U.S. patent application number 17/121624 was filed with the patent office on 2022-06-16 for machine learning models for detecting outliers and erroneous sensor use conditions and correcting, blanking, or terminating glucose sensors.
The applicant listed for this patent is MEDTRONIC MINIMED, INC.. Invention is credited to PETER AJEMBA, ELAINE GEE, JEFFREY NISHIDA, KEITH NOGUEIRA, ANDREA VARSAVSKY.
Application Number | 20220189630 17/121624 |
Document ID | / |
Family ID | 1000005362056 |
Filed Date | 2022-06-16 |
United States Patent
Application |
20220189630 |
Kind Code |
A1 |
GEE; ELAINE ; et
al. |
June 16, 2022 |
MACHINE LEARNING MODELS FOR DETECTING OUTLIERS AND ERRONEOUS SENSOR
USE CONDITIONS AND CORRECTING, BLANKING, OR TERMINATING GLUCOSE
SENSORS
Abstract
Methods, systems, and devices for improving continuous glucose
monitoring ("CGM") are described herein. More particularly, the
methods, systems, and devices describe retrieving a machine
learning model that is trained to classify CGM sensor data and
blanking the CGM sensor data based on an outlier classification
from the machine learning model. The system may terminate sensors
for which there is an aggregation of blanked CGM sensor data. The
methods, systems, and devices described herein may additionally
comprise a machine learning model that is trained to detect and
correct for erroneous sensor use conditions based on error patterns
in sensor data. The system may determine resolutions for correcting
the detected erroneous sensor use conditions.
Inventors: |
GEE; ELAINE; (Windsor,
CA) ; NISHIDA; JEFFREY; (Chicago, IL) ;
AJEMBA; PETER; (Canyon Country, CA) ; NOGUEIRA;
KEITH; (Mission Hills, CA) ; VARSAVSKY; ANDREA;
(Santa Monica, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
MEDTRONIC MINIMED, INC. |
Northridge |
CA |
US |
|
|
Family ID: |
1000005362056 |
Appl. No.: |
17/121624 |
Filed: |
December 14, 2020 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G16H 40/63 20180101;
G06N 20/00 20190101; G16H 50/20 20180101 |
International
Class: |
G16H 50/20 20060101
G16H050/20; G06N 20/00 20060101 G06N020/00; G16H 40/63 20060101
G16H040/63 |
Claims
1. A sensor device for applying machine learning models to improve
integrated continuous glucose monitoring ("iCGM") performance of
continuous glucose monitoring ("CGM") calibration algorithms, the
sensor device comprising: memory configured to store a machine
learning model, wherein the machine learning model is trained to
identify outlier measurements from CGM sensor data based on sensor
glucose-dependent performance against iCGM criteria using training
data comprising clinical data on iCGM performance; and a processor
configured to: receive CGM sensor data; input the sensor data in
the machine learning model; receive an output from the machine
learning model indicating that the sensor data corresponds to an
outlier measurement of the outlier measurements; and blank the
sensor data based on the output.
2. The sensor device of claim 1, wherein the machine learning model
is trained using a set of training sensor data that is labeled
according to known classifications, the known classifications
comprising a large negative bias, large positive bias, or nominal
accuracy.
3. The sensor device of claim 1, wherein the machine learning model
is trained using a set of training sensor data that is labeled
according to known classifications, the known classifications
comprising poor accuracy, intermediate accuracy, or good
accuracy.
4. The sensor device of claim 1, wherein the sensor data comprises
a current signal, a voltage signal, or impedance spectroscopy
signals.
5. The sensor device of claim 4, wherein, to input the sensor data
in the machine learning model, the processor is further configured
to generate a multi-dimensional feature input based on the sensor
data.
6. The sensor device of claim 1, wherein, to blank the sensor data
based on the output, the processor is further configured to:
determine a variable for a CGM calibration algorithm based on the
output; and determine whether to blank the sensor data based on the
output from the machine learning model.
7. The sensor device of claim 1, wherein the sensor data is
received in first time intervals.
8. The sensor device of claim 1, wherein the processor is further
configured to: reset an outlier counter based on determining that a
first sensor datapoint does not correspond to an outlier
measurement; cause the outlier counter to be increased based on
determining that a second sensor datapoint corresponds to an
outlier measurement; compare the outlier counter to a threshold;
and terminate the sensor device based on determining that the
outlier counter has breached the threshold.
9. A method of applying machine learning models to improve
integrated continuous glucose monitoring ("iCGM") performance of
continuous glucose monitoring ("CGM") calibration algorithms by
classifying outlier measurements based on iCGM criteria, the method
comprising: receiving, at a sensor device, CGM sensor data;
inputting, at the sensor device, the sensor data in a machine
learning model, wherein the machine learning model is trained to
identify outlier measurements based on sensor glucose-dependent
performance against iCGM criteria using training data comprising
clinical data on iCGM performance; receiving, at the sensor device,
an output from the machine learning model indicating that the
sensor data corresponds to an outlier measurement of the outlier
measurements; and blanking the sensor data based on the output.
10. The method of claim 9, wherein the machine learning model is
trained using a set of training sensor data that is labeled
according to known classifications, the known classifications
comprising a large negative bias, large positive bias, or nominal
accuracy.
11. The method of claim 9, wherein the machine learning model is
trained using a set of training sensor data that is labeled
according to known classifications, the known classifications
comprising poor accuracy, intermediate accuracy, or good
accuracy.
12. The method of claim 9, wherein the sensor data comprises a
current signal, a voltage signal, or impedance spectroscopy
signals.
13. The method of claim 12, wherein inputting the sensor data in
the machine learning model comprises generating a multi-dimensional
feature input based on the sensor data.
14. The method of claim 9, wherein blanking the sensor data based
on the output further comprises: determining a variable for a CGM
calibration algorithm based on the output; and determining whether
to blank the sensor data based on the output from the machine
learning model.
15. The method of claim 9, wherein the sensor data is received in
first time intervals.
16. The method of claim 9, further comprising: resetting an outlier
counter based on determining that a first sensor datapoint does not
correspond to an outlier measurement; causing the outlier counter
to be increased based on determining that a second sensor datapoint
corresponds to an outlier measurement; comparing the outlier
counter to a threshold; and terminating the sensor device based on
determining that the outlier counter has breached the
threshold.
17. A non-transitory computer-readable media for continuous glucose
monitoring comprising instructions that, when executed by one or
more processors, cause operations comprising: receiving, at a
sensor device, CGM sensor data; inputting, at the sensor device,
the sensor data in a machine learning model, wherein the machine
learning model is trained to identify outlier measurements based on
sensor glucose-dependent performance against iCGM criteria using
training data comprising clinical data on iCGM performance;
receiving, at the sensor device an output from the machine learning
model indicating that the sensor data corresponds to an outlier
measurement; and blanking the sensor data based on the output.
18. The non-transitory computer-readable media of claim 17, wherein
the instructions cause further operations comprising: determining
whether a set of training sensor data in the training data
corresponds to known classification, wherein the known
classification comprises a large negative bias, large positive
bias, or nominal accuracy; labeling the set of training sensor data
with the known classification; and training the machine learning
model using the labeled set of training sensor data.
19. The non-transitory computer-readable media of claim 17, wherein
the instructions cause further operations comprising: determining
whether a set of training sensor data in the training data
corresponds to known classification, wherein the known
classification comprises poor accuracy, intermediate accuracy, or
good accuracy; labeling the set of training sensor data with the
known classification; and training the machine learning model using
the labeled set of training sensor data.
20. The non-transitory computer-readable media of claim 17, wherein
the instructions cause further operations comprising: resetting an
outlier counter based on determining that a first sensor datapoint
does not correspond to an outlier measurement; causing the outlier
counter to be increased based on determining that a second sensor
datapoint corresponds to an outlier measurement; comparing the
outlier counter to a threshold; and terminating the sensor device
based on determining that the outlier counter has breached the
threshold.
Description
FIELD
[0001] The present technology is generally related to sensor
technology, including sensors used for sensing a variety of
physiological parameters, e.g., glucose concentration.
BACKGROUND
[0002] Over the years, a variety of sensors have been developed for
detecting and/or quantifying specific agents or compositions in a
patient's blood, which enable patients and medical personnel to
monitor physiological conditions within the patient's body.
Illustratively, subjects may wish to monitor blood glucose levels
in a subject's body on a continuing basis. Thus, glucose sensors
have been developed for use in obtaining an indication of blood
glucose levels in a diabetic patient. Such readings are useful in
monitoring and/or adjusting a treatment regimen which typically
includes the regular administration of insulin to the patient.
Presently, a patient can measure his/her blood glucose ("BG") using
a BG measurement device (i.e., glucose meter), such as a test strip
meter, a continuous glucose measurement system (or a continuous
glucose monitor), or a hospital BG test. BG measurement devices use
various methods to measure the BG level of a patient, such as a
sample of the patient's blood, a sensor in contact with a bodily
fluid, an optical sensor, an enzymatic sensor, or a fluorescent
sensor. When the BG measurement device has generated a BG
measurement, the measurement is displayed on the BG measurement
device.
SUMMARY
[0003] Current continuous glucose monitoring ("CGM") systems use
CGM calibration algorithms to determine when measurements are
accurate. For example, accuracy may change based on wear, battery
life, and other factors. Current CGM systems determine accuracy
based on an independent input signal, whereas systems and methods
described herein utilize a multi-dimensional input signal for
determining accuracy. The multi-dimensional input signal may
include an Interstitial Current Signal ("Isig"), an Electrochemical
Impedance Spectroscopy Signal ("EIS"), a counter voltage ("Vcntr"),
and/or other signals. The multi-dimensional input signal improves
CGM performance, leading to more reliable determinations of
accuracy, wear, battery life, and other factors and providing a
user with more accurate data.
[0004] Though the use of a multi-dimensional input signal improves
CGM performance, present CGM requirements are difficult to scale
with the multi-dimensional inputs. Government agencies (e.g., the
Federal Drug Administration ("FDA")) impose restrictions and
requirements for the sensitivity and accuracy of CGMs. For example,
CGM devices are required to meet numerous criteria (e.g., FDA's
integrated continuous glucose monitoring ("iCGM") criteria) in
order for the sensor data to be considered accurate enough to
qualify for preferential treatment during regulatory the regulatory
approval process. The iCGM criteria is designed to characterize the
relative distribution of error of a CGM system to balance the
often-competing needs of an overall mean error value and short
tails in the error distribution. In addition, current systems used
as predicate devices in fashioning the iCGM criteria are based on
single signal analysis, so the criteria may be difficult to
generalize to systems based on multi-dimensional input signals as
described herein. To solve these problems, methods, systems, and
devices described herein may train a machine learning model to
classify multi-dimensional input signals in accordance with the
iCGM criteria. The outputs from the trained machine learning model
may be used to blank (i.e., remove, ignore) measurements, during
computation within a glucose estimation device, which do not meet
the iCGM criteria. Thus, the methods, systems, and devices
described herein allow for improved CGM techniques that are
compatible with the FDA's iCGM criteria and related criteria
designed to balance gross measures of accuracy with error
distributions featuring shorter tail.
[0005] More particularly, the methods, systems, and devices
describe training a machine learning model to identify outlier
measurements based on behavior signatures and informed by the iCGM
and similar criteria. The machine learning model may take as inputs
multi-dimensional CGM sensor data and may use training data to set
model parameters. The training data may include clinical data on
iCGM performance. In some embodiments, the system may classify the
training data according to known classifications (e.g., large
negative bias, large positive bias, nominal accuracy, poor
accuracy, intermediate accuracy, good accuracy, or other
classifications). The system may receive multi-dimensional CGM
sensor data from a sensor electrode or another computing device and
may input the sensor data into the machine learning model it
contains or contained in another computing device. Outputs from the
machine learning model may indicate whether the CGM sensor data
corresponds to an outlier (e.g., or to a known classification, as
described above) or fits a known pattern associated with an error
state or error condition. If the CGM sensor data corresponds to an
outlier, the system may not process the corresponding sensor data
or may blank or not display the sensor data on a display interface
of a user's device. In some examples, the system may blank sensor
data by foregoing to transmit the sensor data to a user device or
any other device with a display interface.
[0006] Another limitation of current systems is a loss of data due
to blanking. For example, if a sensor device is consistently
blanking, the quantity of data processed by the device or received
by a user of the sensor device may be inadequate. Accordingly,
methods, systems, and devices described herein may terminate the
sensor device (e.g., stop transmitting sensor data from that sensor
device) based on determining that a certain number of outlier
measurements has been reached or a certain number of blanking
instances has been activated during a specified period during
sensor wear. More particularly, the methods, systems, and devices
describe using an outlier counter (e.g., or a buffer, threshold, or
any other means) to track outlier measurements. For example, the
system may reset the outlier counter (e.g., to zero) upon
determining that a sensor datapoint does not correspond to an
outlier measurement (e.g., as determined by the machine learning
model). The system may increment the outlier counter for each
datapoint that is classified as an outlier by the machine learning
model. The system may use various additional criteria in
conjunction with the outlier counter (e.g., or other means for
tracking outlier measurements). For example, the outlier counter
may track a number of outlier measurements that are received
consecutively, in a certain time period, or within any other
constraints. Once the outlier counter breaches (e.g., meets or
exceeds) a threshold, the system may terminate the sensor device.
In some embodiments, the system may alert the user that the sensor
device must be replaced.
[0007] In some aspects, methods, systems, and devices for
continuous glucose monitoring are described. For example, the
system may retrieve a machine learning model that is trained to
identify outlier measurements based on iCGM criteria using training
data. In some embodiments, the training data may include clinical
data on iCGM performance. The system may receive CGM sensor data
and input the sensor data into the machine learning model. The
system may receive an output from the machine learning model
indicating that the sensor data corresponds to an outlier
measurement. Based on the output, the system may blank (e.g.,
ignore, eschew from transmitting to another device) or not display
the sensor data (e.g., on a display interface) on a user
device.
[0008] Another limitation of conventional continuous glucose
monitoring ("CGM") systems is inability to dynamically detect and
correct errors in the sensor data. CGM systems continuously present
sensor data for a user of a sensor device. The system must
therefore process and respond to characteristics of the sensor data
in real-time. Notably, any error in this data may result in severe
or even fatal effects to the user. Moreover, conventional CGM
systems lack the functionality to detect and correct for errors in
the data as they arise. Systems and methods described herein train
a machine learning model to detect error patterns in the sensor
data. For example, the system may train the machine learning model
to recognize error pattern characteristics and identify underlying
erroneous sensor use conditions. However, conventional pattern
recognition technology would be unsuccessful at identifying
erroneous sensor use conditions associated with error patterns in
the sensor data, as pattern recognition alone is insufficient for
understanding the complexities of the sensor data surrounding the
error patterns. Systems and methods described herein thus rely upon
context information relating to the sensor data in order to
determine the erroneous sensor use condition. For example, the
system relies upon historic information relating to the sensor
data, such as behaviors, trends, and patterns of the sensor data
within a time period. The behaviors, trends, and patterns of the
sensor data allow the system to identify between identical error
patterns that are associated with different erroneous sensor use
conditions. Additionally, the system accounts for behaviors and
patterns of the various sensor input features that make up the
sensor data. Identifying the correct erroneous sensor use condition
associated with the detected error pattern is essential for
correcting the error. Once the system has identified the correct
erroneous sensor use condition, the system may determine a viable
resolution for correcting the error in the data.
[0009] In order to comply with the iCGM criteria, the CGM system
must ensure that sensor data which does not comply with the iCGM
criteria is not shown to the user. With current systems, this leads
to excessive blanking in response to abnormal sensor data. Such
excessive blanking may deprive a user of a sensor device of
valuable sensor glucose data. Systems and methods described herein
improve upon current systems by detecting and correcting for errors
in complex sensor data in real time in order to maximize the
accurate data that the system is able to provide to the user in
compliance with iCGM criteria.
[0010] More particularly, methods, systems, and devices for
continuous glucose monitoring are described. For example, the
system may retrieve a machine learning model that is trained to
identify erroneous sensor use conditions based on sensor data error
patterns using training data. In some embodiments, the training
data may include clinical data on erroneous sensor use conditions.
The system may receive CGM sensor data and input the sensor data
into the machine learning model. The system may receive an output
from the machine learning model indicating an erroneous sensor use
condition based on an error pattern identified in the sensor data.
Based on the output, the system may determine and implement a
resolution in order to correct the identified erroneous sensor use
condition.
[0011] Various other aspects, features, and advantages will be
apparent through the detailed description and the drawings attached
hereto. It is also to be understood that both the foregoing general
description and the following detailed description are examples and
not restrictive of the scope of the invention. As used in the
specification and in the claims, the singular forms of "a," "an,"
and "the" include plural referents unless the context clearly
dictates otherwise. In addition, as used in the specification and
the claims, the term "or" means "and/or" unless the context clearly
dictates otherwise. Additionally, as used in the specification "a
portion," refers to a sub-part of, or the entirety of, a given item
(e.g., data) unless the context clearly dictates otherwise.
[0012] The details of one or more aspects of the disclosure are set
forth in the accompanying drawings and the description below. Other
features, objects, and advantages of the techniques described in
this disclosure will be apparent from the description and drawings,
and from the claims.
BRIEF DESCRIPTION OF THE DRAWINGS
[0013] A detailed description of embodiments of the invention will
be made with reference to the accompanying drawings, wherein like
numerals designate corresponding parts in the figures.
[0014] FIG. 1 illustrates wearable sensor electronics devices, in
accordance with one or more embodiments.
[0015] FIG. 2 is a perspective view of a subcutaneous sensor
insertion set and block diagram of a sensor electronics device, in
accordance with one or more embodiments.
[0016] FIG. 3 illustrates a substrate having two sides, a first
side which contains an electrode configuration and a second side
which contains electronic circuitry, in accordance with one or more
embodiments.
[0017] FIG. 4 illustrates a block diagram of a sensor electronics
device and a sensor including a plurality of electrodes, in
accordance with one or more embodiments.
[0018] FIG. 5 illustrates an alternative embodiment of the
invention including a sensor and a sensor electronics device, in
accordance with one or more embodiments.
[0019] FIG. 6 illustrates an electronic block diagram of the sensor
electrodes and a voltage being applied to the sensor electrodes, in
accordance with one or more embodiments.
[0020] FIG. 7 shows a flowchart of the steps involved in applying
machine learning models to improve integrated continuous glucose
monitoring ("iCGM") performance of continuous glucose monitoring
("CGM") calibration algorithms, in accordance with one or more
embodiments.
[0021] FIG. 8 shows a flowchart of the steps involved in applying
machine learning models to detect and correct for erroneous sensor
use conditions, in accordance with one or more embodiments.
[0022] FIG. 9 shows a machine learning model system for making
predictions that facilitate both classification of outlier
measurements based on iCGM criteria and detection and correction of
erroneous sensor use conditions, in accordance with one or more
embodiments.
[0023] FIG. 10 shows a flow diagram for conditional blanking and
termination and error detection and correction, in accordance with
one or more embodiments.
[0024] FIG. 11 shows a schematic of a sensor feature generator of
FIG. 10, in accordance with one or more embodiments.
[0025] FIG. 12 shows a graph highlighting error patterns in sensor
data, in accordance with one or more embodiments.
DETAILED DESCRIPTION
[0026] In the following description, reference is made to the
accompanying drawings which form a part hereof and which illustrate
several embodiments of the present inventions. It is understood
that other embodiments may be utilized, and structural and
operational changes may be made without departing from the scope of
the present inventions.
[0027] The inventions herein are described below with reference to
flowchart illustrations of methods, systems, devices, apparatus,
and programming and computer program products. It will be
understood that each block of the flowchart illustrations, and
combinations of blocks in the flowchart illustrations, can be
implemented by programing instructions, including computer program
instructions (as can any menu screens described in the figures).
These computer program instructions may be loaded onto a computer
or other programmable data processing apparatus (such as a
controller, microcontroller, or processor in a sensor electronics
device) to produce a machine, such that the instructions which
execute on the computer or other programmable data processing
apparatus create instructions for implementing the functions
specified in the flowchart block or blocks. These computer program
instructions may also be stored in a computer-readable memory that
can direct a computer or other programmable data processing
apparatus to function in a particular manner, such that the
instructions stored in the computer-readable memory produce an
article of manufacture including instructions which implement the
function specified in the flowchart block or blocks. The computer
program instructions may also be loaded onto a computer or other
programmable data processing apparatus to cause a series of
operational steps to be performed on the computer or other
programmable apparatus to produce a computer implemented process
such that the instructions which execute on the computer or other
programmable apparatus provide steps for implementing the functions
specified in the flowchart block or blocks, and/or menus presented
herein. Programming instructions may also be stored in and/or
implemented via electronic circuitry (e.g., storage circuitry,
processing circuitry), including integrated circuits (ICs) and
Application Specific Integrated Circuits (ASICs) used in
conjunction with sensor devices, apparatuses, and systems. The
following terms and definitions may also be used herein:
TABLE-US-00001 Term Definition BG Blood Glucose value in mg/dL
typically from a fingerstick reading. Assumed use is for a sensor
calibration Calibrated Mode Sensor operation mode in which the
algorithm expects to receive BG calibrations as part of regular
operation CE Calibration Error CF (or calFactor) Calibration
Factor, sensor sensitivity to glucose used to calculate sensor
glucose. Units are mg/dL/nA CR (or Cr) Calibration Ratio,
sensitivity based on a single BG and Isig Discard Packet flagged to
be invalid based on Isig. early calibration Temporary CF update on
the packet following a BG EIS Electrochemical Impedance
Spectroscopy, Diagnostic capability to measure impedances at
varying frequencies applied by the AFE IC final calibration Refers
to updates to CF and other variables which may occur 10-15 minutes
after a BG entry fisig Filtered Isig, used in calibration and SG
calculation GST Glucose Sensor Transmitter GOx Glucose Oxidase
initialization Sensor Initialization. This typically refers to data
collection activities during sensor warm up period Instant CE check
based on prior Isig, determines if a calibration error BG can be
used for calibration invalid packet Refers to a packet being
flagged as invalid. Packets flagged as invalid do not show SG to
the user. Isig 5-minute reading of sensor current in nA. Sometimes
called "raw Isig" Isig 1 1-minute reading of sensor current in nA.
Sometimes called "1-minute Isig" Isig Dip Isig Dip Calibration.
Refers to logic which may adjust CF following a calibration on an
abnormally low Isig MAX CR Maximum acceptable CR MIN CR Minimum
acceptable CR Not Calibrated Sensor operation mode in which the
Mode algorithm does not expect to receive BG calibrations as part
of regular operations. The algorithm can utilize BG calibrations if
any is made available. Packet Refers to the collection of variables
(or SG Packet calculated at the 5-minute interval, including or
Isig Packet) Isig, sg, etc. SG Sensor Glucose value in mg/dL Vset
Voltage potential
[0028] FIG. 1 illustrates wearable sensor electronics devices 100
and 150, in accordance with one or more embodiments. In some
embodiments, wearable sensor electronics device 100 may be an
infusion pump. In some embodiments, the infusion pump may include a
display. In some embodiments, wearable sensor electronics device
100 may be a combination infusion pump/glucose sensor. In some
embodiments, wearable sensor electronics device 150 may be a
cellular phone or any computing device. In some embodiments,
wearable sensor electronics devices 100 and 150 may include a
computer, a personal digital assistant, a pager, or any other
suitable wearable device. In some embodiments, wearable sensor
electronics devices 100 and 150 may house components described
below in relation to FIGS. 2-6.
[0029] FIG. 2 is a perspective view of a subcutaneous sensor
insertion set and a block diagram of a sensor electronics device
(e.g., wearable sensor electronics devices 100 or 150, as shown in
FIG. 1, or any other suitable sensor electronics device). As
illustrated in FIG. 2, a subcutaneous sensor set 10 is provided for
subcutaneous placement of an active portion of a flexible sensor 12
(see, e.g., FIG. 3), or the like, at a selected site in the body of
a user. The subcutaneous or percutaneous portion of the sensor set
10 includes a hollow, slotted insertion needle 14, and a cannula
16. The needle 14 is used to facilitate quick and easy subcutaneous
placement of the cannula 16 at the subcutaneous insertion site.
Inside the cannula 16 is a sensing portion 18 of the sensor 12 to
expose one or more sensor electrodes 20 to the user's bodily fluids
through a window 22 formed in the cannula 16. In one embodiment,
the one or more sensor electrodes 20 may include a counter
electrode, a reference electrode, and one or more working
electrodes. After insertion, the insertion needle 14 is withdrawn
to leave the cannula 16 with the sensing portion 18 and the sensor
electrodes 20 in place at the selected insertion site.
[0030] In particular embodiments, the subcutaneous sensor set 10
facilitates accurate placement of a flexible thin film
electrochemical sensor 12 of the type used for monitoring specific
blood parameters representative of a user's condition. The sensor
12 monitors glucose levels in the body and may be used in
conjunction with automated or semi-automated medication infusion
pumps (e.g., wearable sensor electronics device 100, as shown in
FIG. 1) of the external or implantable type to control delivery of
insulin to a diabetic patient, as described, e.g., in U.S. Pat.
Nos. 4,562,751; 4,678,408; 4,685,903 or 4,573,994, which are herein
incorporated by reference.
[0031] Particular embodiments of the flexible electrochemical
sensor 12 are constructed in accordance with thin film mask
techniques to include elongated thin film conductors embedded or
encased between layers of a selected insulative material such as
polyimide film or sheet, and membranes. The sensor electrodes 20 at
a tip end of the sensing portion 18 are exposed through one of the
insulative layers for direct contact with patient blood or other
body fluids, when the sensing portion 18 (or active portion) of the
sensor 12 is subcutaneously placed at an insertion site. The
sensing portion 18 is joined to a connection portion 24 that
terminates in conductive contact pads, or the like, which are also
exposed through one of the insulative layers. In alternative
embodiments, other types of implantable sensors, such as chemical
based, optical based, or the like, may be used.
[0032] As is known in the art, the connection portion 24 and the
contact pads are generally adapted for a direct wired electrical
connection to a suitable monitor or sensor electronics device 200
(e.g., wearable sensor electronics devices 100 or 150, as shown in
FIG. 1, or any other suitable sensor electronics device) for
monitoring a user's condition in response to signals derived from
the sensor electrodes 20. Further description of flexible thin film
sensors of this general type are to be found in U.S. Pat. No.
5,391,250, entitled METHOD OF FABRICATING THIN FILM SENSORS, which
is herein incorporated by reference. The connection portion 24 may
be conveniently connected electrically to the monitor or sensor
electronics device 200 or by a connector block 28 (or the like) as
shown and described in U.S. Pat. No. 5,482,473, entitled FLEX
CIRCUIT CONNECTOR, which is also herein incorporated by reference.
Thus, in accordance with some embodiments, subcutaneous sensor sets
10 may be configured or formed to work with either a wired or a
wireless characteristic monitor system.
[0033] The sensor electrodes 20 may be used in a variety of sensing
applications and may be configured in a variety of ways. For
example, the sensor electrodes 20 may be used in physiological
parameter sensing applications in which some type of biomolecule is
used as a catalytic agent. For example, the sensor electrodes 20
may be used in a glucose and oxygen sensor having a glucose oxidase
(GOx) enzyme catalyzing a reaction with the sensor electrodes 20.
The sensor electrodes 20, along with a biomolecule or some other
catalytic agent, may be placed in a human body in a vascular or
non-vascular environment. For example, the sensor electrodes 20 and
biomolecule may be placed in a vein and be subjected to a blood
stream or may be placed in a subcutaneous or peritoneal region of
the human body.
[0034] The monitor 200 may also be referred to as a sensor
electronics device 200. The monitor 200 may include a power source
210, a sensor interface 222, processing electronics 224, and data
formatting electronics 228. The monitor 200 may be coupled to the
sensor set 10 by a cable 202 through a connector that is
electrically coupled to the connector block 28 of the connection
portion 24. In an alternative embodiment, the cable may be omitted.
In this embodiment, the monitor 200 may include an appropriate
connector for direct connection to the connection portion 204 of
the sensor set 10. The sensor set 10 may be modified to have the
connector portion 204 positioned at a different location, e.g., on
top of the sensor set to facilitate placement of the monitor 200
over the sensor set.
[0035] In one embodiment, the sensor interface 222, the processing
electronics 224, and the data formatting electronics 228 are formed
as separate semiconductor chips, however, alternative embodiments
may combine the various semiconductor chips into a single, or
multiple customized semiconductor chips. The sensor interface 222
connects with the cable 202 that is connected with the sensor set
10.
[0036] The power source 210 may be a battery. The battery can
include three series silver oxide 357 battery cells. In alternative
embodiments, different battery chemistries may be utilized, such as
lithium-based chemistries, alkaline batteries, nickel metal
hydride, or the like, and a different number of batteries may be
used. The monitor 200 provides power to the sensor set via the
power source 210, through the cable 202 and cable connector 204. In
one embodiment, the power is a voltage provided to the sensor set
10. In another embodiment, the power is a current provided to the
sensor set 10. In an embodiment, the power is a voltage provided at
a specific voltage to the sensor set 10.
[0037] FIG. 3 illustrates an implantable sensor, and electronics
for driving the implantable sensor in accordance with one
embodiment. FIG. 3 shows a substrate 320 having two sides, a first
side 322 of which contains an electrode configuration and a second
side 324 of which contains electronic circuitry (e.g., storage
circuitry, processing circuitry, etc.). As may be seen in FIG. 3, a
first side 322 of the substrate comprises two counter
electrode-working electrode pairs 340, 342, 344, 346 on opposite
sides of a reference electrode 348. A second side 324 of the
substrate comprises electronic circuitry. As shown, the electronic
circuitry may be enclosed in a hermetically sealed casing 326,
providing a protective housing for the electronic circuitry. This
allows the sensor substrate 320 to be inserted into a vascular
environment or other environment which may subject the electronic
circuitry to fluids. By sealing the electronic circuitry in a
hermetically sealed casing 326, the electronic circuitry may
operate without risk of short circuiting by the surrounding fluids.
Also shown in FIG. 3 are pads 328 to which the input and output
lines of the electronic circuitry may be connected. The electronic
circuitry itself may be fabricated in a variety of ways. According
to an embodiment, the electronic circuitry may be fabricated as an
integrated circuit using techniques common in the industry.
[0038] FIG. 4 illustrates a general block diagram of an electronic
circuit for sensing an output of a sensor according to one
embodiment. At least one pair of sensor electrodes 410 may
interface to a data converter 412, the output of which may
interface to a counter 414. The counter 414 may be controlled by
control logic 416. The output of the counter 414 may connect to a
line interface 418. The line interface 418 may be connected to
input and output lines 420 and may also connect to the control
logic 416. The input and output lines 420 may also be connected to
a power rectifier 422.
[0039] The sensor electrodes 410 may be used in a variety of
sensing applications and may be configured in a variety of ways.
For example, the sensor electrodes 410 may be used in physiological
parameter sensing applications in which some type of biomolecule is
used as a catalytic agent. For example, the sensor electrodes 410
may be used in a glucose and oxygen sensor having a GOx enzyme
catalyzing a reaction with the sensor electrodes 410. The sensor
electrodes 410, along with a biomolecule or some other catalytic
agent, may be placed in a human body in a vascular or non-vascular
environment. For example, the sensor electrodes 410 and biomolecule
may be placed in a vein and be subjected to a blood stream.
[0040] FIG. 5 illustrates a block diagram of a sensor electronics
device (e.g., wearable sensor electronics devices 100 or 150, as
shown in FIG. 1, or any other suitable sensor electronics device)
and a sensor including a plurality of electrodes according to an
embodiment herein. FIG. 5 includes system 500. System 500 includes
a sensor 555 and a sensor electronics device 560. The sensor 555
includes a counter electrode 565, a reference electrode 570, and a
working electrode 575. The sensor electronics device 560 includes a
power supply 580, a regulator 585, a signal processor 590, a
measurement processor 595, and a display/transmission module 597.
The power supply 580 provides power (in the form of either a
voltage, a current, or a voltage including a current) to the
regulator 585. The regulator 585 transmits a regulated voltage to
the sensor 555. In one embodiment, the regulator 585 transmits a
voltage to the counter electrode 565 of the sensor 555.
[0041] The sensor 555 creates a sensor signal indicative of a
concentration of a physiological characteristic being measured. For
example, the sensor signal may be indicative of a blood glucose
reading. In an embodiment utilizing subcutaneous sensors, the
sensor signal may represent a level of hydrogen peroxide in a
subject. In an embodiment where blood or cranial sensors are
utilized, the amount of oxygen is being measured by the sensor and
is represented by the sensor signal. In an embodiment utilizing
implantable or long-term sensors, the sensor signal may represent a
level of oxygen in the subject. The sensor signal is measured at
the working electrode 575. In one embodiment, the sensor signal may
be a current measured at the working electrode. In an embodiment,
the sensor signal may be a voltage measured at the working
electrode.
[0042] The signal processor 590 receives the sensor signal (e.g., a
measured current or voltage) after the sensor signal is measured at
the sensor 555 (e.g., the working electrode). The signal processor
590 processes the sensor signal and generates a processed sensor
signal. The measurement processor 595 receives the processed sensor
signal and calibrates the processed sensor signal utilizing
reference values. In one embodiment, the reference values are
stored in a reference memory and provided to the measurement
processor 595. The measurement processor 595 generates sensor
measurements. The sensor measurements may be stored in a
measurement memory (not shown) or by circuitry (e.g., storage
circuitry). The sensor measurements may be sent to a
display/transmission device to be either displayed on a display in
a housing with the sensor electronics or transmitted to an external
device.
[0043] The sensor electronics device 560 may be a monitor which
includes a display to display physiological characteristics
readings. The sensor electronics device 560 may also be installed
in a desktop computer, a pager, a television including
communications capabilities, a laptop computer, a server, a network
computer, a personal digital assistant (PDA), a portable telephone
including computer functions, an infusion pump including a display
(e.g., wearable sensor electronics device 100, as shown in FIG. 1),
a glucose sensor including a display, and/or a combination infusion
pump/glucose sensor (e.g., wearable sensor electronics device 100,
as shown in FIG. 1). The sensor electronics device 560 may be
housed in a blackberry (e.g., wearable sensor electronics device
150, as shown in FIG. 1), a network device, a home network device,
or an appliance connected to a home network.
[0044] FIG. 5 also includes system 550. System 550 includes a
sensor electronics device 560 and a sensor 555. The sensor includes
a counter electrode 565, a reference electrode 570, and a working
electrode 575. The sensor electronics device 560 includes a
microcontroller 510 and a digital-to-analog converter (DAC) 520.
The sensor electronics device 560 may also include a
current-to-frequency converter (I/F converter) 530.
[0045] The microcontroller 510 includes software program code,
which when executed, or programmable logic which, causes the
microcontroller 510 to transmit a signal to the DAC 520, where the
signal is representative of a voltage level or value that is to be
applied to the sensor 555. The DAC 520 receives the signal and
generates the voltage value at the level instructed by the
microcontroller 510. In one embodiment, the microcontroller 510 may
change the representation of the voltage level in the signal
frequently or infrequently. Illustratively, the signal from the
microcontroller 510 may instruct the DAC 520 to apply a first
voltage value for one second and a second voltage value for two
seconds.
[0046] The sensor 555 may receive the voltage level or value. In
one embodiment, the counter electrode 565 may receive the output of
an operational amplifier which has as inputs the reference voltage
and the voltage value from the DAC 520. The application of the
voltage level causes the sensor 555 to create a sensor signal
indicative of a concentration of a physiological characteristic
being measured. In an embodiment, the microcontroller 510 may
measure the sensor signal (e.g., a current value) from the working
electrode. Illustratively, a sensor signal measurement circuit 531
may measure the sensor signal. In an embodiment, the sensor signal
measurement circuit 531 may include a resistor and the current may
be passed through the resistor to measure the value of the sensor
signal. In an embodiment, the sensor signal may be a current level
signal and the sensor signal measurement circuit 531 may be a
current-to-frequency (I/F) converter 530. The current-to-frequency
converter 530 may measure the sensor signal in terms of a current
reading, convert it to a frequency-based sensor signal, and
transmit the frequency-based sensor signal to the microcontroller
510. In some embodiments, the microcontroller 510 may be able to
receive frequency-based sensor signals easier than
non-frequency-based sensor signals. The microcontroller 510
receives the sensor signal, whether frequency-based or
non-frequency-based, and determines a value for the physiological
characteristic of a subject, such as a blood glucose level. The
microcontroller 510 may include program code, which when executed
or run, is able to receive the sensor signal and convert the sensor
signal to a physiological characteristic value. In one embodiment,
the microcontroller 510 may convert the sensor signal to a blood
glucose level. In an embodiment, the microcontroller 510 may
utilize measurements stored within an internal memory or by
circuitry (e.g., storage circuitry) in order to determine the blood
glucose level of the subject. In an embodiment, the microcontroller
510 may utilize measurements stored within a memory external to the
microcontroller 510 or by circuitry to assist in determining the
blood glucose level of the subject.
[0047] After the physiological characteristic value is determined
by the microcontroller 510, the microcontroller 510 may store
measurements of the physiological characteristic values for a
number of time periods. For example, a blood glucose value may be
sent to the microcontroller 510 from the sensor in intervals (e.g.,
every second or five seconds), and the microcontroller may save
sensor measurements in intervals (e.g., for five minutes or ten
minutes of estimated SG values from the calibration algorithm). The
microcontroller 510 may transfer the measurements of the
physiological characteristic values to a display on the sensor
electronics device 560 or a display interface of another user
device. For example, the sensor electronics device 560 may be a
monitor which includes a display that provides a blood glucose
reading for a subject. In one embodiment, the microcontroller 510
may transfer the measurements of the physiological characteristic
values to an output interface of the microcontroller 510. The
output interface of the microcontroller 510 may transfer the
measurements of the physiological characteristic values, e.g.,
sensor glucose values, to an external device, e.g., an infusion
pump (e.g., wearable sensor electronics device 100, as shown in
FIG. 1), a combined infusion pump/glucose meter (e.g., wearable
sensor electronics device 100, as shown in FIG. 1), a computer, a
personal digital assistant, a pager, a network appliance, a server,
a cellular phone (e.g., wearable sensor electronics device 150, as
shown in FIG. 1), or any computing device.
[0048] FIG. 6 illustrates an electronic block diagram of the sensor
electrodes and a voltage being applied to the sensor electrodes
according to an embodiment. In some embodiments, FIG. 6 may
illustrate an electrode with a GOx sensor and/or an electrode
capable of sensing GOx. For example, FIG. 6 may illustrate a
working electrode with a GOx sensor that functions with a
background electrode in which the background electrode has no GOx
sensor (e.g., as discussed below in relation to FIGS. 8 and 9). The
system may then compare the first signal and the second signal to
detect ingestion of a medication by the user. The system may
generate a sensor glucose value based on the comparison. In the
embodiment illustrated in FIG. 6, an op amp 630 or other
servo-controlled device may connect to sensor electrodes 610
through a circuit/electrode interface 638. The op amp 630,
utilizing feedback through the sensor electrodes, attempts to
maintain a prescribed voltage (what the DAC may desire the applied
voltage to be) between a reference electrode 632 and a working
electrode 634 by adjusting the voltage at a counter electrode 636.
Current may then flow from a counter electrode 636 to a working
electrode 634. Such current may be measured to ascertain the
electrochemical reaction between the sensor electrodes 610 and the
biomolecule of a sensor that has been placed in the vicinity of the
sensor electrodes 610 and used as a catalyzing agent. The circuitry
(e.g., processing circuitry) disclosed in FIG. 6 may be utilized in
a long-term or implantable sensor or may be utilized in a
short-term or subcutaneous sensor.
[0049] In a long-term sensor embodiment, where a GOx enzyme is used
as a catalytic agent in a sensor, current may flow from the counter
electrode 636 to a working electrode 634 only if there is oxygen in
the vicinity of the enzyme and the sensor electrodes 610.
Illustratively, if the voltage set at the reference electrode 632
is maintained at about 0.5 volts, the amount of current flowing
from the counter electrode 636 to a working electrode 634 has a
fairly linear relationship with unity slope to the amount of oxygen
present in the area surrounding the enzyme and the electrodes.
Thus, increased accuracy in determining an amount of oxygen in the
blood may be achieved by maintaining the reference electrode 632 at
about 0.5 volts and utilizing this region of the current-voltage
curve for varying levels of blood oxygen. Different embodiments may
utilize different sensors having biomolecules other than a glucose
oxidase enzyme and may, therefore, have voltages other than 0.5
volts set at the reference electrode.
[0050] As discussed above, during initial implantation or insertion
of the sensor 610, the sensor 610 may provide inaccurate readings
due to the adjusting of the subject to the sensor and also
electrochemical byproducts caused by the catalyst utilized in the
sensor. A stabilization period is needed for many sensors in order
for the sensor 610 to provide accurate readings of the
physiological parameter of the subject. During the stabilization
period, the sensor 610 does not provide accurate blood glucose
measurements. Users and manufacturers of the sensors may desire to
improve the stabilization timeframe for the sensor so that the
sensors can be utilized quickly after insertion into the subject's
body or a subcutaneous layer of the subject.
[0051] In previous sensor electrode systems, the stabilization
period or timeframe was one hour to three hours. In order to
decrease the stabilization period or timeframe and increase the
timeliness of accuracy of the sensor, a sensor (or electrodes of a
sensor) may be subjected to a number of pulses rather than the
application of one pulse followed by the application of another
voltage. for the second time period. In one embodiment, the first
voltage may be 1.07 volts. In an embodiment, the first voltage may
be 0.535 volts. In an embodiment, the first voltage may be
approximately 0.7 volts.
[0052] FIG. 7 shows a flowchart of the steps involved in applying
machine learning models (e.g., supervised machine learning models,
unsupervised machine learning models, semi-supervised machine
learning models, or any other suitable type of machine learning
model) to improve integrated continuous glucose monitoring ("iCGM")
performance of continuous glucose monitoring ("CGM") calibration
algorithms by classifying outlier measurements based on iCGM
criteria, in accordance with one or more embodiments. For example,
process 700 may represent the steps taken by one or more devices as
shown in FIGS. 1-6.
[0053] At step 702, process 700 (e.g., using components described
in FIGS. 1-6) retrieves a machine learning model. In some
embodiments, the machine learning model is trained to identify
outlier measurements based on iCGM criteria using training data.
For example, the training data may comprise clinical data on iCGM
performance. As discussed below in relation to FIG. 7, process 700
may classify the training data in a number of ways. For example, in
some embodiments, process 700 may determine whether a set of
training sensor data in the training data corresponds to known
classification, wherein the known classification comprises a large
negative bias, large positive bias, or nominal accuracy. In some
embodiments, in order to determine whether the training sensor data
has a large negative bias, large positive bias, or nominal
accuracy, process 700 may compare the training sensor data to one
or more thresholds (e.g., a negative bias threshold or a positive
bias threshold). In some embodiments, this determination may be
calibration algorithm dependent since the bias is calculated from
the sensor glucose value. Based on whether the training sensor data
satisfies the negative bias threshold, process 700 may determine
whether the training sensor data has outlier performance. Based on
whether the training sensor data satisfies the positive bias
threshold, process 700 may determine whether the training sensor
data has a large positive bias. If the training sensor data does
not satisfy either threshold, process 700 may determine that the
training sensor data has nominal accuracy. It should be noted that
iCGM criteria are calculated on aggregate data. While it may be
possible to calculate bias based on the estimated SG values (e.g.,
predicted from the sensor data), the machine learning model can
predict signals that have outlier behavior that can skew iCGM
performance downstream (e.g., as calculated based on the aggregate
data).
[0054] In some other embodiments, process 700 may determine whether
a set of training sensor data in the training data corresponds to
known classification, wherein the known classification comprises
poor accuracy, intermediate accuracy, or good accuracy. In some
embodiments, accuracy may be measured using the mean absolute
relative difference (MARD) and bias. Depending on the range of the
estimated SG value, a hit criteria for iCGM may shift between the
MARD and bias metrics. For example, for certain SG values, the hit
criteria may specify a certain bias, while for other SG values, the
hit criteria may specify a MARD. In some embodiments, in order to
determine whether the training sensor data has poor accuracy,
intermediate accuracy, or good accuracy, process 700 may compare
the training sensor data to one or more thresholds (e.g., an
intermediate accuracy threshold and a good accuracy threshold).
Based on whether the training sensor data satisfies the
intermediate accuracy threshold, process 700 may determine whether
the training sensor data has intermediate accuracy. Based on
whether the training sensor data satisfies the good accuracy
threshold, process 700 may determine whether the training sensor
data has good accuracy. If the training sensor data does not
satisfy either threshold, process 700 may determine that the
training sensor data has poor accuracy. In some embodiments,
process 700 may label the training sensor data with the known
classification.
[0055] At step 704, process 700 (e.g., using components described
in FIGS. 1-6) receives CGM sensor data. For example, process 700
may receive the sensor data at the sensor device. For example, the
sensor data may comprise an Interstitial Current Signal ("Isig"),
counter voltage ("Vcntr"), and Electrochemical Impedance
Spectroscopy Signal ("EIS"). In some embodiments, process 700 may
receive the sensor data in first time intervals (e.g., every five
minutes or any other predetermined period of time).
[0056] At step 706, process 700 (e.g., using components described
in FIGS. 1-6) inputs the sensor data in the machine learning model.
For example, inputting the sensor data in the machine learning
model may comprise generating a multi-dimensional feature input
based on the sensor data. In some embodiments, the
multi-dimensional feature input may include Isig, Isig rate of
change, Isig Noise, real and imaginary EIS signals at a range of
frequencies, sensor age (e.g., time since initialization), Vcntr,
or other features.
[0057] At step 708, process 700 (e.g., using components described
in FIGS. 1-6) receives an output from the machine learning model
indicating that the sensor data corresponds to an outlier
measurement. In some embodiments, the machine learning model may
indicate a known classification to which the sensor data
corresponds (e.g., large negative bias, large positive bias,
nominal accuracy, poor accuracy, intermediate accuracy, good
accuracy, etc.).
[0058] At step 710, process 700 (e.g., using components described
in FIGS. 1-6) blanks the sensor data based on the output. For
example, blanking may comprise temporarily removing, blocking,
replacing, freezing, or otherwise blanking the sensor data (e.g.,
eschew transmitting sensor data to another device, eschew
displaying sensor data on a display interface). In some
embodiments, blanking outlier signals may limit the number of poor
sensor glucose estimates that reach the user, which could lead to
adverse treatment decisions. For example, reporting a sensor
glucose value that is over-reading when a user is in a hypoglycemic
state could lead to incorrect treatment decisions, such as insulin
dosing that further drives the user deeper into hypoglycemia.
Blanking outliers can reduce the instances in which sensor
performance falls into the high-risk zones of the Clarke Error Grid
(e.g., which quantifies clinical accuracy of patient estimates of
their current blood glucose as compared to the blood glucose value
obtained in their meter) and can improve accuracy and ability to
meet iCGM. In some embodiments, to blank the sensor data, process
700 (e.g., processors) may determine a variable for a CGM
calibration algorithm based on the output from the machine learning
model and determine whether to blank the sensor based on the CGM
calibration algorithm output.
[0059] It is contemplated that the steps or descriptions of FIG. 7
may be used with any other embodiment of this disclosure. In
addition, the steps and descriptions described in relation to FIG.
7 may be done in alternative orders or in parallel to further the
purposes of this disclosure. For example, each of these steps may
be performed in any order or in parallel or substantially
simultaneously to reduce lag or increase the speed of the system or
method. Furthermore, it should be noted that any of the devices or
equipment discussed in relation to FIGS. 3-5 could be used to
perform one or more of the steps in FIG. 7.
[0060] FIG. 8 shows a flowchart of the steps involved in applying
machine learning models to detect and correct for erroneous sensor
use conditions, in accordance with one or more embodiments. For
example, process 800 may represent the steps taken by one or more
devices as shown in FIGS. 1-6.
[0061] At step 802, process 800 (e.g., using components described
in FIGS. 1-6) retrieves a machine learning model that is trained to
identify erroneous sensor use conditions based on sensor data error
patterns. For example, error patterns may be characteristics (e.g.,
behaviors, trends, patterns) of the sensor data. For example, in
some embodiments, these characteristics of the sensor data may
include a downward trend over time (e.g., over the lifetime of a
sensor), a spike or drop-off in a signal followed by a return to
normal in a short time (e.g., over several seconds), an abnormality
that repeats several times over a time period (e.g., several
minutes), or other behaviors, trends, or patterns. In some
embodiments, the error pattern may depend on a time frame within
which it occurs. For example, a certain behavior of a signal may
only be characterized as an error pattern if it occurs within a
certain time period (e.g., five seconds). In some embodiments, a
certain behavior of a signal may only be characterized as an error
pattern if it reaches a threshold magnitude (e.g., a minimum
required deviation from a normal signal). In some embodiments, a
behavior of a signal may only be characterized as an error pattern
if it reaches a certain number of repetitions (e.g., three
repetitions). Other data characteristics may factor into error
pattern detection. In some embodiments, the machine learning model
may be trained to recognize error patterns (e.g., such as those
described above) using training data. For example, the training
data may comprise clinical data on erroneous sensor use conditions.
In some embodiments, the training data may additionally include
context information (e.g., historic information, sensor data
features, etc.) relating to the sensor data. In such cases, sensor
data error patterns featuring a known erroneous sensor use
condition may be labeled and used to train the system to identify
the sensor data error patterns in non-training scenarios. In some
embodiments, the system may retrieve a machine learning model that
is trained for a particular user based on clinical data from
similar users. For example, the system may use clinical data from
users of a similar age, height, weight, or level of athleticism,
such that the machine learning model is trained to recognized error
patterns that are most likely to occur for the user.
[0062] At step 804, process 800 (e.g., using components described
in FIGS. 1-6) receives CGM sensor data. For example, process 800
may receive the CGM sensor data at a sensor device. For example,
the sensor device may be associated with a user device of a user.
The sensor data may be real-time sensor data from the sensor
device. At step 806, process 800 (e.g., using circuitry described
in FIGS. 1-6) inputs the sensor data into the machine learning
model.
[0063] At step 808, process 800 (e.g., using components described
in FIGS. 1-6) receives an output from the machine learning model
indicating an erroneous sensor use condition. For example, the
erroneous sensor use condition may be based on an error pattern
identified in the sensor data. In some embodiments, the erroneous
sensor use condition may be further based upon the context
information relating to the sensor data. In some embodiments,
identified error patterns may be associated with multiple erroneous
sensor use conditions.
[0064] It is contemplated that the steps or descriptions of FIG. 8
may be used with any other embodiment of this disclosure. In
addition, the steps and descriptions described in relation to FIG.
8 may be done in alternative orders or in parallel to further the
purposes of this disclosure. For example, each of these steps may
be performed in any order or in parallel or substantially
simultaneously to reduce lag or increase the speed of the system or
method. Furthermore, it should be noted that any of the devices or
equipment discussed in relation to FIGS. 3-5 could be used to
perform one or more of the steps in FIG. 8.
[0065] FIG. 9 shows a machine learning model system for making
predictions that facilitate both classification of outlier
measurements based on iCGM criteria and detection and correction of
erroneous sensor use conditions, in accordance with one or more
embodiments.
[0066] In some embodiments, the machine learning model system may
include one or more neural networks or other machine learning
models. As an example, neural networks may be based on a large
collection of neural units (or artificial neurons). Neural networks
may loosely mimic the manner in which a biological brain works
(e.g., via large clusters of biological neurons connected by
axons). Each neural unit of a neural network may be connected with
many other neural units of the neural network. Such connections can
be enforcing or inhibitory in their effect on the activation state
of connected neural units. In some embodiments, each individual
neural unit may have a summation function which combines the values
of all its inputs together. In some embodiments, each connection
(or the neural unit itself) may have a threshold function such that
the signal must surpass the threshold before it propagates to other
neural units. These neural network systems may be self-learning and
trained, rather than explicitly programmed, and can perform
significantly better in certain areas of problem solving, as
compared to traditional computer programs. In some embodiments,
neural networks may include multiple layers (e.g., where a signal
path traverses from front layers to back layers). In some
embodiments, back propagation techniques may be utilized by the
neural networks, where forward stimulation is used to reset weights
on the "front" neural units. In some embodiments, stimulation and
inhibition for neural networks may be more free flowing, with
connections interacting in a more chaotic and complex fashion.
[0067] In some embodiments, the machine learning model system may
update its configurations (e.g., weights, biases, or other
parameters) based on its assessment of the predictions. Memory may
store training data and one or more trained machine learning
models.
[0068] As an example, a machine learning model 900 may take inputs
902 and provide outputs 904. In one use case, outputs 904 may be
fed back (e.g., active feedback) to machine learning model 900 as
input to train machine learning model 900 (e.g., alone or in
conjunction with user indications of the accuracy of outputs 904,
labels associated with the inputs 902, or with other reference
feedback information). In another use case, machine learning model
900 may update its configurations (e.g., weights, biases, or other
parameters) based on its assessment of its prediction (e.g.,
outputs 904) and reference feedback information (e.g., user
indication of accuracy, reference labels, or other information). In
another use case, where machine learning model 900 is a neural
network, connection weights may be adjusted to reconcile
differences between the neural network's prediction and the
reference feedback. In a further use case, one or more neurons (or
nodes) of the neural network may require that their respective
errors be sent backward through the neural network to them to
facilitate the update process (e.g., backpropagation of error).
Updates to the connection weights may, for example, be reflective
of the magnitude of error propagated backward after a forward pass
has been completed. In this way, for example, the machine learning
model 900 may be trained to generate better predictions.
[0069] For example, in some embodiments, inputs 902 may comprise
CGM sensor data (e.g., sensor glucose data), and reference feedback
information 904 (which feeds back as input to the machine learning
model 900) may include clinical data on iCGM performance. For
example, the clinical data may be labeled training data (e.g.,
labeled as outlier, not outlier). Accordingly, when machine
learning model 900 receives a particular glucose measurement as
input 902, machine learning model 900 may provide an output 904
including a prediction of whether the measurement is an outlier.
This outlier information may be used to correct future signals.
[0070] In some embodiments, the system may label the clinical data
according to known classifications. In some embodiments, the known
classifications may be based upon the FDA iCGM special requirements
(e.g., the known classifications may be defined such that the
outputs 904 conform to the FDA iCGM special requirements). For
example, known classifications may include large negative bias,
large positive bias, nominal accuracy, or other labels with respect
to the iCGM criteria. For example, a large negative bias may
indicate that a particular measurement is underestimated,
undervalued, or otherwise too low. A large positive bias may
indicate that a particular measurement is overestimated,
overvalued, or otherwise too high. In some embodiments, these known
classifications may describe the CGM sensor data relative to an
average measurement or range across the sensor data, an average
measurement or range for sensor data associated with a user of the
sensor device, or another reference. In some embodiments, the
system may take calibration measurements periodically (e.g., every
4-6 hours or any other predetermined period of time) and derive a
reference value from the calibration measurements. The system may
thus determine positive and negative bias relative to the reference
value. Finally, when machine learning model 900 receives a
particular glucose measurement as input 902 to, machine learning
model 900 may provide an output 904 including a prediction of
whether the measurement corresponds to a large negative bias, large
positive bias, or nominal accuracy.
[0071] In some embodiments, known classifications may include poor
accuracy, intermediate accuracy, good accuracy, or other labels.
For example, accuracy labels may correspond to wear times,
consistency or inconsistency of readings (e.g., as compared to data
for a particular user, training data as a whole, etc.), or other
indicators of accuracy. In some embodiments, as described above,
the system may take calibration measurements periodically (e.g.,
every 4-6 hours or any other predetermined period of time) and
derive a reference value from the calibration measurements. The
system may determine accuracy levels relative to the reference
value. Accordingly, when machine learning model 900 receives a
particular glucose measurement as input 902 to, machine learning
model 900 may provide an output 904 including a prediction of
whether the measurement corresponds to a poor accuracy,
intermediate accuracy, or good accuracy.
[0072] In some embodiments, machine learning model 900 may receive
a multi-dimensional feature input based on the sensor data. For
example, the inputs 902 received by machine learning model 900 may
comprise features such as an EIS feature, Isig trending feature,
Isig rate of change feature, previous Isig, previous Vcntr, or
other feature inputs. Further description of the above feature
inputs can be found in U.S. patent Ser. No. 16/773,422, entitled
METHODS, SYSTEMS, AND DEVICES FOR CONTINUOUS GLUCOSE MONITORING,
which is herein incorporated by reference in its entirety. In some
embodiments, the outputs 904 of machine learning model 900 may
depend on combinations of the above feature inputs. For example,
certain combinations of Isig, EIS, and Vcntr feature inputs may
cause machine learning model 900 to classify the signals as
outliers (e.g., or large negative bias, large positive bias, poor
accuracy, etc.). In some embodiments, other combinations of Isig,
EIS, and Vcntr feature inputs may cause machine learning model 900
to classify the signals as not outliers (e.g., or nominal accuracy,
intermediate accuracy, good accuracy, etc.). In some embodiments,
the machine learning model may use hit criteria (e.g., as described
above) to classify the multi-dimensional feature input as an
outlier or not an outlier.
[0073] In some embodiments, inputs 902 may comprise sensor data
from a sensor device and reference feedback information 904 (which
is fed back as input to the machine learning model 900) may include
clinical data on erroneous sensor use conditions. For example, the
clinical data may be labeled training data, which may include a
library of error patterns. In some embodiments, the error patterns
may be labeled with corresponding erroneous sensor use conditions.
For example, erroneous sensor use conditions may include
sensitivity loss, loss of signal, signal spiking, environmental
errors (e.g., moisture, heat), or other conditions. Accordingly,
when a particular glucose measurement is provided as input 902 to
machine learning model 900, machine learning model 900 may provide
an output 904 including a prediction of an erroneous sensor use
condition based on a detected error pattern.
[0074] In some embodiments, inputs 902 may further comprise context
information relating to the sensor data. In some embodiments,
context information may include pattern definition. For example,
error patterns may include combinations of patterns of various
sensor input features. In this example, context information may
include information about the various components (e.g., sensor
input features) of the sensor data and how the error pattern
affects these components. In some embodiments, the context
information may include historic information relating to the sensor
data over a time period. For example, such historic information may
include information about behaviors, trends, or patterns of the
sensor data over a certain time period leading up to a detected
error pattern. In some embodiments, behaviors, trends, or patterns
of the sensor data may include downward trends, noisy conditions, a
history of error patterns, or other context information. In some
embodiments, the time period may be several seconds, minutes,
hours, or days, or the time period may be the lifetime of the
sensor device. The context information may be important for
distinguishing between identical error patterns with different
underlying erroneous sensor use conditions. For example, for an
error pattern (e.g., low signal) within a first context (e.g.,
stable history), the system may determine that a first erroneous
sensor use condition (e.g., temporary signal loss) is associated
with the error pattern. In another example, for the same error
pattern (e.g., low signal) within a second context (e.g., downward
trend), the system may determine that a second erroneous sensor use
condition (e.g., sensitivity loss) is associated with the error
pattern. The context information is thus vital to determining the
correct erroneous sensor use condition associated with a given
error pattern and the correct resolution for correcting the
erroneous sensor use condition. In this example, output 904 may be
further based upon the context information relating to the sensor
data.
[0075] In some embodiments, inputs 902 may comprise sensor data
from a sensor device and reference feedback information 904 (which
is fed back as input to the machine learning model 900) may include
data on resolutions associated with various erroneous sensor use
conditions. For example, the system may use retrospective
techniques to apply resolutions to the training data. The system
may determine which resolutions are most effective for resolving
each erroneous sensor use condition. Thus, the training data may be
labeled training data (e.g., error patterns or erroneous sensor use
conditions labeled with effective resolutions). Accordingly, when
particular sensor data is provided as input 902 to machine learning
model 900, machine learning model 900 may provide an output 904
including a prediction of a resolution that is likely to correct
for a detected erroneous sensor use condition.
[0076] While machine learning model 900 is described in relation to
the foregoing examples, it should be understood that the system may
train machine learning model 900 to classify or predict
characteristics or errors of sensor data according to any other
criteria or based on any other inputs. In some embodiments, the
system may utilize outputs from machine learning model 900 to
determine blanking and termination of signals (e.g., as described
below in relation to FIGS. 10 and 11) or to determine resolutions
for erroneous sensor use conditions (e.g., as described below in
relation to FIGS. 10 and 12).
[0077] FIG. 10 shows a flow diagram 1000, in accordance with one or
more embodiments. As shown schematically in FIG. 10, the methods
and systems described herein include: a sensor feature generator
1002, a blood glucose calibrator 1004, a sensor glucose modeler
1006, a conditional blanker and terminator 1008, and an error
detector and corrector 1010. In some embodiments, input data (i.e.,
interstitially measured current (Isig), counter voltage (Vcntr),
electrochemical impedance spectroscopy (EIS), and blood glucose
calibration values (BG)) may pass through the algorithm to be
transformed to sensor glucose values, or SG. The Table below shows
the information input and output from each of the four
components.
TABLE-US-00002 Description of the Information Transfer Information
Component Component Component Component Component Content 1002 1004
1006 1008 1010 Input signals, Isig, Input N/A N/A Input N/A Vcntr,
EIS, BG Base and Output Input Input Input Input Derivative Sensor
Features Requiring No Calibration Base and N/A Output Input Input
Input Derivative Sensor Features Requiring BG Calibration Initial
Estimates of N/A N/A Output Input Output Sensor Glucose Values
Final Estimates of N/A N/A N/A Output N/A Sensor Glucose Values
[0078] In some embodiments, the input data may flow through
components 1002, 1004, 1006, and 1008 (i.e., bypassing component
1010). For example, components 1002, 1004, and 1006 may complete
sensor feature generation, BG calibration, and SG modelling,
respectively, and component 1008 may classify the sensor data and
blank or terminate signals accordingly. In some embodiments,
conditional blanker and terminator 1008 may include or be
associated with machine learning model 900, as shown in FIG. 9. For
example, machine learning model 900 may classify CGM sensor data.
For example, machine learning model 900 may classify the sensor
data as outlier/not outlier, as having large negative bias/large
positive bias/nominal accuracy, or as having poor
accuracy/intermediate accuracy/good accuracy. In some embodiments,
conditional blanker and terminator 1008 may blank the sensor data
(e.g., eschew transmitting sensor data to another device, eschew
displaying sensor data on a display interface) based on the output
from machine learning model 900. For example, if the output from
machine learning model 900 indicates that a measurement from the
sensor data is not an outlier (e.g., does not breach a threshold),
conditional blanker and terminator 1008 may not blank that
measurement. Similarly, if the output from machine learning model
900 indicates that a measurement has intermediate, normal, or good
accuracy, conditional blanker and terminator 1008 may not blank
that particular measurement. In some embodiments, if the output
from machine learning model 900 indicates that a measurement from
the sensor data is an outlier, conditional blanker and terminator
1008 may blank that measurement. Similarly, if the output from
machine learning model 900 indicates that a measurement has a large
bias (e.g., positive or negative) or has poor accuracy, conditional
blanker and terminator 1008 may blank that measurement. In some
embodiments, conditional blanker and terminator 1008 may blank
certain measurements based on any other classifications output by
machine learning model 900.
[0079] In some embodiments, blanking the CGM sensor data (e.g., via
a display interface) may include determining a variable for the CGM
calibration algorithm based on the output from machine learning
model 900 (e.g., as shown in FIG. 7). In some embodiments, the
variable for the CGM calibration algorithm may depend on whether
machine learning model 900 outputs an outlier classification (e.g.,
nominal accuracy, intermediate accuracy, good accuracy, etc.) or a
non-outlier classification (e.g., large positive bias, large
negative bias, poor accuracy, etc.). In some embodiments,
conditional blanker and terminator 1008 may determine whether to
blank the sensor data based on the output from the CGM calibration
algorithm.
[0080] In some embodiments, conditional blanker and terminator 1008
may additionally or alternatively terminate a sensor (e.g., stop
transmitting any sensor data from that sensor and/or stop
displaying any sensor data from that sensor). For example,
conditional blanker and terminator 1008 may monitor measurements
that have been blanked (i.e., outliers). Conditional blanker and
terminator 1008 may track a number of consecutive blanked
measurements (e.g., within a certain time frame). For example,
conditional blanker and terminator 1008 may use an outlier counter
to track blanked measurements and determine when to terminate a
sensor. In some embodiments, when machine learning model 900
classifies a measurement as not an outlier, the system may reset
the outlier counter (e.g., to zero). In some embodiments, when
machine learning model 900 classifies a certain number of
measurements in a row as not outliers, the system may reset the
outlier counter. For example, machine learning model 900 classifies
five measurements as not outliers, the system may reset the outlier
counter. In some embodiments, the system may reset the outlier
counter based on measurements that machine learning model 900
classifies as having nominal accuracy, intermediate accuracy, good
accuracy, etc.
[0081] If conditional blanker and terminator 1008 identifies a
measurement that has been classified as an outlier (e.g., or large
positive bias, large negative bias, poor accuracy, etc.) by machine
learning model 900, the system may increase the outlier counter.
For example, the system may increment the outlier counter by one.
In this example, the outlier counter may continue to increase when
outlier measurements are received. As described above, the system
may reset the outlier counter at any time in response to receiving
one or more measurements classified as non-outliers. The outlier
counter may continue to increase in response to outlier
measurements until a threshold is breached. For example, the
threshold may be predetermined. In some embodiments, the threshold
may dynamically change based on wear time, battery life, or other
factors. In some embodiments, the threshold may have an associated
time limit. For example, in some embodiments, the outlier counter
must breach the threshold within a certain time frame (e.g., 1
hour) in order to terminate the signal (e.g., stop transmitting any
sensor data from that sensor and/or stop displaying any sensor data
from that sensor). In some embodiments, once the system terminates
the signal, conditional blanker and terminator 1008 may alert the
user that the sensor must be replaced (e.g., via a display
interface).
[0082] FIG. 11 shows the schematic 1100 of the sensor feature
generator 1002 of FIG. 10, in accordance with one or more
embodiments. As shown, the consumes the following input signals:
Isig (e.g., 1-minute) 1102, Vcntr (e.g., 5-minute) 1104, and EIS
(e.g., 15-30 minutes) 1106. The Isig signals are collected over 5
minutes to generate a 5-minute Isig signal 1108 and the EIS signal
is validated at 1112. The latter two signals are then used, along
with preprocessing, to generate 5-minute and derivative features
(e.g., 1110, 1116). In some embodiments, measurement frequency of
signals can change depending on the hardware design, e.g., Vcntr
may be measured at 1-minute and EIS may be measured more
frequently, depending on battery life and memory size limitations.
The Table below provides a description of the input signals to
sensor feature generator 1002. Sensor feature generator 1002 will
make these input signals, as well as the signals it generates,
available to blood glucose calibrator 1004, sensor glucose modeler
1006, and conditional blanker and terminator 1008 (e.g., at
1114).
TABLE-US-00003 Description of the Input Signals to Sensor Feature
Generator 1002 Input Output Time Time Signal Description Lapse
Lapse Isig Interstitial Current Signal 1 minute 5 minutes Vcntr
Counter Voltage Signal 5 minutes 5 minutes EIS Electrochemical
Impedance 15 to 5 minutes Spectroscopy Signal 30 minutes
[0083] As shown in FIG. 11, the system may process the Isig, Vcntr,
and EIS signals based on the first time interval (e.g., 5-minute
features 1110). In some embodiments, the packet, including all or
some of these signals, may be used to generate a calibrated SG
value, which is displayed to the user (e.g., via a display
interface). At any point in each interval, the system may blank the
output values provided to the user (e.g., as described above in
relation to FIG. 10) in response to the system classifying a signal
included in the interval as an outlier. In some embodiments, the
outlier counter described above may utilize the first interval to
reset or increase. In other words, the system may reset the outlier
counter or increase the outlier counter based identifying a
measurement included in the interval as an outlier. An aggregation
of packets including blanked signals (e.g., multiple consecutive
five-minute increments) may cause the system to terminate the
sensor (e.g., stop transmitting any sensor data from that sensor
and/or stop displaying any sensor data from that sensor).
[0084] Returning to FIG. 10, error detector and corrector 1010 may
function in parallel with sensor glucose modeler 1006. For example,
error detector and corrector 1010 may take the same inputs as
sensor glucose modeler 1006 (e.g., as shown in the Table above). In
some embodiments, outputs from error detector and corrector 1010
may, at times, feed into conditional blanker and terminator 1008.
For example, if error detector and corrector 1010 detects no error
patterns in the sensor data, the output from sensor glucose modeler
1006 may function as the initial estimates of sensor glucose
values, as input into conditional blanker and terminator 1008. If
error detector and corrector 1010 detects an error pattern in the
sensor data, the system may switch over such that the output from
error detector and corrector 1010 functions as the initial
estimates of sensor glucose values, as input into conditional
blanker and terminator 1008. Thus, while sensor glucose modeler
1006 and error detector and corrector 1010 may process the sensor
data in parallel, the output from sensor glucose modeler 1006 may
be the primary input into conditional blanker and terminator 1008
under circumstances in which no error patterns are detected in the
sensor data and the output from error detector and corrector 1010
may be the primary input into conditional blanker and terminator
1008 under circumstances in which one or more error patterns are
detected in the sensor data. In other words, if error detector and
corrector 1010 detects no error patterns in the signal, flow
diagram 1000 may function as if it were independent of error
detector and corrector 1010.
[0085] In some embodiments, error detector and corrector 1010 may
identify a resolution in response to a detected erroneous sensor
use condition. For example, error detector and corrector 1010 may
identify a resolution that is associated with the detected
erroneous sensor use condition. In some embodiments, the resolution
may be associated with the detected erroneous sensor use condition
via a database, library of patterns, library of resolutions, or by
some other means. In some embodiments, the resolution may be
associated with the detected erroneous sensor use condition via an
output from a machine learning model (e.g., machine learning model
900), as discussed above in relation to FIG. 9. In some
embodiments, error detector and corrector 1010 may implement the
identified resolution or may cause the identified resolution to be
implemented (e.g., by another component). In some embodiments, the
system may implement the identified resolution by manipulating the
sensor glucose data that is displayed on a user device (e.g., via a
display interface). For example, the system (e.g., conditional
blanker and terminator 1008) may blank the sensor glucose data from
the sensor device. In another example, the system may flag a user
of the sensor device (e.g., by generating an alert, informing the
user of the erroneous sensor use condition, requesting an action
from a user, etc.). In some embodiments, the system (e.g., error
detector and corrector 1010) may implement the identified
resolution by manipulating (e.g., processing) the sensor data
directly. For example, error detector and corrector 1010 may adjust
a signal, add a filter to a signal, adjust a filter of a signal,
replace a signal (e.g., with a trending signal, a signal from a
library, etc.), or otherwise manipulate or process the sensor
data.
[0086] In some embodiments, error detector and corrector 1010 may
include or be associated with machine learning model 900, as shown
in FIG. 9. For example, machine learning model 900 may detect error
patterns in the sensor data or identify a resolution in response to
a detected erroneous sensor use condition. In some embodiments,
machine learning model 900 may generate additional predictions
which error detector and corrector 1010 may utilize for identifying
and correcting erroneous sensor use conditions.
[0087] FIG. 12 shows a graph 1200 highlighting error patterns in
sensor data, in accordance with one or more embodiments. As shown
in graph 1200, the system (e.g., error detector and corrector 1010,
as shown in FIG. 10) may identify various error patterns, such as
error pattern 1202 and error pattern 1204. Both error pattern 1202
and error pattern 1204 may demonstrate a dropping off of a signal.
In this example, error detector and corrector 1010 may identify an
underlying erroneous sensor use condition for each error pattern
based on the error pattern, context information of the sensor data
(e.g., as described above), or other information relating to the
sensor data.
[0088] The above-described embodiments of the present disclosure
are presented for purposes of illustration and not of limitation,
and the present disclosure is limited only by the claims which
follow. Furthermore, it should be noted that the features and
limitations described in any one embodiment may be applied to any
other embodiment herein, and flowcharts or examples relating to one
embodiment may be combined with any other embodiment in a suitable
manner, done in different orders, or done in parallel. In addition,
the systems and methods described herein may be performed in real
time. It should also be noted that the systems and/or methods
described above may be applied to, or used in accordance with,
other systems and/or methods.
[0089] The present techniques will be better understood with
reference to the following enumerated embodiments:
1. A method comprising: receiving, at a sensor device, CGM sensor
data; inputting, at the sensor device, the sensor data in a machine
learning model, wherein the machine learning model is trained to
identify outlier measurements based on sensor glucose-dependent
performance against iCGM criteria using training data comprising
clinical data on iCGM performance; receiving, at the sensor device,
an output from the machine learning model indicating that the
sensor data corresponds to an outlier measurement of the outlier
measurements; and blanking the sensor data based on the output. 2.
The method of embodiment 1, wherein the machine learning model is
trained using a set of training sensor data that is labeled
according to known classifications, the known classifications
comprising a large negative bias, large positive bias, or nominal
accuracy. 3. The method of any of embodiments 1-2, wherein the
machine learning model is trained using a set of training sensor
data that is labeled according to known classifications, the known
classifications comprising poor accuracy, intermediate accuracy, or
good accuracy. 4. The method of any of embodiments 1-3, wherein the
sensor data comprises a current signal, a voltage signal, or
impedance spectroscopy signals. 5. The method of embodiment 4,
wherein inputting the sensor data in the machine learning model
comprises generating a multi-dimensional feature input based on the
sensor data. 6. The method of any of embodiments 1-5, wherein
blanking the sensor data based on the output further comprises:
determining a variable for a CGM calibration algorithm based on the
output; and determining whether to blank the sensor data based on
the output from the from the machine learning model. 7. The method
of any of embodiments 1-6, wherein the sensor data is received in
first time intervals. 8. The method of any of embodiments 1-7,
further comprising: resetting an outlier counter based on
determining that a first sensor datapoint does not correspond to an
outlier measurement; causing the outlier counter to be increased
based on determining that a second sensor datapoint corresponds to
an outlier measurement; comparing the outlier counter to a
threshold; and terminating the sensor device based on determining
that the outlier counter has breached the threshold. 9. A method
comprising: receiving, at a sensor device, CGM sensor data;
inputting, at the sensor device the sensor data into a machine
learning model, wherein the machine learning model is trained to
identify erroneous sensor use conditions based sensor data error
patterns using training data comprising clinical data on erroneous
sensor use conditions; and receiving, at the sensor device, an
output from the machine learning model indicating an erroneous
sensor use condition based on an error pattern identified in the
sensor data. 10. The method of embodiment 9, further comprising
blanking the sensor data based on the output. 11. The method of any
of embodiments 9-10, further comprising flagging a user of the
sensor device, on a display interface, based on the output. 12. The
method of any of embodiments 9-11, further comprising identifying a
resolution associated with the erroneous sensor use condition
identified in the sensor data. 13. The method of embodiment 12,
further comprising implementing the resolution by manipulating the
sensor data, on a display interface, of the sensor device. 14. The
method of embodiment 12, further comprising implementing the
resolution by manipulating the sensor data received from the sensor
device. 15. The method of any of embodiments 9-14, further
comprising: receiving input indicating context information relating
to the sensor data; inputting the context information relating to
the sensor data into the machine learning model; and wherein the
output from the machine learning model indicating an erroneous
sensor use condition is further based on the context information
relating to the sensor data. 16. The method of embodiment 15,
wherein the context information relating to the sensor data
includes historic information relating to the sensor data over a
time period. 17. The method of claim 9, further comprising training
the machine learning model to identify the erroneous sensor use
conditions based on the sensor data error patterns using the
training data comprising the clinical data on the erroneous sensor
use conditions. 18. A tangible, non-transitory, machine-readable
medium storing instructions that, when executed by a data
processing apparatus, cause the data processing apparatus to
perform operations comprising those of any of embodiments 1-17. 19.
A system comprising: one or more processors; and memory storing
instructions that, when executed by the processors, cause the
processors to effectuate operations comprising those of any of
embodiments 1-17. 20. A system comprising means for performing any
of embodiments 1-17.
* * * * *