs
provided by their respective instance of the new sensing device 108
during the trial period are maintained in association.
[0036] As described in greater detail below in the context of FIG.
3, in some embodiments, the database 104 also maintains measurement
data 116 associated with uses of different instances of the
template sensor 106 concurrently with or contemporaneously to
respective instances of the target sensor 108. For example, during
a clinical trial, individual patients may wear or otherwise utilize
a first instance of a template glucose sensor 106 concurrently with
a second instance of a target glucose sensor 108, thereby providing
different glucose measurement data sets for a common patient that
were concurrently obtained using different types or configurations
of glucose sensors. Thus, the measurement data 116 may include data
obtained from the same patients using respective instances of the
legacy sensor 106 during the current, recent, or ongoing clinical
trial for the target sensor 108 concurrently with obtaining the
target sensor measurement data 114, where measurement outputs
provided by the respective instances of the legacy sensing device
106 during the trial period are maintained in association with the
respective patient and/or the respective patient's instance of the
target sensor 108, thereby allowing for correlations to be
established between the different measurement outputs concurrently
provided by different sensing devices 106, 108 at or around the
same time for the same patient. In this regard, the relationships
between the target sensor measurement data 114 and the template
sensor measurement data 116 for the current clinical trial may be
utilized to derive a model for translating between datasets,
thereby allowing historical template sensor measurement data 112 to
be translated or otherwise converted into simulated measurement
data 118 for the target sensor 108. The new sensor simulated
measurement data 118 may then be analyzed, either independently or
in concert with the new sensor clinical measurement data 114, to
derive a model for calculating or otherwise determining an
estimated calibrated measurement value, such as an estimated
calibrated sensor glucose measurement value, as a function of the
electrochemical measurement outputs generated by an instance of the
target sensor 108.
[0037] Although not illustrated in FIG. 1, to support one or more
embodiments described herein, the database 104 may also store or
otherwise maintain logically distinct sets of training, testing,
and validation data for the different sensors 106, 108. For
example, the training set of data may be utilized to train one or
more models, with the test set of data being utilized to optimize
the resulting model(s) and the validation set of data being
utilized to validate the performance or accuracy of the model(s).
In this regard, in some embodiments, the depicted sets of data 112,
114, 116 may be partitioned or otherwise logically divided into
various training, testing, and/or validation subsets. In exemplary
embodiments, the training, testing, and validation data sets are
mutually exclusive.
[0038] In exemplary embodiments, the server 102 utilizes the
simulated measurement data 118 stored in the database 104 to
determine a glucose estimation model for a particular type,
configuration, make and/or model of sensing element and/or sensing
arrangement. Thereafter, the server 102 may store or otherwise
maintain the data 120 characterizing the sensor glucose estimation
model in the database 104 and subsequently provide the sensor
glucose estimation model to instances of the particular type or
configuration of sensing element and/or sensing arrangement. For
example, upon initialization of an instance of a sensing device
106, 108, the respective sensing device 106, 108 may be configured
to connect to the network 110 and download or otherwise obtain the
appropriate sensor glucose estimation model from the remote server
102 via the network 110. Thereafter, a controller or other
processing system of the sensing device 106, 108 may utilize the
sensor glucose estimation model to determine estimated calibrated
glucose measurement values for a patient independent of and/or
without requiring a fingerstick measurement or other calibration
procedure. In yet other embodiments, the sensor glucose estimation
model may be provided to another electronic device (e.g., an
infusion device or another electronic device in an infusion system)
that is configured to receive measurement outputs from a sensing
device 106, 108. In such embodiments, the infusion device or other
electronic device may utilize the obtained sensor glucose
estimation model to determine estimated calibrated glucose
measurement values using measurement outputs provided by the
particular sensing device 106, 108 without requiring a fingerstick
measurement or other calibration procedure.
[0039] As described in greater detail below in the context of FIG.
6, in some embodiments, rather than relying on concurrent template
sensor measurement data 116 (which may be absent from such
embodiments), the server 102 analyzes the target sensor measurement
data 114 using a generative adversarial network (GAN) or similar
machine learning technique to derive a generative model
representative of the electrochemical behavior of the target sensor
108. The generative model is then utilized by the server 102 to
generate or otherwise emulate probabilistically the measurement
outputs by theoretical instances of the target sensor 108, and
thereby obtain simulated measurement data 118 that represents the
expected electrochemical behavior and/or characteristics of the
target sensor 108. The server 102 may then similarly utilize the
simulated measurement data 118 stored in the database 104, either
independently or in combination with the observed target sensor
measurement data 114, to determine a glucose estimation model for a
particular type, configuration, make and/or model of sensing
element and/or sensing arrangement.
[0040] It should be appreciated that FIG. 1 is a simplified
representation of a data management system 100 for purposes of
explanation and is not intended to be limiting. In this regard, in
practice, various aspects of data storage and/or data processing
described herein in the context of the server 102 and/or the
database 104 could equivalently be implemented on or at a sensing
device 106, 108. For example, the sensing device 106, 108 may
implement or otherwise support a file system, an object store, or
the like to store or otherwise maintain measurement data or other
reference data, which in turn, may be analyzed by or at the sensing
device 106, 108 to generate training data, modeling data,
calibration data, and/or the like. That said, for purposes of
explanation, the subject matter may be described primarily in the
context of data storage at the database 104 with data processing
performed by or at the server 102.
[0041] FIG. 2 depicts an exemplary embodiment of a sensing
arrangement 200 suitable for use in the data management system 100
of FIG. 1 (e.g., as one or more of sensing devices 106, 108). in
accordance with one or more embodiments. The illustrated sensing
device 200 includes, without limitation, a controller 204, a
sensing element 202, an output interface 208, and a data storage
element (or memory) 206. The controller 204 is coupled to the
sensing element 202, the output interface 208, and the memory 206,
and the controller 204 is suitably configured to support the
operations, tasks, and/or processes described herein.
[0042] The sensing element 202 generally represents the component
of the sensing device 200 that is configured to generate, produce,
or otherwise output one or more electrical signals indicative of a
condition that is sensed, measured, or otherwise quantified by the
sensing device 200. In this regard, the physiological condition of
a user influences a characteristic of the electrical signal output
by the sensing element 202, such that the characteristic of the
output signal generated by the electrochemical response of the
sensing element 202 corresponds to or is otherwise correlative to
the physiological condition that the sensing element 202 is
sensitive to. In exemplary embodiments, the sensing element 202 is
realized as an interstitial glucose sensing element that generates
or otherwise provides one or more output electrical signals having
a current, voltage, or other characteristic associated therewith
that is correlative to the interstitial fluid glucose level that is
sensed or otherwise measured in the body of the patient by the
sensing arrangement 200. For example, the output measurement
parameters generated or otherwise provided by the electrochemical
response of the glucose sensing element 202 may include an
electrical current generated by the sensing element 202 in response
to a glucose concentration (alternatively referred to as an isig
value), one or more electrochemical impedance spectroscopy (EIS)
values (for one or more frequencies) or other measurements
indicative of a characteristic impedance associated with the
sensing element 202 in response to a glucose concentration, a
counter electrode voltage (Vctr) (e.g., the difference between
counter electrode potential and working electrode potential),
and/or the like. That said, it should be noted that the subject
matter described herein is not limited to signals indicative of a
physiological condition and could be implemented in an equivalent
manner for sensing elements generating signals indicative of
non-physiological conditions.
[0043] In some embodiments, the sensing element 202 is replaceable
or interchangeable within the sensing arrangement 200. For example,
a patient may periodically replace an interstitial glucose sensing
element (e.g., every 3 days) and reinsert the new interstitial
glucose sensing element at the same or different location on the
patient's body (alternatively referred to as a site location). In
this regard, in such embodiments, measurement data obtained from
the sensing arrangement 200 and/or sensing element 202 may be
associated with a particular instance of the sensing element 202
and/or the particular sensor site location utilized with that
respective instance of the sensing element 202 for purposes of
analyzing performance with respect to the age or site location of
the sensing element 202. In the context of such embodiments, sensor
age should be understood as referring to the amount or duration of
time for which an instance of the sensing element 202 has been in
use from the initial time of insertion.
[0044] Still referring to FIG. 2, the controller 204 generally
represents the processing system or other hardware, circuitry,
logic, firmware and/or other component(s) of the sensing device 200
that is coupled to the sensing element 202 to receive the
electrical signals output by the sensing element 202 and perform
various additional tasks, operations, functions and/or processes
described herein. For example, the controller 204 may filter,
analyze or otherwise process the electrical signals received from
the sensing element 202 to obtain a calibrated measurement of the
interstitial fluid glucose level. In one or more embodiments, the
sensing device 200 comprises an instance of the target sensor 108,
and the controller 204 utilizes a glucose estimation model derived
using the simulated measurement data 118 for the target sensor 108
to calculate or otherwise determine an effectively-calibrated
sensor glucose measurement value as a function of the output
measurement parameters (e.g., electrical current output, counter
electrode voltage, electrochemical impedance, and the like)
provided by the sensing element 202 of the instance of the target
sensor 108. In this regard, function, equation, and/or other data
for the model associated with the sensing element 202 may be stored
or otherwise maintained in the memory 206 and downloaded from or
otherwise provided by the remote server 102, as described in
greater detail below.
[0045] Depending on the embodiment, the controller 204 may be
implemented or realized with a general purpose processor, a
microprocessor, a controller, a microcontroller, a state machine, a
content addressable memory, an application specific integrated
circuit, a field programmable gate array, any suitable programmable
logic device, discrete gate or transistor logic, discrete hardware
components, or any combination thereof, designed to perform the
functions described herein. In this regard, the steps of a method
or algorithm described in connection with the embodiments disclosed
herein may be embodied directly in hardware, in firmware, in a
software executed by the controller 204, or in any practical
combination thereof. In exemplary embodiments, the controller 204
includes or otherwise accesses the data storage element or memory
206. The memory 206 may be realized using any sort of RAM, ROM,
flash memory, registers, hard disks, removable disks, magnetic or
optical mass storage, short or long term storage media, or any
other non-transitory computer-readable medium capable of storing
programming instructions, code, or other data for execution by the
controller 204. The computer-executable programming instructions,
when read and executed by the controller 204, cause the controller
204 to perform the tasks, operations, functions, and processes
described in greater detail below.
[0046] In some embodiments, the controller 204 includes an
analog-to-digital converter (ADC) or another similar sampling
arrangement that samples or otherwise converts the output
electrical signal(s) received from the sensing element 202 into
corresponding digital measurement data value(s) correlative to the
interstitial fluid glucose level sensed by the sensing element 202.
In other embodiments, the sensing element 202 may incorporate an
ADC and output a digital measurement value. In one or more
embodiments, the current of the electrical signal output by the
sensing element 202 is influenced by the user's interstitial fluid
glucose level, and the digital measurement data value is realized
as a current measurement value provided by an ADC based on an
analog electrical output signal from the sensing element 202.
[0047] The output interface 208 generally represents the hardware,
circuitry, logic, firmware and/or other components of the sensing
arrangement 200 that are coupled to the controller 204 for
outputting data and/or information from/to the sensing device 200,
for example, to/from the remote server 102 or another device on the
network 110. In this regard, in exemplary embodiments, the output
interface 208 is realized as a communications interface configured
to support communications to/from the sensing device 200. In such
embodiments, the communications interface 208 may include or
otherwise be coupled to one or more transceivers or communication
devices capable of supporting wireless communications between the
sensing device 200 and another electronic device (e.g., an infusion
device or another electronic device in an infusion system).
Alternatively, the communications interface 208 may be realized as
a port that is adapted to receive or otherwise be coupled to a
wireless adapter that includes one or more transceivers and/or
other components that support the operations of the sensing device
200 described herein. In other embodiments, the communications
interface 208 may be configured to support wired communications
to/from the sensing device 200. In yet other embodiments, the
output interface 208 may include or otherwise be realized as an
output user interface element, such as a display element (e.g., a
light-emitting diode or the like), a display device (e.g., a liquid
crystal display or the like), a speaker or another audio output
device, a haptic feedback device, or the like, for providing
notifications or other information to the user. In such
embodiments, the output user interface 208 may be integrated with
the sensing arrangement 200 (e.g., within a common housing) or
implemented separately.
[0048] It should be understood that FIG. 2 is a simplified
representation of a sensing device 200 for purposes of explanation
and is not intended to limit the subject matter described herein in
any way. In this regard, although FIG. 2 depicts the various
elements residing within the sensing device 200, one or more
elements of the sensing device 200 may be distinct or otherwise
separate from the other elements of the sensing device 200. For
example, the sensing element 202 may be separate and/or physically
distinct from the controller 204 and/or the communications
interface 208. Furthermore, features and/or functionality of
described herein as implemented by the controller 204 may
alternatively be implemented at another device within an infusion
system.
[0049] FIG. 3 depicts an exemplary embodiment of a sensor
translation process 300 for developing a model for estimating a
calibrated output of a sensing arrangement using simulated
measurement data obtained by translating measurement data from a
different sensing arrangement. The various tasks performed in
connection with the sensor translation process 300 may be performed
by hardware, firmware, software executed by processing circuitry,
or any combination thereof. For illustrative purposes, the
following description may refer to elements mentioned above in
connection with FIGS. 1-2. It should be appreciated that the sensor
translation process 300 may include any number of additional or
alternative tasks, the tasks need not be performed in the
illustrated order and/or the tasks may be performed concurrently,
and/or the sensor translation process 300 may be incorporated into
a more comprehensive procedure or process having additional
functionality not described in detail herein. Moreover, one or more
of the tasks shown and described in the context of FIG. 3 could be
omitted from a practical embodiment of the sensor translation
process 300 as long as the intended overall functionality remains
intact.
[0050] The illustrated sensor translation process 300 begins by
receiving or otherwise obtaining measurement data for the
particular type or configuration of sensing arrangement to be
modeled (task 302). For example, as described above, instances of a
target sensor 108 may be provided to a number of different
individuals or patients as part of a clinical trial. In this
regard, the target sensor measurement data 114 includes or
otherwise maintains, for each individual or patient, the
measurement parameters (e.g., output electrical current, counter
electrode voltage, electrochemical impedance, and the like) that
were generated, output, measured, or otherwise obtained by the
sensing element 202 of the respective sensor 108 being used by the
patient during the clinical trial. The target sensor measurement
data 114 may also maintain calibration data associated with the
respective patient (e.g., timestamped reference blood glucose
measurements and corresponding calibration factors) and/or other
data associated with the respective patient (e.g., demographic data
and/or the like).
[0051] The sensor translation process 300 also receives or
otherwise obtains concurrent measurement data for a different type
or configuration of sensing arrangement that is paired with a
respective instance of the sensing arrangement to be calibrated and
maintaining associations between the different sets of concurrent
measurement data (tasks 304, 306). For example, during the clinical
trial, instances of a template sensor 106 may be provided to the
same individuals or patients that are part of the clinical trial
for use concurrently with their respective instance of the target
sensor 108. Similar to the target sensor measurement data 114, the
template sensor measurement data 116 also includes or otherwise
maintains, for each individual or patient, the measurement
parameters (e.g., output electrical current, counter electrode
voltage, electrochemical impedance, and the like) that were
generated, output, measured, or otherwise obtained by the sensing
element 202 of the respective template sensor 106 being used by the
patient during the clinical trial. The template sensor measurement
data 116 and the target sensor measurement data 114 may each be
stored or maintained using one or more unique patient identifiers
that allows the remote server 102 to correlate measurements for the
same patient across the different sensors 106, 108. Additionally,
the samples that make up the template sensor measurement data 116
and the target sensor measurement data 114 may be timestamped to
allow the sensor translation process 300 to temporally associate
measurement samples for the same patient across the different
sensors 106, 108.
[0052] Still referring to FIG. 3, the sensor translation process
300 continues by calculating or otherwise determining a predictive
model for generating simulated measurement data for use in
developing a calibration algorithm for the target sensor based at
least in part on the relationships between the paired data sets
(task 308). In this regard, based on the relationship between the
target (or new) sensor measurement data and the concurrent or
contemporaneous template (or legacy) sensor measurement data for
each patient, a sensor data translation model is derived for
translating historical legacy sensor measurements to probable
measurement values that would likely be output or generated by the
target sensor 108 having a new or different design or configuration
for the given glucose dynamics that resulted in the respective
historical legacy sensor measurements. That is, the template sensor
measurement data 116 provides the input variable combinations by
which the synthetic data algorithms translate template sensor
measurement values into temporally associated simulated target
sensor measurement data 118.
[0053] In some embodiments, for each different measurement
parameter to be output by the target sensor 108 (e.g., electrical
current output, counter electrode voltage, electrochemical
impedance, and the like), analytical, machine learning, or
artificial intelligence techniques are utilized to determine which
combination of measurement parameters output by the template sensor
106 are correlated to or predictive of the respective measurement
parameter based on the relationships between the paired sets of
observed template measurement parameter values and observed
measurement parameter value for each of the patients for which
paired concurrent measurement data 114, 116 is maintained.
Additionally, the remote server 102 may utilize analytical, machine
learning, or artificial intelligence techniques to identify other
contextual or non-measurement variables that may be relevant to
modeling the measurement parameter of interest, such as, for
example, the patient's age, gender, or other demographic
attributes. For example, non-measurement variables may be utilized
to augment training of models to achieve better dataset balance,
exclude irrelevant data, and/or perform other pre-processing
techniques. The remote server 102 may then determine a
corresponding equation, function, or model for calculating a
probable or expected measurement parameter value to be generated by
the target sensor 108 based on the correlative subset of legacy
sensor measurement parameters (e.g., isig, EIS, Vctr, and/or the
like) that are input variables to the model. Thus, the sensor data
translation model is capable of characterizing or mapping a
particular combination of template sensor measurement parameter
values to a probable measurement parameter value for the target
sensor 108.
[0054] In one embodiment, a shifting translation technique is
utilized to derive the sensor data translation model based on
population statistics as a function of electrical current output,
counter electrode voltage, electrochemical impedance, and time from
sensor connection. That is, the sensor data translation model may
map a particular combination of electrical current output,
electrode voltage and/or electrochemical impedance measurement
values obtained via a template sensor 106 to a probable measurement
value that would be produced by the target sensor 108 for one or
more of the electrical current output, electrode voltage and/or
electrochemical impedance. Using the shifting translation
technique, the translation model is generated by minimizing the
error between the template sensors 116, after the signal has been
transformed, and the paired template sensors 114. Error between the
transformed template sensor and target sensor may be calculated as
difference in means of signal distributions, mean of difference
between each paired sensor reading, or similar calculation. In this
regard, the translation model minimizes the error between the
distribution of the template sensor signal (e.g., the sequential
template sensor measurement values) shifted by the model equation
and the distribution of the target sensor signal (e.g., the
sequential target sensor measurement values). Possible model
equations include but are not limited to polynomial functions,
logistic functions, and sigmoidal functions, etc. In this regard,
those skilled in the art will appreciate that various different
potential shifting techniques and possible model equations exist,
and the details of any particular implementation are not germane to
this detailed description.
[0055] In another embodiment, a concatenative translation technique
is utilized to derive the translation model between template sensor
106 and target sensor 108 as a function of electrical current
output, counter electrode voltage, electrochemical impedance, time
from sensor connection, blood glucose reference measurements, and
sensor calibration data from paired template sensor trial data 116
and target sensor trial data 114. The concatenative translation
technique segments each signal from the template sensor trial data
116 and the target sensor trial data 114 into signal units
(signits) to generate a paired library. To translate template
sensor data 112, the template sensor data 112 is similarly
processed into signits. For each signit in the template sensor data
112, a nearest neighbor match is identified from the library by
matching to signits generated from the template sensor trial data
116. The match is identified by minimizing a cost function with
match and concatenation penalties, where the calibration data is
used to maintain specified signal-to-calibration data
relationships. Once the match is found, the corresponding paired
signit from the target sensor trial data 114 to the identified
matched signit from the template sensor data 112 is used for
concatenation. This matching process is repeated for all signits
across the template sensor data 112 to identify a series of signits
from the best match target sensor trial data 114 to concatenate
into the full translated signal. The concatenation algorithm can be
configurable to allow adjustments to the signit length, the degree
of adjacent signit overlap length, the range of time from sensor
connection for signits in the library, the featurization method,
data transformation methods, and match and concatenation cost
function definitions.
[0056] In yet another embodiment, a deep neural network translation
technique is utilized to derive the sensor data translation model
as a function of electrical current output, counter electrode
voltage, electrochemical impedance, and time from sensor connection
from paired template sensor trial data 116 and target sensor trial
data 114. The input data is processed to adjust data input size
(segment length), length of adjacent segment overlap (e.g. 50%), a
time series smoothing parameter (e.g. 3-to-1, centered or trailing
smoothing), window region to filter a specific time from sensor
connection for training (e.g. days 2-5), and data transformation
methods (e.g. standardization or normalization). The processed
paired data from the template sensor trial data 116 and the target
sensor trial data 114 is used to train a deep feed-forward neural
network with fully connected layers and regularization. The neural
network is configurable based on the number of hidden layers, the
width of hidden layers, the activation function per layer, the loss
function (e.g. mean squared error (MSE), root mean squared error
(RMSE), mean absolute percentage error (MAPE), or the like), least
absolute shrinkage and selection operator (LASSO) and Ridge
regularization parameters, learning rate, training batch size, and
number of training epochs. During translation, the processed query
template sensor data 112 is translated by the trained neural
network into the target sensor data 118.
[0057] Still referring to FIG. 3, after deriving the sensor data
translation model based on the relationship between paired
measurement data sets, the sensor translation process 300 retrieves
or otherwise obtains historical measurement data for the template
(or legacy) sensor and utilizes the sensor data translation model
to calculate or otherwise determine simulated measurement data for
the target (or new) sensor by translating measurement data from one
sensor design into another sensor design (tasks 310, 312). In this
regard, the sensor data translation model may be applied to a set
of historical measurement data that was previously obtained using
an instance of the template sensor 106 to convert measurement
values of that historical legacy sensor measurement data set into a
different set of measurement values that represent the probable
electrochemical behavior of the target sensor 108 to the same
stimulus or conditions that produced the respective set of
historical measurement data. For example, if the database 104
maintains historical legacy sensor clinical trial measurement data
112 for 4000 different patients, the remote server 102 may retrieve
the respective data set for each individual patient, apply the
sensor data translation model to the respective data set for each
individual patient to generate a simulated measurement data set for
that patient, and store the simulated measurement data set
associated with the target sensor 108 in the database 104,
resulting in 4000 patient sets of simulated measurement data 118
for the target sensor 108 without actually requiring those 4000
different patients to have worn or utilized the target sensor 108.
In this manner, historical measurement data 112 from previous
clinical trials may be mapped to a new model, make, type, and/or
configuration of sensing device 108 to effectively increase the
amount of available clinical trial data for the new model, make,
type, and/or configuration of sensing device 108 without requiring
patients engage in such a trial.
[0058] After generating simulated measurement data for the
particular type or configuration of sensing arrangement to be
calibrated, the sensor translation process 300 continues by
calculating or otherwise determining a glucose estimation model for
predicting the patient's glucose level as a function of the
measurement parameters output by the sensing arrangement using the
simulated measurement data (task 314). In exemplary embodiments,
the remote server 102 may create an augmented set of measurement
data for the target sensor 108 by combining the observed target
sensor clinical trial measurement data 114 with the simulated
measurement data 118 for the target sensor 108 to increase the size
of the data set. For example, the observed target sensor clinical
trial measurement data 114 may be obtained for a fewer number of
patients than the number of patient sets of simulated measurement
data 118, thereby reducing the time or costs associated with the
clinical trial for the target sensor 108, while the simulated
measurement data 118 provides a larger or more robust data set for
deriving the glucose estimation model to maintain the performance
of the efficacy of the glucose estimation model even though the
number of trial patients may be reduced. That said, in some
embodiments, the glucose estimation model could be derived solely
based on the simulated measurement data 118.
[0059] In exemplary embodiments, the glucose estimation model is
utilized to generate an estimated sensor glucose value as a
function of one or more measurement parameters (e.g., output
electrical current, counter electrode voltage, electrochemical
impedance, and the like). For example, a training data set for the
glucose estimation model may be created by the remote server 102
identifying and obtaining the reference blood glucose measurement
values associated with the calibration data points for the various
patient data sets of the historical template sensor measurement
data 112 that were translated into simulated measurement data 118
and utilizing the timestamps and patient identifiers associated
with those reference blood glucose measurement values to identify
simulated measurement parameter values temporally associated with
or otherwise corresponding to that calibration data point, with the
reference blood glucose measurement values functioning as the
output variable of the training data set and the simulated
measurement parameter values functioning as the input variable
combinations corresponding to the respective blood glucose
measurement values. Similarly, the remote server 102 may identify
and obtain the reference blood glucose measurement values
associated with the calibration data points for the patient data
sets of the observed target sensor measurement data 114 and utilize
the timestamps and patient identifiers to identify the temporally
associated measurement parameter values corresponding to that
calibration data point for use as additional combinations of output
variable value and input variable combinations, respectively, for
the training data set.
[0060] In exemplary embodiments, the remote server 102 utilizes
machine learning techniques, such as genetic programming (GP),
artificial neural network (NN), regression decision tree (DT),
and/or the like to derive a glucose estimation model for predicting
or estimating the patient's interstitial glucose measurement value
to which the output measurement parameters of the target sensor 108
most likely corresponds as a function of the measurement parameters
based on the relationships between the reference blood glucose
measurement values and the respective combinations of measurement
parameter values. In some embodiments, the estimated sensor glucose
value derived using the glucose estimation model is fused or
otherwise combined with one or more other values (e.g., a current
sensor glucose measurement determined using a calibration factor)
to arrive at a final sensor glucose measurement value that is
output or otherwise provided to other devices or components for
other uses (e.g., generating notifications, adjusting insulin
delivery, etc.). In this regard, the estimated sensor glucose value
may augment or otherwise adjust the normal sensor glucose
measurement value determined using a calibration factor in a manner
that accounts for variability in the accuracy or reliability of the
calibration factor with respect to time. Examples of using machine
learning to derive sensor glucose models and determining estimated
sensor glucose values are described in greater detail in U.S.
Patent Application Pub. No. 2019/0076066.
[0061] In exemplary embodiments, after determining the glucose
estimation model for the target sensor 108, the remote server 102
may store or otherwise maintain the data defining the glucose
estimation model in the database 104 (e.g., modeling data 120) in
association with the target sensor 108. In some embodiments, the
remote server 102 may automatically push or otherwise provide the
glucose estimation model to various instances of the target sensor
108 on the network 110 or to other electronic devices that are used
in connection with the target sensor 108 (e.g., fluid infusion
devices, mobile devices, and/or the like). In other embodiments,
upon deployment, a new instance of the target sensor 108 may
automatically download the glucose estimation model from the remote
server 102 via the network 110. The glucose estimation model is
then utilized in connection with the deployed instances of the
target sensor 108 to determine estimated sensor glucose measurement
values that may influence insulin delivery, patient alerts or
notifications, and/or the like.
[0062] FIG. 4 depicts a graph 400 illustrating an exemplary
relationship between an observed measurement parameter 402 (e.g.,
isig, EIS, Vctr, or the like) with respect to time obtained using
an instance of a sensor to be modeled (e.g., target sensor 108) and
the same observed measurement parameter 404 with respect to time
that is concurrently obtained using an instance of a different
sensor for which translation between the two sensors is desired
(e.g., template sensor 106). For example, with reference to FIG. 1,
graph line 402 may depict the observed electrical current (isig)
output by an instance of the target sensor 108 during a clinical
trial for a patient that was concurrently using an instance of the
template sensor 106, with the graph line 404 depicting the observed
electrical current (isig) output by the instance of the template
sensor 106 during the clinical trial. In this regard, the graph
lines 402, 404 correspond to concurrent measurement data obtained
for the same patient using the different instances of sensors 106,
108 (e.g., a first instance of a template sensing arrangement 106
and a second instance of a target sensing arrangement 108). In one
embodiment, the target sensor measurement data 402 for the patient
may correspond to an entry in the target sensor clinical trial
measurement data set (e.g., data 114) maintained by the database
104 while the template sensor measurement data 404 for the patient
corresponds to an entry in the template sensor clinical trial
measurement data set (e.g., data 116) maintained by the database
104, with an identifier associated with the patient being utilized
to associated or otherwise correlate the entries with one another.
As described above, the relationships between the target sensor
clinical trial measurement data 114 and the template sensor
clinical trial measurement data 116 is analyzed by the remote
server 102 using machine learning to obtain a sensor translation
model for translating measurement data from the legacy sensor
domain to the new sensor domain. In the graph 400 depicted in FIG.
4, graph line 406 represents the predicted electrical current
(isig) output for the instance of the target sensor 108 that is
calculated by applying the sensor translation model to the template
sensor measurement data 404. Thus, the predicted target sensor
measurement data 406 represents the expected electrochemical
response and corresponding electrical current (isig) output that
would likely be generated by the instance of the target sensor 108
in response to the same interstitial fluid glucose levels that
resulted in the output 404 generated by the template sensor 106. As
described above, the simulated measurement data set represented by
line 406 may be included in the training data set used to derive
the sensor glucose estimation model for the target sensor 108.
[0063] FIG. 5 depicts an exemplary embodiment of a real-time
translation process 500 for using a sensor data translation model
to leverage a calibration algorithm associated with one design,
type or configuration of sensor with measurements output by a
different design, type or configuration of sensor. For example, an
existing (or legacy) calibration algorithm previously developed for
a template (or legacy) sensor may be utilized to determine
estimated calibrated measurement values in real-time using the
existing calibration algorithm in connection with an instance of a
target (or new) sensor, which may be alternatively referred to
herein as a backwards translation process (e.g., by translating
measurement output from a new sensor effectively "backwards" in
time to an older sensor domain). In this regard, the backwards
translation process may be used to leverage a legacy calibration
algorithm for a new sensor without retraining the legacy algorithm
or requiring development of a calibration algorithm for the new
sensor. Conversely, the real-time translation process 500 could
also be performed in an equivalent manner to leverage a new or more
recently developed calibration algorithm for a new sensor with a
legacy sensor to determine estimated calibrated measurement values
in real-time using the new calibration algorithm in connection with
an instance of the legacy sensor, which may alternatively be
referred to herein as a forwards translation process (e.g., by
translating measurement output from an older legacy sensor
effectively "forwards" in time to a newer sensor domain).
[0064] The various tasks performed in connection with the real-time
translation process 500 may be performed by hardware, firmware,
software executed by processing circuitry, or any combination
thereof. For illustrative purposes, the following description may
refer to elements mentioned above in connection with FIGS. 1-2. It
should be appreciated that the real-time translation process 500
may include any number of additional or alternative tasks, the
tasks need not be performed in the illustrated order and/or the
tasks may be performed concurrently, and/or the real-time
translation process 500 may be incorporated into a more
comprehensive procedure or process having additional functionality
not described in detail herein. Moreover, one or more of the tasks
shown and described in the context of FIG. 5 could be omitted from
a practical embodiment of the real-time translation process 500 as
long as the intended overall functionality remains intact.
[0065] The real-time translation process 500 obtains concurrent
measurement data for different sensors, maintains associations
between the measurement data, and determines a predictive
translation model for generating simulated measurement data in one
sensor domain as a function of measurement data from the other
sensor domain based at least in part on the relationships between
the paired data sets (tasks 502, 504, 506, 508) in a similar manner
as described above in the context of the translation modeling
process 300 of FIG. 3 (e.g., tasks 302, 304, 306, 308), and such
common aspects will not be described herein in the context of FIG.
5. As described above, depending on the embodiment, the translation
model may be determined for translating from the template sensor
domain to the target sensor domain or vice versa. For purposes of
explanation, the sensor domain that the real-time translation
process 500 is utilized to translate measurement values into in
real-time may alternatively be referred to herein as the
destination sensor domain, while the other sensor domain may
alternatively be referred to as the initiating sensor domain. In
this regard, the sensor translation model developed for the
real-time translation process 500 is provided to instances of
sensors associated with the initiating sensor domain for real-time
translation of measurement values into the destination sensor
domain.
[0066] As described in greater detail below, the real-time
translation process 500 receives or otherwise obtains the existing
predictive calibration model associated with the destination sensor
domain, receives or otherwise obtains real-time measurements in the
initiating sensor domain, and applies the translation model to
translate the real-time measurements from the initiating sensor
domain into corresponding simulated measurement values in the
destination sensor domain (tasks 510, 512, 514). Thereafter, the
real-time translation process 500 applies the existing predictive
calibration model associated with the destination sensor domain to
the simulated real-time measurements to obtain a calibrated
measurement output for an instance of the initiating sensor design
using the existing calibration model associated with the
destination sensor domain (task 516).
[0067] For example, for so-called backwards translation where
measurement values from a new sensor design or configuration
corresponding to target sensor 108 are translated into a domain
associated with an older or legacy sensor design or configuration
corresponding to template sensor 106, the real-time translation
process 500 is performed to develop a model for translating from
the target sensor domain to the legacy template sensor domain
(e.g., tasks 502, 504, 506, 508) in a similar manner as described
above. The translation model may then be pushed or otherwise
provided to instances of the target sensor 108 for use with
measurement values in real-time. In this regard, with reference to
FIGS. 1-2, the translation model is implemented on the target
sensing device 108, 200 (e.g., by controller 204) for real-time
translation of measurements captured via the sensing element 202 of
a new sensor design or configuration into simulated measurements in
a legacy sensor domain (e.g., tasks 512, 514), thereby translating
the measurement signals, such as electrical current output,
voltage, and impedance obtained via the sensing element 202 of the
new sensor into a domain that more closely mimics the sensor
signals the legacy calibration algorithm was trained on. The
simulated measurement values in the legacy sensor domain are then
input or otherwise provided to the calibration model associated
with the template sensor 106 to obtain a predicted or estimated
calibrated measurement value for the target sensing device 108, 200
using the legacy calibration algorithm. In this regard, the target
sensing device controller 204 may receive or otherwise obtain the
legacy calibration model (e.g., from the server 102) and apply the
legacy calibration model to sampled measurement values from sensing
element 202 after translating those measurement samples into the
legacy sensor domain to calculate or otherwise determine a
calibrated glucose measurement value according to the legacy
calibration algorithm that may be output by the target sensor 108,
200 (e.g., via output interface 208). Thus, a target sensor 108
with a new design or configuration could be deployed without a new
calibration algorithm being trained or developed for the target
sensor 108, but rather, estimated or predicted calibrated
measurement outputs may be obtained in accordance with an existing
calibration algorithm, thereby allowing well performing algorithms
established for other sensors to be deployed in connection with
other sensors.
[0068] In one or more embodiments, the glucose estimation model
associated with a legacy or template sensor is derived or otherwise
trained using measurement data obtained from instances of the
legacy sensor and then stored or otherwise maintained by the remote
server 102 in the database 104 for subsequent deployment to other
sensor devices. Similarly, the remote server 102 may derive or
otherwise determine translation models for translating between the
new or target sensor domain and the legacy sensor domain and
maintain those models in the database 104 as described above.
Accordingly, in such embodiments, the controller 204, 722 (e.g., as
discussed in further detail below with reference to FIG. 7)
associated with an instance of a target sensor 108, 200, 702 may
download or otherwise obtain, from the remote server 102 via a
network 110, the glucose estimation model associated with the
legacy sensor 106 along with the sensor translation model for
translating from the new sensor domain to the legacy sensor domain.
In this regard, the controller 204, 722 may then merely apply the
downloaded sensor translation model to the sampled measurement
outputs of the sensing element 202 to obtain simulated measurement
data in the legacy sensor domain, and then apply the downloaded
glucose estimation model to the simulated measurement data in the
legacy sensor domain to obtain estimated glucose values based on
the sampled measurement outputs of the sensing element 202. The
estimated glucose values may then be output or otherwise provided
by the controller 204, 722 via the output interface 208 for
presentation to a user (e.g., via a display device or other
graphical user interface) or analysis, collection, and/or other
action by another device (e.g., an infusion device, a remote
server, and/or the like).
[0069] Similarly, for so-called forwards translation where
measurement values from a legacy sensor design or configuration
corresponding to template sensor 106 are translated into a domain
associated with a newer sensor design or configuration
corresponding to target sensor 108, the real-time translation
process 500 is performed to develop a model for translating from
the template sensor domain to the target sensor domain (e.g., tasks
502, 504, 506, 508) in an equivalent manner as described above in
the context of FIG. 3. The translation model may then be pushed or
otherwise provided to instances of the template sensor 106 for use
with measurement values in real-time. In this regard, with
reference to FIGS. 1-2, the translation model may be implemented on
the template sensing device 106, 200 (e.g., by controller 204) for
real-time translation of measurements captured via the sensing
element 202 of a legacy sensor design or configuration into
simulated measurements in a new sensor domain (e.g., tasks 512,
514). The simulated measurement values in the new sensor domain are
then input or otherwise provided to the calibration model
associated with the target sensor 108 to obtain a predicted or
estimated calibrated measurement value for the template sensing
device 106, 200 using the newer calibration algorithm. In this
regard, rather than retraining or updating a legacy sensor
calibration algorithm, a newer or better performing calibration
algorithm trained or developed for the target sensor 108 may be
propagated back to legacy template sensors 106, thereby allowing
well performing algorithms established for newer sensors to be
deployed in connection with older sensors.
[0070] FIG. 6 depicts an exemplary embodiment of a generative
modeling process 600 for developing a model for estimating a
calibrated output of a sensing arrangement using simulated
measurement data. The various tasks performed in connection with
the generative modeling process 600 may be performed by hardware,
firmware, software executed by processing circuitry, or any
combination thereof. For illustrative purposes, the following
description may refer to elements mentioned above in connection
with FIGS. 1-2. It should be appreciated that the generative
modeling process 600 may include any number of additional or
alternative tasks, the tasks need not be performed in the
illustrated order and/or the tasks may be performed concurrently,
and/or the generative modeling process 600 may be incorporated into
a more comprehensive procedure or process having additional
functionality not described in detail herein. Moreover, one or more
of the tasks shown and described in the context of FIG. 6 could be
omitted from a practical embodiment of the generative modeling
process 600 as long as the intended overall functionality remains
intact.
[0071] The illustrated generative modeling process 600 initializes
by receiving or otherwise obtaining measurement data for the
particular type or configuration of sensing arrangement to be
modeled (task 602). For example, as described above, instances of a
target sensor 108 may be provided to a number of different
individuals or patients as part of a clinical trial, with the
measurement outputs from the respective sensors 108 being uploaded
or otherwise transmitted to a remote server 102 (e.g., via network
110) for storage in a database 104 in association with the
respective individual or patient along with calibration data
associated with the respective patient (e.g., timestamped reference
blood glucose measurements and corresponding calibration factors)
or other data associated with the respective patient (e.g.,
demographic data and/or the like).
[0072] In one embodiment, the generative modeling process 600
continues by associating measurement data with calibration data
points (task 604). In this regard, measurement data 114 for the
sensor 108 to be modeled is associated with concurrent or
contemporaneous reference blood glucose measurement and related
calibration data for a respective patient. For example, for each
patient having a corresponding data set in the new sensor trial
measurement data 114, the remote server 102 may identify different
calibration data points associated with the patient, and then based
on the associated timestamps (or sensor age or time after
insertion), identify corresponding measurement outputs from the
patient's sensor 108 that were obtained at the same time as or
within a threshold amount of time of the respective calibration
timestamp. In other words, the output measurements that are not
temporally relevant to the calibration data points may be filtered
or otherwise excluded from further analysis. Again, it should be
noted that the measurement outputs from the patient's sensor 108
need not be synchronous with the respective calibration timestamp
but may rather be within a threshold time of the calibration
timestamp. Moreover, one or more values within the threshold time
of the calibration timestamp may be averaged, interpolated, or
otherwise combined to arrive at a representative value to be
associated with the calibration timestamp.
[0073] After associating the measurement data with calibration data
points, the generative modeling process 600 continues by developing
or otherwise identifying a generative model for predicting the
output measurements likely to be generated by the sensing
arrangement as a function of the calibration factor and age (task
606). In exemplary embodiments, the generative model is implemented
using a generative adversarial network made up of two neural
networks. A first neural network is trained to derive a model,
alternatively referred to herein as the generator model, using the
respective pairs of calibration factors and timestamps (or sensor
age) as conditional inputs to the generator model and the
corresponding sets of sensor output measurements (e.g., electrical
current, counter electrode voltage, electrochemical impedance, and
the like) as the corresponding outputs to be produced by the
generator model. In this regard, the generator model is capable of
generating time-varying or time-dependent synthetic values for the
sensor output measurements. A second neural network is trained
using the same training data set to derive a model, alternatively
referred to herein as the discriminator model, that votes or
otherwise provides indication of whether a combination of input
variables represents an actual or plausible combination of
variables. Thus, for a given combination of sensor output
measurement values (e.g., isig, Vctr and EIS values) and
calibration data values (e.g., calibration factor and sensor age or
time after insertion), the discriminator model provides an
indication of whether that combination of input variables is
plausible, such as, for example, a probability that the input
variable combination is realistic. In exemplary embodiments, the
generator and discriminator models are trained and function in
concert with one another, such that the generator model generates
values for the sensor output measurements for a given calibration
factor and sensor age that maximizes or otherwise optimizes the
output of the discriminator model.
[0074] After deriving a generative model, the generative modeling
process 600 continues by retrieving or otherwise obtaining
historical calibration data points and providing the historical
calibration data points as input variables to the generative model
to calculate or otherwise determine synthetic measurement data for
the new sensor as a function of the historical calibration data
points (tasks 608, 610). In this regard, the generative model may
be applied to a set of historical calibration data that was
previously obtained in connection with using an instance of the
legacy sensor 106 to generate measurement outputs by the target
sensor 108 that represent the probable electrochemical behavior of
the target sensor 108 to the same stimulus and/or aging conditions
that resulted in the input calibration factor at a particular
sensor age. For example, if the database 104 maintains historical
legacy sensor clinical trial measurement data 112 for 4000
different patients, the remote server 102 may retrieve the
respective calibration data points (e.g., reference blood glucose
measurement, calibration factor, and timestamp) for each individual
patient, apply the generative model to each of the calibration data
points for each individual patient to generate a synthetic
measurement data set for that patient, and store the synthetic
measurement data set associated with the target sensor 108 in the
database 104, resulting in 4000 patient sets of simulated
measurement data 118. In this manner, historical measurement data
112 from previous clinical trials may be utilized to effectively
increase the amount of available clinical trial data for the new
model, make, type, and/or configuration of sensing device 108
without requiring patients engage in such a trial. Synthetic data
can also be created by the generative model by applying the
generative model to simulated calibration data points.
[0075] In a similar manner as described above, after generating
simulated measurement data for the particular type or configuration
of sensing arrangement to be calibrated, the generative modeling
process 600 continues by calculating or otherwise determining a
glucose estimation model for predicting the patient's glucose level
as a function of the measurement parameters output by the sensing
arrangement using the simulated measurement data (task 612). For
example, the remote server 102 may create an augmented set of
measurement data for the target sensor 108 by combining the subset
of observed target sensor clinical trial measurement data 114
associated with the calibration data points from the current
clinical trial with the simulated measurement data 118 for the
target sensor 108 associated with historical calibration data
points to increase the size of the training data set for the
glucose estimation model. In this regard, in a similar manner as
described above, the glucose estimation model is trained using a
training data set that includes the combinations of synthetic
measurement outputs as input variables (e.g., generated isig, Vctr,
and EIS values from the simulated measurement data 118) and the
corresponding reference blood glucose measurement values from the
historical data set (e.g., from historical legacy sensor data 112)
as the output variable of the training data set.
[0076] In a similar manner as described above, the remote server
102 may analyze the observed new sensor trial measurement data 114
to derive the generative model and then store or otherwise maintain
the data defining the generative model in the database 104 (e.g.,
modeling data 120) in association with the target sensor 108.
Thereafter, the remote server 102 may apply the generative model to
the calibration data points from the historical template sensor
measurement data 112 from one or more previous trials to generate
the simulated measurement data 118 corresponding to the historical
calibration data points. The remote server 102 may then analyze the
simulated measurement data 118, individually or in combination with
the observed new sensor trial measurement data 114, to derive a
glucose estimation model for the target sensor 108 using the
synthetic measurement data. As described above, after determining
the glucose estimation model for the target sensor 108, the remote
server 102 may store or otherwise maintain the data defining the
glucose estimation model in the database 104 (e.g., modeling data
120) in association with the target sensor 108 for distribution to
other instances of the target sensor 108 or other electronic
devices utilized with instances of the target sensor 108 (e.g.,
fluid infusion devices, client electronic devices, and/or the
like).
[0077] In one embodiment, a generative modeling process involves
obtaining measurement data from one sensor design, partitioning the
measurement data into separate training, testing, and generative
folds (an optional fold can be created for use in training the
calibrated measurement algorithm), developing a generative model
for sensor output based on features of sensor signals
pre-conditioned on calibration data points from the training folds,
and evaluating performance of the generative model by applying the
generative model to extracted calibration data from the testing
fold to evaluate performance. Then simulated data can be generated
by applying the generative model to extracted calibration data from
the generative folds or simulated and used to determine a
predictive model for calibrated measurement output based on
training with the simulated sensor measurements (synthetic data)
generated sensor measurements with or without additional sensor
measurements from the current sensor design.
[0078] FIG. 7 depicts an exemplary embodiment of a patient
monitoring system 700 that includes a medical device 702 capable of
using a glucose estimation model derived from simulated measurement
data. In the illustrated embodiment, the medical device 702 is
communicatively coupled to a sensing element 704 that is inserted
into the body of a patient or otherwise worn by the patient to
obtain measurement data indicative of a physiological condition in
the body of the patient, such as a sensed glucose level. In the
illustrated embodiment, the medical device 702 is communicatively
coupled to a client device 706 via a communications network 710,
with the client device 706 being communicatively coupled to a
remote device 714 (e.g., remote server 102) via another
communications network 712 (e.g., network 110). In this regard, the
client device 706 may function as an intermediary for uploading or
otherwise providing measurement data from the medical device 702 to
the remote device 714. It should be appreciated that FIG. 7 depicts
a simplified representation of a patient monitoring system 700 for
purposes of explanation and is not intended to limit the subject
matter described herein in any way. For example, some embodiments
may support direct communications between the medical device 702
and the remote device 714 via communications network 712.
[0079] In exemplary embodiments, the client device 706 is realized
as a mobile phone, a smartphone, a tablet computer, or other
similar mobile electronic device; however, in other embodiments,
the client device 706 may be realized as any sort of electronic
device capable of communicating with the medical device 702 via
network 710, such as a laptop or notebook computer, a desktop
computer, or the like. In exemplary embodiments, the network 710 is
realized as a Bluetooth network, a ZigBee network, or another
suitable personal area network. That said, in other embodiments,
the network 710 could be realized as a wireless ad hoc network, a
wireless local area network (WLAN), or local area network (LAN).
The client device 706 includes or is coupled to a display device,
such as a monitor, screen, or another conventional electronic
display, capable of graphically presenting data and/or information
pertaining to the physiological condition of the patient. The
client device 706 also includes or is otherwise associated with a
user input device, such as a keyboard, a mouse, a touchscreen, or
the like, capable of receiving input data and/or other information
from the user of the client device 706.
[0080] In some embodiments, a user, such as the patient, the
patient's doctor or another healthcare provider, or the like,
manipulates the client device 706 to execute a client application
708 that supports communicating with the medical device 702 via the
network 710. In this regard, the client application 708 supports
establishing a communications session with the medical device 702
on the network 710 and receiving data and/or information from the
medical device 702 via the communications session. The medical
device 702 may similarly execute or otherwise implement a
corresponding application or process that supports establishing the
communications session with the client application 708. The client
application 708 generally represents a software module or another
feature that is generated or otherwise implemented by the client
device 706 to support the processes described herein. Accordingly,
the client device 706 generally includes a processing system and a
data storage element (or memory) capable of storing programming
instructions for execution by the processing system, that, when
read and executed, cause processing system to create, generate, or
otherwise facilitate the client application 708 and perform or
otherwise support the processes, tasks, operations, and/or
functions described herein. Depending on the embodiment, the
processing system may be implemented using any suitable processing
system and/or device, such as, for example, one or more processors,
central processing units (CPUs), graphics processing units (GPUs),
controllers, microprocessors, microcontrollers, processing cores
and/or other hardware computing resources configured to support the
operation of the processing system described herein. Similarly, the
data storage element or memory may be realized as a random-access
memory (RAM), read only memory (ROM), flash memory, magnetic or
optical mass storage, or any other suitable non-transitory short or
long-term data storage or other computer-readable media, and/or any
suitable combination thereof.
[0081] In one or more embodiments, the client device 706 and the
medical device 702 establish an association (or pairing) with one
another over the network 710 to support subsequently establishing a
point-to-point communications session between the medical device
702 and the client device 706 via the network 710. For example, in
accordance with one embodiment, the network 710 is realized as a
Bluetooth network, wherein the medical device 702 and the client
device 706 are paired with one another (e.g., by obtaining and
storing network identification information for one another) by
performing a discovery procedure or another suitable pairing
procedure. The pairing information obtained during the discovery
procedure allows either of the medical device 702 or the client
device 706 to initiate the establishment of a secure communications
session via the network 710.
[0082] In one or more exemplary embodiments, the client application
708 is also configured to store or otherwise maintain a network
address and/or other identification information for the remote
device 714 on the second network 712. In this regard, the second
network 712 may be physically and/or logically distinct from the
network 710, such as, for example, the Internet, a cellular
network, a wide area network (WAN), or the like. The remote device
714 generally represents a server or other computing device
configured to receive and analyze or otherwise monitor measurement
data, event log data, and potentially other information obtained
for the patient associated with the medical device 702. In
exemplary embodiments, the remote device 714 is coupled to a
database 716 (e.g., database 104) configured to store or otherwise
maintain data associated with individual patients. In practice, the
remote device 714 may reside at a location that is physically
distinct and/or separate from the medical device 702 and the client
device 706, such as, for example, at a facility that is owned
and/or operated by or otherwise affiliated with a manufacturer of
the medical device 702. For purposes of explanation, but without
limitation, the remote device 714 may alternatively be referred to
herein as a server.
[0083] It should be noted that in some embodiments, some or all of
the functionality and processing intelligence of the remote
computing device 714 can reside at the medical device 702 and/or at
other components or computing devices that are compatible with the
patient monitoring system 700. In other words, the patient
monitoring system 700 need not rely on a network-based or a
cloud-based server arrangement as depicted in FIG. 7, although such
a deployment might be the most efficient and economical
implementation. These and other alternative arrangements are
contemplated by this disclosure. To this end, some embodiments of
the system 700 may include additional devices and components that
serve as data sources, data processing units, and/or recommendation
delivery mechanisms. For example, the system 700 may include any or
all of the following elements, without limitation: computer devices
or systems; patient monitors; healthcare provider systems; data
communication devices; and the like.
[0084] Still referring to FIG. 7, the sensing element 704 generally
represents the component of the patient monitoring system 700 that
is configured to generate, produce, or otherwise output one or more
electrical signals indicative of a physiological condition that is
sensed, measured, or otherwise quantified by the sensing element
704 (e.g., sensing element 202). In this regard, the physiological
condition of a patient influences a characteristic of the
electrical signal output by the sensing element 704, such that the
characteristic of the output signal corresponds to or is otherwise
correlative to the physiological condition that the sensing element
704 is sensitive to. In exemplary embodiments, the sensing element
704 is realized as an interstitial glucose sensing element inserted
at a location on the body of the patient that generates an output
electrical signal having a current (or voltage) associated
therewith that is correlative to or otherwise influenced by the
interstitial fluid glucose level that is sensed or otherwise
measured in the body of the patient by the sensing element 704. In
some embodiments, the sensing element 704 is implemented or
otherwise realized as an instance of the target sensing device 108,
200 (or new sensor) having an associated glucose estimation model
derived using simulated measurement data.
[0085] The medical device 702 generally represents the component of
the patient monitoring system 700 that is communicatively coupled
to the output of the sensing element 704 to receive or otherwise
obtain the measurement data samples from the sensing element 704
(e.g., the measured glucose and characteristic impedance values),
store or otherwise maintain the measurement data samples, and
upload or otherwise transmit the measurement data to the server 714
via the client device 706. In one or more embodiments, the medical
device 702 is realized as an infusion device configured to deliver
a fluid, such as insulin, to the body of the patient. In such
embodiments, the infusion device 702 may employ closed-loop control
or other delivery control schemes that vary insulin delivery in a
manner that is influenced by the patient's current glucose level
received via the sensing element 704 or other sensing device (e.g.,
a continuous glucose monitor (CGM) device). That said, in other
embodiments, the medical device 702 could be a standalone sensing
or monitoring device separate and independent from an infusion
device (e.g., sensing device 108, 200), such as, for example, a
continuous glucose monitor (CGM) (or CGM device), an interstitial
glucose sensing arrangement, or similar device. It should be noted
that although FIG. 7 depicts the medical device 702 and the sensing
element 704 as separate components, in practice, the medical device
702 and the sensing element 704 may be integrated or otherwise
combined to provide a unitary device that can be worn by the
patient.
[0086] In exemplary embodiments, the medical device 702 includes a
controller 722, a data storage element 724 (or memory), a
communications interface 726, and a user interface 728. The user
interface 728 generally represents the input user interface
element(s) and/or output user interface element(s) associated with
the medical device 702. The controller 722 generally represents the
processing system or other hardware, circuitry, logic, firmware
and/or other component(s) of the medical device 702 that is coupled
to the sensing element 704 to receive the electrical signals output
by the sensing element 704 and perform or otherwise support various
additional tasks, operations, functions and/or processes described
herein. Depending on the embodiment, the controller 722 may be
implemented or realized with a general purpose processor, a
microprocessor, a controller, a microcontroller, a state machine, a
content addressable memory, an application specific integrated
circuit, a field programmable gate array, any suitable programmable
logic device, discrete gate or transistor logic, discrete hardware
components, or any combination thereof, designed to perform the
functions described herein. In some embodiments, the controller 722
includes an analog-to-digital converter (ADC) or another similar
sampling arrangement that samples or otherwise converts an output
electrical signal received from the sensing element 704 into
corresponding digital measurement data value. In other embodiments,
the sensing element 704 may incorporate an ADC and output a digital
measurement value.
[0087] The communications interface 726 generally represents the
hardware, circuitry, logic, firmware and/or other components of the
medical device 702 that are coupled to the controller 722 for
outputting data and/or information from/to the medical device 702
to/from the client device 706. For example, the communications
interface 726 may include or otherwise be coupled to one or more
transceiver modules capable of supporting wireless communications
between the medical device 702 and the client device 706. In
exemplary embodiments, the communications interface 726 is realized
as a Bluetooth transceiver or adapter configured to support
Bluetooth Low Energy (BLE) communications.
[0088] In exemplary embodiments, the remote device 714 receives,
from the client device 706, measurement data values associated with
a particular patient (e.g., sensor glucose measurements,
acceleration measurements, and the like) that were obtained using
the sensing element 704, and the remote device 714 stores or
otherwise maintains the historical measurement data in the database
716 in association with the patient (e.g., using one or more unique
patient identifiers). Additionally, the remote device 714 may also
receive, from or via the client device 706, meal data or other
event log data that may be input or otherwise provided by the
patient (e.g., via client application 708) and store or otherwise
maintain historical meal data and other historical event or
activity data associated with the patient in the database 716. In
this regard, the meal data include, for example, a time or
timestamp associated with a particular meal event, a meal type or
other information indicative of the content or nutritional
characteristics of the meal, and an indication of the size
associated with the meal. In exemplary embodiments, the remote
device 714 also receives historical fluid delivery data
corresponding to basal or bolus dosages of fluid delivered to the
patient by an infusion device. For example, the client application
708 may communicate with an infusion device to obtain insulin
delivery dosage amounts and corresponding timestamps from the
infusion device, and then upload the insulin delivery data to the
remote device 714 for storage in association with the particular
patient. The remote device 714 may also receive geolocation data
and potentially other contextual data associated with a device 702,
706 from the client device 706 and/or client application 708, and
store or otherwise maintain the historical operational context data
in association with the particular patient. In this regard, one or
more of the devices 702, 706 may include a global positioning
system (GPS) receiver or similar modules, components or circuitry
capable of outputting or otherwise providing data characterizing
the geographic location of the respective device 702, 706 in
real-time.
[0089] In various embodiments, the remote device 714 may implement,
facilitate, support or otherwise perform the sensor translation
process 300 of FIG. 3 or the generative modeling process 600 of
FIG. 6 to obtain a glucose estimation model associated with the
sensing element 704. Thereafter, the remote device 714 may push or
otherwise provide the glucose estimation model to the medical
device 702 and/or the client device 706 for determining an
estimated sensor glucose value in real-time based on measurement
data samples obtained via sensing arrangement 704. For example,
when the medical device 702 is realized as an infusion device, the
glucose estimation model may be utilized to adjust or otherwise
augment the current sensor glucose measurement value determined
using a calibration factor with a current estimated sensor glucose
value to arrive at an adjusted sensor glucose measurement value
that accounts for aging of the sensing element 704 since the most
recent calibration. Based on a difference between the adjusted
sensor glucose measurement value and a target glucose value for a
patient, the infusion device 702 may determine a corresponding
amount of insulin to be delivered to reduce the different and
autonomously operate a motor or other actuation arrangement of the
infusion device 702 in accordance with the delivery command to
displace a plunger or otherwise dispense insulin from a reservoir
of the infusion device 702. That said, in other embodiments, the
medical device 702 and/or the client application 708 may utilize
the current estimated sensor glucose value and/or the adjusted
sensor glucose measurement value to provide alerts or other
notifications to a user (e.g., hyperglycemia and/or hypoglycemia
alerts, alerts to replace or change the sensing element 704, alerts
to change the insertion site for the sensing element 704, etc.). In
this regard, the subject matter described herein is not limited to
any particular application or use of the glucose estimation model
or estimated sensor glucose values determined therefrom.
Additionally, as described, in various embodiments, the remote
device 714 may facilitate or otherwise support development of
different sensor translation models, generative models, and the
like in connection with one or more of the processes 300, 500, 600
described above using measurement data maintained in the database
716, maintain the modeling data (e.g., in the database 716), and
push or otherwise provide modeling data to different instances of
the medical device 702 (e.g., sensors 106, 108) as appropriate for
real-time deployment or application at a medical device 702.
[0090] In one or more embodiments, the remote device 714 may
facilitate or otherwise support the real-time translation process
500 of FIG. 5 by developing and providing a translation model to
the medical device 702 and/or the client device 706 for translating
measurement data samples obtained via the sensing arrangement 704
to a legacy sensor domain in real-time. For example, the medical
device 702 may utilizes the translation model to translate
measurements captured via the sensing element 202, 704 of a new
sensor design or configuration into simulated measurements in a
legacy sensor domain, and then apply a legacy glucose estimation
model to the simulated measurements to obtain an estimated
calibrated measurement value that may be utilized by the medical
device 702 (e.g., to determine insulin delivery commands, generate
graphical user interface displays, and/or the like) or provided to
another device 706. Thus, a sensing element 704 with a new design
or configuration could be deployed in the system 700 without a new
calibration algorithm (or glucose estimation model) being trained
or developed for the sensing element 704, but rather, estimated or
predicted calibrated measurement outputs may be obtained in
accordance with an existing calibration algorithm, thereby allowing
well performing algorithms established for other sensors to be
deployed in connection with sensing element 704.
[0091] For the sake of brevity, conventional techniques related to
glucose sensing and/or monitoring, sampling, filtering,
calibration, closed-loop glucose control, machine learning,
artificial intelligence, and other functional aspects of the
subject matter may not be described in detail herein. In addition,
certain terminology may also be used in the herein for the purpose
of reference only, and thus is not intended to be limiting. For
example, terms such as "first", "second", and other such numerical
terms referring to structures do not imply a sequence or order
unless clearly indicated by the context. The foregoing description
may also refer to elements or nodes or features being "connected"
or "coupled" together. As used herein, unless expressly stated
otherwise, "coupled" means that one element/node/feature is
directly or indirectly joined to (or directly or indirectly
communicates with) another element/node/feature, and not
necessarily mechanically.
[0092] While at least one exemplary embodiment has been presented
in the foregoing detailed description, it should be appreciated
that a vast number of variations exist. It should also be
appreciated that the exemplary embodiment or embodiments described
herein are not intended to limit the scope, applicability, or
configuration of the claimed subject matter in any way. For
example, the subject matter described herein is not necessarily
limited to the infusion devices and related systems described
herein. Moreover, the foregoing detailed description will provide
those skilled in the art with a convenient road map for
implementing the described embodiment or embodiments. It should be
understood that various changes can be made in the function and
arrangement of elements without departing from the scope defined by
the claims, which includes known equivalents and foreseeable
equivalents at the time of filing this patent application.
Accordingly, details of the exemplary embodiments or other
limitations described above should not be read into the claims
absent a clear intention to the contrary.
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