U.S. patent application number 16/226069 was filed with the patent office on 2019-05-02 for computer implemented system and method for assessing a neuropsychiatric condition of a human subject.
The applicant listed for this patent is CHILDREN'S HOSPITAL MEDICAL CENTER. Invention is credited to Bruce Aronow, Tracy A. Glauser, John P. Pestian.
Application Number | 20190131015 16/226069 |
Document ID | / |
Family ID | 43032511 |
Filed Date | 2019-05-02 |
United States Patent
Application |
20190131015 |
Kind Code |
A1 |
Pestian; John P. ; et
al. |
May 2, 2019 |
COMPUTER IMPLEMENTED SYSTEM AND METHOD FOR ASSESSING A
NEUROPSYCHIATRIC CONDITION OF A HUMAN SUBJECT
Abstract
The disclosure provides methods for assessing a neuropsychiatric
condition of a human subject by combining the subject's biomarker
data and thought marker data into a quantitative assessment of the
subject's neuropsychiatric condition.
Inventors: |
Pestian; John P.; (Loveland,
OH) ; Glauser; Tracy A.; (Cincinnati, OH) ;
Aronow; Bruce; (Cincinnati, OH) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
CHILDREN'S HOSPITAL MEDICAL CENTER |
Cincinnati |
OH |
US |
|
|
Family ID: |
43032511 |
Appl. No.: |
16/226069 |
Filed: |
December 19, 2018 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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13266272 |
Oct 26, 2011 |
10204707 |
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PCT/US10/32513 |
Apr 27, 2010 |
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16226069 |
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61214707 |
Apr 27, 2009 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G16Z 99/00 20190201;
G06F 19/00 20130101; G16H 50/30 20180101; G06N 20/00 20190101; G16H
50/20 20180101 |
International
Class: |
G16H 50/20 20060101
G16H050/20; G06N 20/00 20060101 G06N020/00; G16H 50/30 20060101
G16H050/30 |
Claims
1-50. (canceled)
51. A method for assessing a neuropsychiatric condition of a human
subject, the method comprising determining one or more
neuropsychiatric condition associated biological markers in a
biological sample from the subject to provide biomarker data for
the subject, the markers determined by a method comprising one or
more of a polymerase chain reaction (PCR), a reverse transcription
PCR reaction (RT-PCR), mass spectroscopy (MS), high pressure liquid
chromatography (HPLC), LC-MS, DNA sequencing, and an enzyme-linked,
bead based, or sandwich immunoassay, generating, using one or more
computer processors, a biomarker score based on the strength of the
association of the biomarker data with the neuropsychiatric
condition, obtaining thought-marker data from the subject, the
thought marker data including one or more of the subject's recorded
thoughts, spoken words, transcribed speech, and writings,
generating, using one or more computer processors, a thought-marker
score based on the strength of the association of the thought
marker data with the neuropsychiatric condition by a method
comprising the steps of determining a correlation between (i) the
thought marker data of the subject and (ii) a corpus of thought
data pertaining to the neuropsychiatric condition, the correlation
determined using a machine learning method implementing a
classification algorithm selected from the group consisting of
decision trees, classification rules, function models, and
instance-based learner methods, the machine learning method
comprising extracting and quantifying relevant content features of
the thought marker data and creating a heterogeneous,
multidimensional feature space, normalizing the feature values, and
generating the thought-marker score based upon the strength of the
correlation, and generating a neuropsychiatric condition score
based on the biomarker score and the thought-marker score, thereby
providing a quantitative assessment of the neuropsychiatric
condition of the subject.
52. The method of claim 51, wherein the neuropsychiatric condition
is suicide attempt risk.
53. The method of claim 52, wherein the step of determining the one
or more neuropsychiatric condition associated biological markers
includes a step of determining a level of a hydroxyindoleacetic
acid (5HIAA).
54. The method of claim 52, wherein the step of determining the one
or more neuropsychiatric condition associated biological markers
includes a step of determining the presence of the S and L alleles
of the 5' upstream regulatory region of the serotonin transporter
gene (5-HTTLPR).
55. The method of claim 52, wherein the step of determining the one
or more neuropsychiatric condition associated biological markers
includes a step of determining the presence of one or more single
nucleotide polymorphisms taken from a group consisting of A218C of
the TPH1 gene, A779C of the TPH1 gene, and A59G of the SLC6A3
gene.
56. The method of claim 52, wherein the step of determining the one
or more neuropsychiatric condition associated biological markers
includes a step of determining an mRNA level of 5-HT(2A) mRNA.
57. The method of claim 52, wherein the step of determining the one
or more neuropsychiatric condition associated biological markers
includes a step of determining a level of one or more cytokines
taken from a group consisting of IL-6, IL-2, IFN-.gamma., IL-4 and
TGF-.beta.1.
58. The method of claim 52, wherein the step of determining the one
or more neuropsychiatric condition associated biological markers
includes a step of determining a level of serotonin (5-HT).
59. The method of claim 51, further comprising receiving clinical
data of the subject associated with the neuropsychiatric
condition.
60. The method of claim 59, wherein the clinical data includes one
or more of data pertaining to a level of interpersonal discord,
data pertaining to a presence of a mood disorder, data pertaining
to a history of substance use, data pertaining to a history of
impulsive aggression, data pertaining to a family history of
suicidal behavior, data pertaining to access to weapons such as
firearms, and data pertaining to recent psychosocial stressors.
61. A method for assessing a suicide attempt risk of a human
subject, the method comprising determining one or more suicide risk
associated biological markers in a biological sample obtained from
the subject by a method comprising one or more of a polymerase
chain reaction (PCR), a reverse transcription PCR reaction
(RT-PCR), mass spectroscopy (MS), high pressure liquid
chromatography (HPLC), LC-MS, DNA sequencing, and an enzyme-linked,
bead based, or sandwich immunoassay, receiving, using one or more
computer processors, one or more thought markers of the subject,
the one or more thought markers including one or more of the
subject's recorded thoughts, spoken words, transcribed speech, and
writings; executing, using one or more computer processors, a first
query and transmitting the first query to a database to obtain a
plurality of suicide notes associated with prior completions of
suicides, the one or more processors being communicatively coupled
to the database using one or more communications networks;
comparing, using a machine learning method, the one or more thought
markers and the obtained plurality of suicide notes to determine a
correlation between (a) the one or more thought markers of the
subject and (b) the obtained plurality of suicide notes, and
generating a thought-marker score based upon a strength of the
correlation, generating a suicide attempt risk score based on a
combination of the biomarker score and the thought-marker score;
and generating, using the suicide attempt risk score, an assessment
of the subject.
62. The method of claim 61, wherein the machine learning method
comprises an implementation of a classification algorithm including
at least one of a decision tree, a classification rule, a function
model, an instance-based learner method, and combinations
thereof.
63. The method of claim 61, wherein the machine learning method
comprises extracting and quantifying relevant content features of
the one or more thought markers and generating, based on the
extracted and quantified content features, a heterogeneous,
multidimensional feature space containing a plurality of feature
values corresponding to the extracted and quantified content
features.
64. The method of claim 63, further comprising normalizing the
generated feature values and generating, using the normalized
generated feature values, a thought-marker score based upon a
strength of the correlation between (a) and (b).
65. The method of claim 64, further comprising normalizing, using
one or more computer processors, the one or more determined suicide
risk associated biological markers and generating a normalized
score for each marker based on the strength of marker's association
with suicide risk.
66. The method of claim 65, further comprising generating, using
one or more computer processors, a biomarker score based on a sum
of individual normalized scores for the one or more determined
suicide risk associated biological markers.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims the benefit of U.S. Provisional App.
Ser. No. 61/214,707, filed Apr. 27, 2009, the entire disclosure of
which is incorporated herein by reference.
BACKGROUND
[0002] There is a need for more accurate assessment of human
subject's neuropsychiatric conditions so that the human subject may
be better treated for such conditions by their caregivers. For
example, there are needs for better assessment of a suicide risk in
an individual, there are needs for better assessment of end-of-life
treatment care for terminally ill patients, there are needs for
better assessment and treatment of schizophrenic patients, there
are needs for better assessment and handling of a criminal act (or
repeat criminal act) attempt risk for an individual, there are
needs for better assessment and treatment of other neuropsychiatric
conditions, and there are needs for better assessment and handling
of those feigning neuropsychiatric conditions.
[0003] With respect to suicide risk, it is estimated that each year
800,000 people die by suicide worldwide. In the United States
alone, eighty people kill themselves each day, twelve under the age
25. Experts estimate the total life time costs of suicide to be $33
billion. The Centers for Disease Control and Prevention, however,
notes that approximately 15% of all high-school students have
developed a serious plan to attempt suicide, 9% have attempted
suicide, and nearly 3% have required medical attention due to a
suicide attempt. In an average year, a typical pediatric emergency
department evaluates at least 2,000 patients exhibiting suicidal
behavior. A challenge for those who care for suicide attempters may
be assessing the likelihood of another serious suicide attempt,
which may be lethal.
[0004] With respect to end-of-life treatment and care of terminally
ill subjects, One option is to support a clinical atmosphere that
understands when death is certain, and knows when to shift from
life saving medical care to preparing for the inevitable, death. In
the later case, establishing expectations and providing specialized
end-of-life care becomes the norm. With children, especially,
understanding the dying child's concerns can be difficult. These
children may be anxious about pain and discomfort, they may
struggle with what will happen to them when they die, or they may
worry about making family members sad, they may feel alone, stupid,
or angry. As care providers understand the dying child's concerns
they can better provide personalized care to the child and
family.
SUMMARY
[0005] A method for assessing a neuropsychiatric condition (such
as, but not limited to, a risk that a subject may attempt to commit
suicide or repeat an attempt to commit suicide, a risk that
terminally ill patient is not being care-for or treated according
to the patient's true wishes, a risk that a subject may perform or
repeat a criminal act and/or a harmful act, a risk of the subject
having a psychiatric illness, and/or a risk of a subject feigning a
psychiatric illness) may be provided. Such method may be operating
from one or more memory devices including computer-readable
instructions configured to instruct a computerized system to
perform the method, and the method may be operating on a
computerized system including one or more computer servers (or
other available devices) accessible over a computer network such as
the Internet or over some other data network. The method may
include a plurality of steps. A step may include receiving
biomarker data associated from an analysis of the subject's
biological sample and a step of receiving thought-marker data
obtained pertaining to one or more of the subject's recorded
thoughts, spoken words, transcribed speech, and writings. A step
may include generating a biomarker score associated with the
neuropsychiatric condition from the biomarker data. A step may
include generating a thought-marker score associated with the
neuropsychiatric condition from the thought-marker data. And a step
may involve calculating a neuropsychiatric condition score based,
at least in part, upon the biomarker score and the thought-marker
score. As will be appreciated, many of these steps do not
necessarily need to be performed in the order provided and some of
the steps may be combined into a single step or operation.
[0006] In an embodiment, the step of generating the biomarker score
may include a step of assessing a level of at least a cytokine, a
metabolite, a polymorphism, a genotype, a polypeptide, and an mRNA
of the human subject. For example, the step of generating the
biomarker score may include a step of assessing a level of a
hydroxyindoleaceticacid (5HIAA).
[0007] In an embodiment, the step of generating a thought-marker
score includes a step of determining a correlation between (a) the
human subject's recorded thoughts, spoken words, transcribed speech
and/or writings; and (b) a corpus of thought data collected
pertaining, at least in part, to the neuropsychiatric condition.
Further, this correlation may be determined, at least in part,
utilizing natural language processing and/or machine learning
algorithms.
[0008] In an embodiment, the method may further include a step of
receiving clinical data of the subject associated with the
neuropsychiatric condition; may include a step of generating a
clinical data score from the clinical data; and the step of
calculating in neuropsychiatric condition score may be based, at
least in further part, upon the clinical data score. Further, the
clinical data of the subject associated with the neuropsychiatric
condition may include at least a portion of medical patient record
data associated with the subject; may include demographic data
associated with the subject; and/or may include interview and/or
survey data obtained from the subject. With this embodiment, it is
possible that the step of calculating a neuropsychiatric condition
score may include steps of (a) normalizing the biomarker score, (b)
normalizing the thought-marker score, (c) normalizing the clinical
data score and (d) calculating a mean of at least the normalized
biomarker, thought marker and clinical data scores. Further, the
normalizing steps normalize between a numerical scale of 0.0 to 1.0
and/or a scale of 0 and 10.sup.N, wherein N is an integer. Further,
the step of generating a clinical data score may include a step of
calculating a percentage of risks associated with the
neuropsychiatric condition from the subject compared to a
predetermined set of risks associated with the neuropsychiatric
condition.
[0009] In an embodiment, the step of generating a biomarker score
includes a step of calculating a composite score related to two or
more biological markers associated with the neuropsychiatric
condition from the biomarker data.
[0010] In an embodiment, the step of calculating a neuropsychiatric
condition score includes steps of (a) normalizing the biomarker
score, (b) normalizing the thought marker score and (c) calculating
a mean of at least the normalized biomarker and the thought marker
scores.
[0011] In an embodiment, the method further includes a step of
automatically recommending, based upon the calculated
neuropsychiatric condition score, a subject's treatment regimen, a
subject's counseling session, a subject's intervention program
and/or a subject's care program.
BRIEF DESCRIPTION OF THE DRAWINGS
[0012] FIG. 1 is a flow diagram depicting an exemplary embodiment
of the present invention.
[0013] FIG. 2 is a flow diagram depicting another exemplary
embodiment of the present invention.
[0014] FIG. 3 is a flow diagram depicting another exemplary
embodiment of the present invention.
[0015] FIG. 4 is a diagram depicting yet another exemplary
embodiment of the present invention.
[0016] FIG. 5 is a diagram depicting another exemplary embodiment
of the present invention.
[0017] FIG. 6. is a flow diagram depicting another exemplary
embodiment of the present invention.
[0018] FIG. 7 is a flow diagram depicting another exemplary
embodiment of the present invention.
DETAILED DESCRIPTION
[0019] In the following detailed description, reference is made to
the accompanying drawings, which form a part hereof. In the
drawings, similar symbols typically identify similar components,
unless context dictates otherwise. The illustrative embodiments
described in the detailed description, drawings, and potential
points of novelty are not meant to be limiting. Other embodiments
may be utilized, and other changes may be made, without departing
from the spirit or scope of the subject matter presented here. It
will be readily understood that the aspects of the present
disclosure, as generally described herein, and illustrated in the
Figures, may be arranged, substituted, combined, and designed in a
wide variety of different configurations, all of which are
explicitly contemplated and make part of this disclosure.
[0020] This disclosure is drawn to methods and systems related to
assessing neuropsychiatric conditions of human subjects. A first
example implementation involves assessing suicide risks of human
subjects. This first example implementation will be described in
detail; and, as will be appreciated by those of ordinary skill,
other example implementations described and/or contemplated herein
(such as, for example, the end-of-life care assessment and the
schizophrenia assessment) will be readily implemented using the
methodologies, components and systems of this first example
embodiment.
I. Suicide Risk Impementation
[0021] As depicted in FIG. 1, this embodiment may include an
operation 10 of gathering biological sample(s) from the human
subject. This embodiment also may include an operation 12 of
gathering clinical risk factor(s) such as the human subject's
suicide attempt history, suicidal intent, psychological health,
interpersonal relationships, behavior, family history of suicide,
access to weapons, psychosocial stressors and/or other similar
clinical risk factors. This embodiment further may include an
operation 14 of gathering thought marker(s) related to the human
subject's thoughts, spoken words, transcribed speech, writings
and/or other similar thought markers. This embodiment may further
include an operation 16 of analyzing the biological sample to
identify suicide risk associated biological marker(s). These
suicide risk associated biological marker(s) may relate to
cytokine(s), metabolite(s), polymorphism(s), genotype(s),
polypeptide(s), mRNA of the human subject and/or other similar
biological marker(s).
[0022] This exemplary embodiment may include analyzing the
biological marker(s) by using known analysis methods including,
without limitation, those discussed below. This analysis may result
in the determination of a biological marker score. This embodiment
may also include a computer-implemented operation 18 of analyzing
the clinical risk factor(s) by using known analysis methods
including, without limitation, those discussed below. This analysis
may result in the determination of a clinical risk factor score.
This embodiment may further include a computer-implemented
operation 20 of comparing the thought marker(s) to suicide notes
(such as those developed from a predetermined suicide note
database, for example). This comparison may result in the
determination of a thought marker score. This embodiment may
further include the computer-implemented operation 22 of
calculating a suicide risk score based, at least in part, on the
biological marker score(s), the clinical risk factors score(s)
and/or the thought marker score(s). The calculation of the suicide
risk score may be implemented in many ways, including utilizing
averages, weighted formulas, normalization of scores, regressions
and/or other similar calculation implementations. This suicide risk
score may be provided to the doctor(s), clinician(s), nurse(s),
parent(s) or others and may be used in the determination of whether
further treatment, counseling, observation or intervention is
appropriate (e.g., to help prevent a next or even initial suicide
attempt by the patient if the suicide risk score is at or above a
certain level).
[0023] In another exemplary embodiment, the method may include
analyzing biological marker(s) to determine a composite biological
marker score. The composite biological marker score may be a score
including two or more biological marker scores, each related to a
suicide risk associated biological marker. For example, a composite
score could be determined based on a cytokine biological marker
score, a polymorphism biological marker score and a genotype
biological marker score. In that example, the composite biological
marker score may be determined by a computerized system in many
ways, including utilizing averages, weighted formulas,
normalization of scores, regressions and/or other similar
calculation implementations. In yet another embodiment, the
composite biological marker score may be a score including two or
more biological marker scores, each biological marker scores
related to the same type of suicide risk associated biological
marker. For example, a composite score could be determined based on
a first metabolite biological marker score and a second metabolite
biological marker score.
[0024] Another exemplary embodiment, as depicted in FIG. 2, may
include a method of assessing a suicide risk (initial risk or
follow-up risk) of a human subject. This embodiment may include an
operation 24 of gathering biological sample(s) from the human
subject. This embodiment further may include the operation 26 of
gathering thought marker(s) related to the human subject's
thoughts, spoken words, transcribed speech, writings and/or other
similar thought markers. In this embodiment, the biological sample
may include suicide risk associated biological marker(s). These
suicide risk associated biological marker(s) may relate to
cytokine(s), metabolite(s), polymorphism(s), genotype(s),
polypeptide(s), mRNA of the human subject and/or other similar
biological marker(s).
[0025] This exemplary embodiment may include an operation 28 of
analyzing the biological marker(s) by using known analysis methods
including, without limitation, those discussed below. This analysis
may result in the determination of a biological marker score. This
embodiment may further include a computer-implemented operation 30
of comparing the thought marker(s) to suicide notes (such as those
developed from a predetermined suicide note database, or a corpus
of suicide note language, for example). This comparison may result
in the determination of a thought marker score. This embodiment may
further include a computer-implemented operation 32 of calculating
a suicide risk score based, at least in part, on the biological
marker score(s) and/or the thought marker score(s). The calculation
of the suicide risk score may be implemented in many ways,
including utilizing averages, weighted formulas, normalization of
scores, regressions and/or other similar calculation
implementations.
[0026] In one embodiment, the method may include a tool utilized by
a physician in evaluating potential treatment regimens for a
patient who has exhibited at least one suicidal attribute such as,
but not limited to, a suicide attempt.
[0027] In another exemplary embodiment as depicted in FIG. 3, the
invention may include a computer-readable medium having
instructions configured to perform computer-implemented operations.
These operations may include an operation 34 of receiving
biological marker score(s), an operation 36 of receiving clinical
risk factor(s) and an operation 38 of receiving thought marker(s).
The biological marker score(s) may be related to suicide risk
associated biological marker(s) in biological sample(s) of a human
subject. The suicide risk associated biological marker(s) may be
related to cytokine(s), metabolite(s), polymorphism(s),
genotype(s), polypeptide(s), mRNA of the human subject and/or other
similar biological marker(s). The clinical risk factor(s) may be
related to the human subject's suicide attempt history, suicidal
intent, psychological health, interpersonal relationships,
behavior, family history of suicide, access to weapons,
psychosocial stressors and/or other similar clinical risk factors.
The thought marker(s) may be related to the human subject's
thoughts, spoken words, transcribed, writings and/or other similar
thought markers.
[0028] The computer readable instructions in this embodiment may
also include instructions for performing the operation 40 of
analyzing the clinical risk factor(s) by using known analysis
methods including, without limitation, those discussed below. This
analysis may result in the determination of a clinical risk factor
score. The computer readable instructions in this embodiment may
further include instructions for performing the operation 42 of
comparing the thought marker(s) to suicide notes (such as those
found in a predetermined suicide note database, for example). This
comparison may result in the determination of a thought marker
score. The computer readable instructions in this embodiment may
further include instructions for performing the operation of
calculating a suicide risk score based, at least in part, on the
biological marker score(s), the clinical risk factors score(s)
and/or the thought marker score(s). The calculation of the suicide
risk score may be implemented in many ways, including utilizing
averages, weighted formulas, normalization of scores, regressions
and/or other similar calculation implementations.
[0029] FIG. 4 provides an exemplary system for assessing a suicide
risk of a human subject. In this embodiment, users at one or more
locations (for example, locations 46a through 46n, where n
corresponds to any number) may communicate with biological marker
database(s) 48 and thought marker database(s) 50. This
communication may be implemented through any network connection 52
(such as the Internet or an intranet, for example). Locations may
include medical facilities, hospitals, research facilities,
laboratories, blood testing centers and other similar locations.
Users may transmit or request and receive data to the biological
marker database(s) and thought marker database(s). The biological
marker database(s) and thought marker database(s) may also be in
communication with a suicide risk assessment inference engine 54.
The suicide risk assessment inference engine may receive data from
the biological marker database(s) 48 and thought marker database(s)
50 and output a suicide risk score based, at least in part on the
data and one or more predefined rule sets. The users at the one or
more locations may receive the suicide risk score from the suicide
risk assessment inference engine 54.
[0030] In yet another embodiment, a system for assessing a suicide
risk of a human subject may be provided. This system may include a
computer system, server system(s) in communication with the
computer system and a suicide risk interface (including a graphical
user interface) stored on the server system(s) and accessible by
the computer system. The suicide risk interface may provide suicide
risk information related to suicide risk associated biological
marker(s), clinical risk factor(s) and thought marker(s). In one
embodiment, the system may generate a suicide risk score based, at
least in part, on the suicide risk associated biological marker(s),
the clinical risk factor(s) and/or the thought marker(s). In one
embodiment, the suicide risk score may be a numerical value. In
another embodiment, the numerical value may be a numerical value
that is relative to the numerical value of other human
subjects.
[0031] In another embodiment, the suicide risk interface may
generate a suicide risk quartile based, at least in part, on
suicide risk associated biological marker(s), clinical risk
factor(s) and thought marker(s), where the suicide risk quartile
may be a quartile that is relative to the quartile of other human
subjects.
[0032] In yet another embodiment, the suicide risk interface may
generate a biological marker score based, at least in part, on
suicide risk associated biological marker(s), a clinical risk
factor score based, at least in part, on the clinical risk
factor(s), and a thought marker score based, at least in part, on
thought marker(s).
[0033] In another embodiment, the suicide risk interface may
normalize the biological marker score(s), the clinical risk factor
score(s) and/or the thought marker score(s). This normalization may
generate normalized biological marker score(s), normalized clinical
risk factor score(s) and/or normalized thought marker score(s). In
this embodiment, the suicide risk interface may generate a suicide
risk score based, at least in part, on the biological marker
score(s), the clinical risk factor score(s), the thought marker
score(s), the normalized biological marker score(s), the normalized
clinical risk factor score(s) and the normalized thought marker
score(s).
I.A Biological Analysis
[0034] An exemplary embodiment of the present disclosure may
provide systems and methods of assessing a suicide risk in a human
subject involving gathering at least one biological sample and
analyzing at least one suicide risk associated biological marker in
the sample to determine a biological marker score.
[0035] A "biological sample" may include a sample collected from a
subject including, but not limited to, tissues, cells, mucosa,
fluid, scrapings, hairs, cell lysates, blood, plasma, serum, and
secretions. Biological samples such as blood samples may be
obtained by any method known to one skilled in the art.
[0036] A "biological marker" may include any physiological
indicator such as, but not limited to, the genotype of the subject
at a particular loci such as a gene, SNP, or portion of a gene,
polymorphism, mRNA, cytokine, metabolite, peptide, polypeptide,
hormone, neurotransmitter, or cell type. Any means of evaluating a
biological marker known in the art may be utilized in the current
methods. A "suicide risk associated" biological marker may include
a physiological indicator that has been linked to an abnormal
frequency of suicide attempts or suicide completion. Such an
abnormal risk may include an elevated frequency of suicide attempts
or suicide completion as compared to a healthy population or
population of subjects at risk for suicide attempts or suicide
completion or a decreased frequency of suicide attempts or suicide
completion within a population of subjects at risk for suicide
attempts or suicide completion. Exemplary populations of subjects
at risk for suicide attempts or suicide completion may include, but
are not limited to, subjects who have already made at least one
suicide attempt or who have been diagnosed with a disease or
condition affiliated with an elevated frequency of suicide attempts
or suicide completion, such as schizophrenia.
[0037] Suicide risk associated genotypes may include, but are not
limited to, suicide risk associated SNPs and allelic variations
larger than a single nucleotide within the coding region of a gene,
the exon-intron boundaries, or the 5' upstream regulatory region of
a gene linked to an abnormal frequency of suicide attempts or
completions. It is contemplated that suicide risk associated
genotypes may include, but are not limited to, the S and L alleles
of the 5' upstream regulatory region of the serotonin transporter
gene (5-HTTLPR) (Weizman, 2000 "Serotonin transporter polymorphism
and response to SSRIs in major depression and relevance to anxiety
disorders and substance abuse", Pharmacogenomics, 1:335-341; herein
incorporated by reference in its entirety).
[0038] A suicide risk associated SNP may include, but is not
limited to, a single nucleotide polymorphism (SNP) for which at
least one variant has been linked to an abnormal frequency of
suicide attempts or completions. It is contemplated that suicide
risk associated SNP's may include, but are not limited to, A218C
and A779C of the TPH1 gene and A59G of the SLC6A3 gene (Bondy et al
(2006) "Genetics of Suicide", Molecular Psychiatry, 11(4) 336-351
and U.S. 2007/0065821, herein incorporated by reference in their
entirety).
[0039] Suicide risk associated mRNAs may include, but are not
limited to, altered mRNA levels of a gene linked to an abnormal
frequency of suicide attempts or completions. It is contemplated
that suicide risk associated mRNAs may include, but are not limited
to, the 5-HT(2A) mRNA in the prefrontal cortex and hippocampus
(Pandey (2002) "Higher Expression of serotonin 5-HT(2A) receptors
in the postmortem brains of teenage suicide victims" American J.
Psychiatry 159:419-429, herein incorporated by reference in it's
entirety). Suicide risk associated polypeptides may be polypeptides
linked to an abnormal frequency of suicide attempts or
completions.
[0040] Suicide risk associated cytokines may include, but are not
limited to, cytokines linked to an abnormal frequency of suicide
attempts or completions. It is contemplated that suicide risk
associated cytokines may include, but are not limited to, IL-6,
IL-2, IFN-.gamma., IL-4 and TGF-.beta.1. See for example Shaffer et
al (1996) "Psychiatric diagnosis in child and adolescent suicide",
Arch Gen Psychiatry 53:339-348 and Kim et al (2007) "Differences in
cytokines between non-suicidal patients and suicidal patients in
major depression", Prog Neuropsychopharmacol Biol Psychiatry,
32:356-61, herein incorporated by reference in their entirety).
Increased IL-6 production may be correlated with decreased risk of
suicide attempt or completion. Decreased IL-2 may be correlated
with increased risk of suicide attempt or completion. A shift in
the ratio of Th1 and Th2 cell types toward the Th1 cell types may
be associated with decreased risk of suicide attempt or
completion.
[0041] Suicide risk associated neurotransmitters may include, but
are not limited to, neurotransmitters linked to an abnormal
frequency of suicide attempts or completions. It is contemplated
that suicide risk associated neurotransmitters may include, but are
not limited to, serotonin (5-HT). See for example Pandey (1997)
"Protein kinase C in the post mortem brain of teenage suicide
victims", Neurosci Lett 228:111-114 and Samuelsson (2006) "CSF
5-H1AA, suicide intent and hopelessness in the prediction of early
suicide in male high risk suicide attempters" Acta Psychiatr Scanda
113:44-47, herein incorporated by reference in their entirety.
[0042] Suicide risk associated metabolites may include, but are not
limited to, metabolites either directly linked to an abnormal
frequency of suicide attempts or completions or a metabolite of a
biological compound linked to an abnormal frequency of suicide
attempts or completions. It is contemplated that suicide risk
associated metabolites may include, but are not limited to,
5-hydroxyindoleaceticacid (5HIAA), a metabolite of serotonin. Low
5HIAA levels may be linked to elevated risk of suicide attempt or
suicide completion. See Nordstrom (1994) "CSF 5-HIAA predicts
suicide risk after attempted suicide", Suicide Life Threat Behav
24:1-9, herein incorporated by reference in its entirety. The
number of different metabolites in humans is unknown but range from
approximately 2000 to approximately 20,000 compared with
significantly higher numbers of proteins and genes (Claudino et al
(2007) "Metabolomics: available results, current research projects
in breast cancer, and future applications", J. Clinical Oncology
25:2840-2846, herein incorporated by reference in its entirety.
These small molecule metabolites may be found in biological samples
such as serum or urine. Mass spectroscopy may be used to analyze an
individual metabolite or collection of metabolites. See, for
example, Wu et al, (2008), "High-throughput tissue extraction
protocol for NMR and MS-based metabolomics", Analytical
Biochemistry 372:204-212 and Yee et al (2002) "An NMR Approach to
Structural Proteomics", PNAS 99:1825-30, herein incorporated by
reference in their entirety.
[0043] Any method of analyzing a biological marker known in the art
may be utilized in the present methods. Methods of analyzing
suicide risk associated biological markers may include, but are not
limited to, RT-PCR array profiling such as the Human Th1-Th2-Th3
PCR Array (SABiosciences), DNA microarrays, immunogenic methods,
mass spectroscopy, HPLC, NMR, DNA sequencing, genotyping, PCR,
reverse transcription-PCR, real-time PCR, MALDI-TOF, HPLC, gas
chromatography mass spectrometry (GC-MS), liquid chromatography
mass spectrometry (LC-MS), Fourier transform mass spectrometry
(FT-MS), electron paramagnetic resonance (EPR) spectrometry, atomic
force microscopy, and Raman spectroscopy, solid phase ELISA, fluid
phase multi-analyte analysis, fluorescent bead based immunoassay,
sandwich based immunoassays, and expression analysis (see for
example Domon et al (2006) "Mass Spectrometry and Protein Analysis,
Science 312:212-217; Walker (2003) Protein Protocols Handbook,
2.sup.nd ed, Humana Press; and Walker (2005) Proteomics Protocols
Handbook, Humana Press; Winning et al (2007) "Quantitative Analysis
of NMR Spectra with Chemometrics", Journal of Magnetic Resonance
190:26-32; Bowtell & Sambrook (2003) DNA Microarrays Cold
Spring Harbor Laboratory Press; herein incorporated by reference in
their entirety.)
[0044] Different methods of analyzing suicide risk associated
biological markers may generate different data types. For instance,
mass spectroscopy may generate a mass/charge ratio while SNP
genotyping may indicate the presence or absence of a particular
nucleotide at a specified residue. Analysis of other biological
markers may yield data about the concentration of the biomarker,
relative concentration data (such as in gene expression analysis),
or a detectable v. non-detectable indication. The raw data obtained
for each biomarker may be normalized before information about a
particular biomarker is incorporated in the biological marker
score.
[0045] The following examples are offered by way of illustration
and not limitation.
EXPERIMENTAL
Example 1. Biological Sample Collection
[0046] Whole blood samples are collected from a human subject using
clinically acceptable blood collection methods. Two aliquots of 8.5
ml whole blood are drawn from the subject. One aliquot is
centrifuged to separate cells and sera. Serum samples (200 .mu.l)
are utilized in NMR and mass spectrometry analysis or cytokine
analysis. An additional sample in a purple-top (EDTA containing)
tube is utilized in molecular genetic analysis. (Additional blood
samples are obtained and analyzed if the patient becomes suicidal
after the initial evaluation.)
Example 2. Blood Sample Preparation for NMR Studies
[0047] Blood samples for Nuclear Magnetic Resonance (NMR) studies
are thawed. 400 .mu.l saline (0.9% NaCl in 10% D.sub.2O (deuterium
oxide) is mixed with the blood sample. The samples are centrifuged
at 13400 g for 5 minutes prior to NMR analysis. The blood is
prepared according to NMR and clinical standards.
Example 3. NMR Data Collection and Analysis
[0048] NMR data is collected using a Bruker US2 Avance II NMR
spectrometer (Bruker Biospin, Rheinstettin, Germany) operating at
850 MHz .sup.1H frequency and 298K. Data is zero-filled by a factor
of two and exponentially weighted by 0.3 Hz of line broadening
prior to Fourier transform, followed by spectral phasing and
baseline correction.
[0049] Processed spectra are prepared for principal component
analysis (PCA) using AMIX. When distinct clustering patterns are
observed, models are built for each class. Robust models are
selected from these models and are investigated to identify
spectral outlier regions correlated with suicide risk. When
significant loadings are identified, chemical analysis methods are
combined with the spectral analysis.
Example 4. HPLC and HPLC Analysis
[0050] Liquid chromatography in the LC-MS system is conducted using
an Agilent Technologies 1200 series HPLC. The LC-MS instrument
collects raw data in the form of individual mass spectra at each
time point in the total ion chromatogram. Individual LC-MS analyses
are loaded into the sample table in the Bruker Profile Analysis
software package (Bruker Daltonics).
Example 5. DNA Analysis
[0051] The Promega Magnesil RED silica-coated magnetic bead kit on
a KingFisher 96 robotic magnetic bead manipulator is used to
extract DNA from a blood sample.
I.B. Clinical Analysis
[0052] An embodiment of the present disclosure may provide systems
and methods of assessing a suicide risk in a human subject
involving gathering clinical risk factor(s) and analyzing the
clinical risk factor(s) to determine a clinical risk factor
score.
[0053] Clinical characteristics about a human subject may be
collected during patient interviews, from medical record databases
and/or other similar means. Medical and/or mental health staff may
administer interviews with a subject and/or the subject's parent(s)
or guardian(s).
[0054] Clinical factors that increase the risk of completed suicide
in children and adolescents may include (without limitation): high
suicidal intent as evidenced by planning, timing and method, a
history of previous suicide attempts, a high level of interpersonal
discord, a presence of a mood disorder, substance use, a history of
impulsive aggression, a family history of suicidal behavior, access
to weapons such as firearms, and recent psychosocial stressors such
as conflicts with authority, breakups with significant others or
legal issues. Other clinical risk factors may include evidence of
planning, timing the attempt to avoid detection, not confiding
suicidal plans ahead of time and expressing a wish to die.
[0055] In one or more embodiments, a prior suicide attempt may be a
primary risk factor for youth suicide, and may greatly elevate the
risk of a subsequent suicide completion. The risk for another
attempt may be high in the first 3 to 6 months after an
unsuccessful suicide attempt, and the risk may remain elevated for
at least several years. Suicidal intent may be another indicator
and risk factor for repetition of suicide attempts and completed
suicide.
[0056] In an exemplary embodiment, clinical interviews with a
subject and/or their parent(s) or guardian(s) may include oral
and/or written interviews. Examples of such interview tools may
include (without limitation): [0057] 1. Background form(s) to
elicit demographics information [0058] 2. Suicide history form(s)
to elicit exposure to suicide, connectedness to family, history of
neglect or abuse, access to firearms, sleep habits, etc. [0059] 3.
The Columbia suicide history form(s) to elicit information about
lifetime suicide attempts. [0060] 4. The Suicide Intent Scale (SIS)
to evaluate the severity of suicidal intent for a previous suicide
attempt. [0061] 5. Family History-Research Diagnostic Criteria
(FH-RDC) to diagnose psychiatric illnesses in first- and
second-degree relatives of subjects. [0062] 6. Affective Story Task
for Speech Sample to measure "Theory of Mind" ability within the
context of emotionally charged situations. This may be a measure of
false-belief understanding (e.g. one character's beliefs about the
mental state of another character) and consists of positive-,
neutral- and negative-valenced stories. Stories may be matched on
word length, complexity (e.g. details, dialogue, characters and
events) and semantic structure. The positive, neutral and negative
stories may include content consistent with subjective experience
of respectively manic, euthymic or depressed states. Three stories
from each condition may be generated, and each subject may receive
one story from each of the three conditions. Each story may be read
aloud to the interview subject, and the order of conditions may be
counterbalanced across subjects to control for order effects.
Stories may be gender specific; female subjects may receive a
female story version and male subjects may receive a male story
version. Subjects may be assessed on their ability to recognize
that a misleading series of events could lead one of the characters
in the story to develop a false belief about another character's
mental state. At the end of each story, the subject may be asked a
false-belief question that assesses whether the subject recognized
the potential for misunderstanding. Subject responses may be
recorded and transcribed into a secure database. The choice of
transcribed speech is pragmatic. That is, in an emergency situation
it may be unreasonable to ask the suicidal patient to write. It may
be, however, practical to ask questions of those patients who are
conscious and to receive answers. These interviews may be retained
for subsequent analysis.
[0063] In an exemplary embodiment, clinical interviews with just
the subject may include oral and/or written interviews. Examples of
such interview tools may include (without limitation): [0064] 1.
Suicide Probability Scale (SPS): a tool for rating "normals," a
psychiatric inpatient group, and a suicide attempter group. [0065]
2. Youth Risk Behavior Survey: a tool related to personal safety,
suicide attempt, tobacco use, alcohol and drug use, sexual
activity, etc. [0066] 3. Stressful Life Events Schedule (SLES): a
tool yielding information on the occurrence, the date of
occurrence, the duration, and the perceived threat of events
experienced by the patient. [0067] 4. Achenbach Youth
Self-Report/11-18 (YSR): A tool for 5th grade reading skills that
obtains reports from parents, relatives and/or guardians about
children's competencies and behavioral/emotional problems. [0068]
5. Affective Story Task for Speech Sample (as discussed
previously).
[0069] In an exemplary embodiment, clinical interviews with just
the parent(s) and/or guardian(s) may include oral and/or written
interviews. Examples of such interview tools may include (without
limitation):
[0070] 1. Achenbach Child Behavior Checklist for Ages 6-18
(CBCL/6-18).
[0071] 2. Stressful Life Events Schedule (SLES) (as described
previously)
[0072] 3. Conflict behavior questionnaire form(s)
I.C. Thought Analysis
[0073] An embodiment of the present disclosure may provide methods
of assessing a suicide risk in a human subject involving gathering
thought marker(s) and comparing the thought marker(s) to a
plurality of suicide notes to determine a thought marker score.
Natural language processing methods may be conducted to determine a
correlation between the thought marker(s) and a suicide notes
database. U.S. patent application Ser. No. 12/006,813, entitled,
Processing Text with Domain-Specific Spreading Activation Methods,
by Pestian et. al., provides examples of certain natural language
processing methods that may be used with present embodiments.
[0074] Suicide notes may essentially be artifacts of suicidal
thought. It is contemplated that machine-learning methods can
successfully differentiate a suicide note (or suicide note wording)
from a non-suicide writing (or non-suicide note wording). Such
machine-learning methods may be implemented as software
instructions. Such machine-learning methods may include linguistic
analysis (including open source algorithms available in "Perl"
language, for example). This linguistic analysis may include spell
checking, tokenizing, filtering, stemming, outlier removal and
normalization. Testing of exemplary machine-learning methods proved
to be about 78% accurate at identifying a suicide note.
[0075] Additional analyses may include: mean number of words per
sentence, proportion of ambiguous words, percent similarity (the
proportion of words that were shared between two different
corpora--a suicide note database and WordNet, an English language
lexical database, for example), relative entropy (amount of
information contained in one corpus [suicidal patients, for
example] compared to another corpus [control patients]), and
Squared Chi-square distance. Other known analyses may also be
implemented.
[0076] A number of machine-learning methods may be applied to the
transcribed data to test for differences between suicide notes and
non-suicide note writings. One tool for this analysis may be the
Waikato Environment for Knowledge Analysis (Weka). Specificity,
sensitivity and F1 may be computed. Methods useful in this research
may be organized into five categories.
[0077] Decision trees may include: J48/C4.5, Logistic Model Trees,
DecisionStump and M5P.
[0078] Classification Rules may include: JRIP, Repeated Incremental
Pruning to Produce Error Reduction (RIPPER), M5Rules, OneR, and
PART.
[0079] Function models may include: Sequential minimal
optimization, PolyKernel, Puk, RBFKernel, Logistic, and Linear
Regression.
[0080] Lazy Learners or Instance-based learner methods may include:
IBk and LBR.
[0081] Meta learner methods may include: AdaBoostM1, Bagging,
LogitBoost, MultiBoostAB and Stacking.
[0082] Exemplary thought analysis and machine learning methods may
include one or more of the following components: feature selection,
expert classification, word mending, annotation and machine
learning.
[0083] Feature Selection.
[0084] Feature selection, also called variable selection is a data
reduction technique for selecting the most relevant features for a
learning models. As irrelevant and redundant features are removed
the model's accuracy increases. Multiple methods for feature
selection may be used: bag-of-words, latent semantic analysis and
heterogeneous selection. In one example, heterogeneous selection
may be used. To reduce co-linearity, highly correlated features may
be removed; to increase the certainty that a feature is not
randomly selected, that feature may be required to appear in at
least 10% of the documents.
[0085] Parts of Speech.
[0086] A first step may be to tokenize each sentence to determine
if additional analysis is feasible. This may be done, for example,
using a custom Perl program. Next, using the Penn-Treebak tag set
and/or using The Lingua-EN-Tagger-0.13, 2004 module, for example,
several part of speech tags may be added to the feature space. This
tagging may be beneficial to establish the relationship of a
particular word to a particular concept.
[0087] Readability.
[0088] The Flesch and Kincaid readability scores may produce a high
information gain and may be included in the feature space. These
scores are designed to indicate comprehension difficulty. They
include an ease of reading and text-grade level calculation.
Computation of the Flesch and Kincaid indexes may be completed by
adding the Lingua::EN::Fathom module to the exemplary Perl
program.
[0089] Suicidal Emotions.
[0090] Collected suicide notes may be annotated with emotional
concepts. Developing an ontology to organize these concepts may
utilize both the Pubmed queries and expert literature reviews.
Using the Pubmed queries, a frequency analysis of the key-words in
a collection (e.g. 2,000) of suicide related manuscripts may be
conducted. Expert review of those keywords may yield a subset of
suicide related manuscripts that contain suicide emotional
concepts. These emotional concepts may be allocated to a plurality
of different classes. Several mental health professional may then
review each of the collected suicide notes, and assign the
emotional concepts found in those notes to the appropriate classes.
For example, the emotional concepts of guilt may be assigned to the
class of emotional states.
[0091] Machine Learning.
[0092] There are multiple general types of machine learning:
unsupervised, semi-supervised and supervised. Semi-supervised
methods use both labeled and unlabeled data and is efficient when
labeling data is expensive, which leads to small data sets. In an
example approach, the semi-supervised approach may be selected
mainly because the labeled data may be small. Additionally,
exemplary machine learning algorithms for that may be used, without
limitation, may be organized into five categories: Decision trees:
J48/C4.5, Logistic Model Trees, Decision Stump and M5P;
Classification Rules: JRIP, Repeated incremental Pruning to Produce
Error Reduction (RIPPER), M5Rules, OneR, and PART; Function models:
Sequential minimal optimization (SMO, a variant of SVM),
PolyKernel, Puk, RBF Kernel, Logistic, and Linear Regression; Lazy
Learners or Instance-based learner: Ibo and LBR; Meta learners;
AdaBoostM1, Bagging, Logit Boost, Multi Boost AB and Stacking.
[0093] Machine Categorization.
[0094] The following algorithms may be used to extract and quantify
relevant content features and create a heterogeneous,
multidimensional feature space:
[0095] 1. Structure: number of paragraphs,
[0096] 2. Spelling: number of misspellings (perl module
Text::SpellChecker).
[0097] 3. Tagging: number of tokens, number of words, number of
non-word characters, number of sentences, mean frequency of a word,
standard deviation of frequency of a word, maximal frequency of a
word, mean length of a sentence, standard deviation of length of a
sentence, maximal length of a sentence, frequency of 32 parts of
speech (perl module Lingua::EN::Tagger).
[0098] 4. Readability: Flesch-Kincaid grade level, Flesch reading
ease (perl module Lingua::EN::Fathom).
[0099] 5. Parsing: mean depth of a sentence, standard deviation of
depth of a sentence, maximal depth of a sentence (perl module
Lingua::CollinsParser.
[0100] Features arrived from different sources; and so, their
numeric values naturally fall in different ranges. For certain
machine categorization algorithms that means that some features
would become more important than others. To remedy this problem,
feature values were normalized based on a maximum value of one.
this created a matrix with 66 documents and 49 features and values
between 0 and 1. Since there are fewer features than documents,
features selection was not applied.
[0101] Algorithm Classification
[0102] Decision trees.
[0103] Classifier may be represented as a tree. Every node of a
tree may be represented by a decision list. The decision about
which branch to go to next may be based on a single feature
response. Leaves of the tree may be represented by the decisions
about which class should be assigned to a single document. The
following algorithms may be used: [0104] J48generates un-pruned or
pruned C4.5 revision for 8 decision trees. [0105] LMTimplements
`Logistic Model Trees. [0106] Decision Stump implements decision
stumps (trees with a single split only, i.e. one-level-decision
trees), which are frequently used as base learners for meta
learners such as Boosting.
[0107] Classification Rules.
[0108] Classifier may be represented by a set of logical
implications. If a condition for a document is true, then a class
is assigned. Condition may be composed of a set of feature
responses OR-ed or AND-ed together. These rules can also be viewed
as a simplified representation of a decision tree. The following
algorithms may be used: [0109] JR implements a fast propositional
rules learner, "Repeated Incremental Pruning to Produce Error
Reduction" (RIPPER). [0110] OneR builds a simple 1R classifier; it
is a set of rules that test a response of only one attribute.
[0111] PART generates a set of simplified rules from a C4.5
decision tree.
[0112] Function Models.
[0113] Classifiers can be written down as mathematical equations.
Decision trees and rules typically cannot. There are 2 example
classifiers in this category. The following algorithms may be used:
[0114] SMOI implements a sequential minimal optimization algorithm
for training a support vector classifier using linear kernel.
[0115] Logistic builds multinomial logistic regression models based
on ridge estimation. [0116] Lazy learners. Classifiers in this
category may not work until classification time.
[0117] Instance-Based Learning.
[0118] May be done by reviewing every instance in the training set
separately. An example algorithm that may be used in this category:
[0119] Ibo' provides a k-nearest neighbors classifier, which uses
Euclidean metric as a distance measure.
[0120] Bayesian classifiers.
[0121] Classifiers use Bays theorem and the assumption of
independence of features. An example algorithm that may be used in
this category: [0122] NB implements the probabilistic Naive Bayes
classifier.
I.D. Suicide Risk Score Analysis
[0123] An embodiment of the present disclosure may provide methods
of assessing a suicide risk in a human subject involving
calculating a suicide risk score based, at least in part, on
biological marker score(s), clinical risk factors score(s) and
thought marker score(s). Such calculation may be implemented in a
variety of implementations. In one embodiment, a single suicide
risk score may be calculated. Such a score may assist physicians
and/or medical employee in determining how likely a subject is to
attempt suicide upon or after being released from a medical
facility.
[0124] In an example embodiment, factors of the suicide risk score
calculations may include biological marker(s), clinical risk
factor(s) and thought marker(s). The example table below identifies
factors, measurement tool(s), method(s) and example result ranges
for each factor.
TABLE-US-00001 Example Result Factor Measurement tool(s) Method(s)
Range Biological Mass-spectrometry of Mass/charge ratio 3.0-13.0
Marker(s) 5-HIAA, cytokines, genomic analysis Thought Comparison of
Machine-learning 0.0-1.0 Marker(s) subject`s thoughts methods and
to suicide note correlation. database Clinical Subject and
Percentage of risks 0.0-1.0 Risk parent/guardian present in
patients as Factor(s) interviews compared to all risks.
[0125] Analyses may include independent factor analysis of
biological marker(s), clinical risk factor(s) and thought
marker(s), as discussed previously.
[0126] Data for one or more factors may be normalized between zero
and one to create a composite score of the biological marker(s),
clinical risk factor(s) and thought marker(s) and their
interaction.
[0127] In one embodiment (as shown in the table above), biological
markers may be reported as a scale from 3-13, where a lower
concentration may be of more concern. On the other hand, regarding
the clinical risk factor(s) and thought marker(s), a higher
concentration may be of more concern. In this case, the biological
marker(s) may be normalized using
1-(Patient.sub.i-min(Range.sub.i)/max(Range.sub.i)-min(Range.sub.i)).
In this case, the clinical risk factor(s) and thought marker(s) may
be normalized using
-(P.sub.i-min(Range.sub.i)/max(Range.sub.i)-min(Range.sub.i). An
example of such normalizations is shown in the table below. In this
table, a suicide risk score two patients is calculated as a mean of
the biological marker score, clinical risk factor score and thought
marker score.
[0128] In another embodiment, the subject's quartile rank as
compared to a database of other subjects may be calculated.
Quartiles (as shown in the table below) may provide some decision
support without purporting exactness.
TABLE-US-00002 Clinical Suicide SRS x Biological Thought Risk Risk
10 for Marker Marker Factor Score ease of Quartile Score Score
Score (SRS) reading Position Patient 1 3 0.78 0.6 Normalized 1.00
0.78 0.6 0.79 8 1st Patient 2 9 0.45 0.6 Normalized 0.40 0.45 0.6
0.48 5 3rd
[0129] To provide additional context for various aspects of the
present invention, the following discussion is intended to provide
a brief, general description of a suitable computing environment in
which the various aspects of the invention may be implemented. One
exemplary computing environment is depicted in FIG. 4. While one
embodiment of the invention relates to the general context of
computer-executable instructions that may run on one or more
computers, those skilled in the art will recognize that the
invention also may be implemented in combination with other program
modules and/or as a combination of hardware and software.
[0130] Generally, program modules include routines, programs,
components, data structures, etc., that perform particular tasks or
implement particular abstract data types. Moreover, those skilled
in the art will appreciate that aspects of the inventive methods
may be practiced with other computer system configurations,
including single-processor or multiprocessor computer systems,
minicomputers, mainframe computers, as well as personal computers,
hand-held wireless computing devices, microprocessor-based or
programmable consumer electronics, and the like, each of which can
be operatively coupled to one or more associated devices. Aspects
of the invention may also be practiced in distributed computing
environments where certain tasks are performed by remote processing
devices that are linked through a communications network. In a
distributed computing environment, program modules may be located
in both local and remote memory storage devices.
[0131] A computer may include a variety of computer readable media.
Computer readable media may be any available media that can be
accessed by the computer and includes both volatile and nonvolatile
media, removable and non-removable media. By way of example, and
not limitation, computer readable media may comprise computer
storage media and communication media. Computer storage media
includes volatile and nonvolatile, removable and non-removable
media implemented in any method or technology for storage of
information such as computer readable instructions, data
structures, program modules or other data. Computer storage media
includes, but is not limited to, RAM, ROM, EEPROM, flash memory or
other memory technology, CD ROM, digital video disk (DVD) or other
optical disk storage, magnetic cassettes, magnetic tape, magnetic
disk storage or other magnetic storage devices, or any other medium
which may be used to store the desired information and which may be
accessed by the computer.
[0132] An exemplary environment for implementing various aspects of
the invention may include a computer that includes a processing
unit, a system memory and a system bus. The system bus couples
system components including, but not limited to, the system memory
to the processing unit. The processing unit may be any of various
commercially available processors. Dual microprocessors and other
multi processor architectures may also be employed as the
processing unit.
[0133] The system bus may be any of several types of bus structure
that may further interconnect to a memory bus (with or without a
memory controller), a peripheral bus, and a local bus using any of
a variety of commercially available bus architectures. The system
memory may include read only memory (ROM) and/or random access
memory (RAM). A basic input/output system (BIOS) is stored in a
non-volatile memory such as ROM, EPROM, EEPROM, which BIOS contains
the basic routines that help to transfer information between
elements within the computer, such as during start-up. The RAM may
also include a high-speed RAM such as static RAM for caching
data.
[0134] The computer may further include an internal hard disk drive
(HDD) (e.g., EIDE, SATA), which internal hard disk drive may also
be configured for external use in a suitable chassis, a magnetic
floppy disk drive (FDD), (e.g., to read from or write to a
removable diskette) and an optical disk drive, (e.g., reading a
CD-ROM disk or, to read from or write to other high capacity
optical media such as the DVD). The hard disk drive, magnetic disk
drive and optical disk drive may be connected to the system bus by
a hard disk drive interface, a magnetic disk drive interface and an
optical drive interface, respectively. The interface for external
drive implementations includes at least one or both of Universal
Serial Bus (USB) and IEEE 1394 interface technologies.
[0135] The drives and their associated computer-readable media
provide nonvolatile storage of data, data structures,
computer-executable instructions, and so forth. For the computer,
the drives and media accommodate the storage of any data in a
suitable digital format. Although the description of
computer-readable media above refers to a HDD, a removable magnetic
diskette, and a removable optical media such as a CD or DVD, it
should be appreciated by those skilled in the art that other types
of media which are readable by a computer, such as zip drives,
magnetic cassettes, flash memory cards, cartridges, and the like,
may also be used in the exemplary operating environment, and
further, that any such media may contain computer-executable
instructions for performing the methods of the invention.
[0136] A number of program modules may be stored in the drives and
RAM, including an operating system, one or more application
programs, other program modules and program data. All or portions
of the operating system, applications, modules, and/or data may
also be cached in the RAM. It is appreciated that the invention may
be implemented with various commercially available operating
systems or combinations of operating systems.
[0137] A user may enter commands and information into the computer
through one or more wired/wireless input devices, for example, a
keyboard and a pointing device, such as a mouse. Other input
devices may include a microphone, an IR remote control, a joystick,
a game pad, a stylus pen, touch screen, or the like. These and
other input devices are often connected to the processing unit
through an input device interface that is coupled to the system
bus, but may be connected by other interfaces, such as a parallel
port, an IEEE 1394 serial port, a game port, a USB port, an IR
interface, etc.
[0138] A display monitor or other type of display device may also
be connected to the system bus via an interface, such as a video
adapter. In addition to the monitor, a computer may include other
peripheral output devices, such as speakers, printers, etc.
[0139] The computer may operate in a networked environment using
logical connections via wired and/or wireless communications to one
or more remote computers. The remote computer(s) may be a
workstation, a server computer, a router, a personal computer, a
portable computer, a personal digital assistant, a cellular device,
a microprocessor-based entertainment appliance, a peer device or
other common network node, and may include many or all of the
elements described relative to the computer. The logical
connections depicted include wired/wireless connectivity to a local
area network (LAN) and/or larger networks, for example, a wide area
network (WAN). Such LAN and WAN networking environments are
commonplace in offices, and companies, and facilitate
enterprise-wide computer networks, such as intranets, all of which
may connect to a global communications network such as the
Internet.
[0140] The computer may be operable to communicate with any
wireless devices or entities operatively disposed in wireless
communication, e.g., a printer, scanner, desktop and/or portable
computer, portable data assistant, communications satellite, any
piece of equipment or location associated with a wirelessly
detectable tag (e.g., a kiosk, news stand, restroom), and
telephone. This includes at least Wi-Fi (such as IEEE 802.11x (a,
b, g, n, etc.)) and Bluetooth.TM. wireless technologies. Thus, the
communication may be a predefined structure as with a conventional
network or simply an ad hoc communication between at least two
devices.
[0141] The system may also include one or more server(s). The
server(s) may also be hardware and/or software (e.g., threads,
processes, computing devices). The servers may house threads to
perform transformations by employing aspects of the invention, for
example. One possible communication between a client and a server
may be in the form of a data packet adapted to be transmitted
between two or more computer processes. The data packet may include
a cookie and/or associated contextual information, for example. The
system may include a communication framework (e.g., a global
communication network such as the Internet) that may be employed to
facilitate communications between the client(s) and the
server(s).
[0142] In one exemplary embodiment, as depicted in FIG. 5, medical
facility computer system(s) 146a, 146b . . . 146n, may be in
communication with server system(s) 56. This communication may be
implemented through any network connection 52 (such as the Internet
or an intranet, for example). The server system(s) may include
software instructions, database(s), and inference engine and/or a
front end (such as a graphical user interface, for example). In the
embodiment shown in FIG. 5, the software instructions stored on the
server(s) may be configured to implement a suicide risk interface.
The server(s) may also be configured to provide a suicide risk
assessment front end 58. This front end may be provide to the
medical facility computers (146a-n) user interface, a graphical
user interface or other similar front end. The server(s) may also
store one or more databases that may include management databases
60, biological marker databases 48, clinical risk factor databases
62, thought marker databases 50, suicide risk score databases 64
and/or suicide note language databases 66. The management
database(s) 60 may be configured to store and make accessible data
used by the suicide risk interface software instructions, among
other components. The biological marker database(s) 48 may be
configured to store and make accessible data associated with
biological markers. The clinical risk factor database(s) 62 may be
configured to store and make accessible data associated with
clinical risk factors. The thought marker database(s) 50 may be
configured to store and make accessible data associated with
thought markers. The suicide risk score database(s) 64 may be
configured to store and make accessible data associated with
biological marker scores, clinical risk factor scores, thought
marker scores, suicide risk scores, quartiles and other similar
data. The suicide note language database(s) 66 may be configured to
store and make accessible data associated with suicide note
language, such as providing a corpus of suicide note language.
II. End-of-Life Assessment and Care Implementation
[0143] Beyond the long-standing traditional method of regular
conversation with the terminally ill patient, the present example
implementation provides that at least two additional sources of
information may aide the caregiver in understanding the needs of
the dying child and their family. They are thought-markers and
biomarkers. Thought-markers can be described as artifacts of
thought that are expressed through conversations and writings.
First order thought-markers may include writings and transcribed
conversations of the individual. Second order thought-markers may
include items like facial expressions or the natural pauses during
conversation.
[0144] A second source of information are biomarkers that
potentially change as death approaches. Some biomarkers that are
related to tracking death include C-reactive protein (Erlinger, T.
P., et al., "C-reactive protein and the risk of incident colorectal
cancer," JAMA, 2004. 291(5): pp. 585-90; and Clarke, R., et al.,
"Biomarkers of inflammation predict both vascular and non-vascular
mortality in older men," Eur Heart J, 2008. 29(6): pp. 800-9), NGal
(Mishra, J., et al., "Neutrophil gelatinase-associated lipocalin
(NGAL) as a biomarker for acute renal injury after cardiac
surgery," Lancet, 2005. 365(9466): pp. 1231-8), cystatin C
(Gronroos, M. H., et al., "Comparison of glomerular function tests
in children with cancer; and Shlipak, M. G., et al., "Cystatin C
and the risk of death and cardiovascular events among elderly
persons," N Engl J Med, 2005. 352(20): pp. 2049-60), albumin (Wang,
T. J., et al., "Multiple biomarkers for the prediction of first
major cardiovascular events and death," N Engl J Med, 2006.
355(25): pp. 2631-9), and various cytokines (Maletic, V. et al.,
"Neurobiology of depression: an integrated view of key findings,"
Int J Clin Pract, 2007. 61(12): pp. 2030-2040). The references
listed above are herein incorporated by reference in their
entirety. This example implementation may provide end-of-life care
that can be personalized and dispensed based upon the analyses
provided herein.
[0145] The terminally ill patient follows a certain illness
trajectory when moving from health to ill health. This includes
three stages: having a potentially curable illness, undergoing
intensive treatment, and being diagnosed where no curative
treatment exists. Often a patient is considered to be terminally
ill when the life expectancy is estimated to be six months or less,
under the assumption the disease will run its course. At each of
these stages there is an age-dependent cognitive trajectory that is
hypothesized as tractable. This trajectory may include depression,
hopelessness, suicidal ideation, fear, anxiety, and anger. Treating
these patients may fall into one of two approaches: palliative and
hospice care.
[0146] The stress of having a terminal illness can lead to
psychiatric disorders and need for mental health services. In one
study, two-hundred and fifty-one pediatric patients with advanced
cancer were studied for mental illness. Twelve percent met criteria
of having Major Depressive Disorder, Generalized Anxiety Disorder,
Panic Disorder, or Post-Traumatic Stress Disorder. Twenty-eight
percent had access to mental health services, 17% used those
services, and 90% responded that they would use mental health
services if available (Kadan-Lottick, N. S., et al., "Psychiatric
disorders and mental health service use in patients with advanced
cancer: a report from the coping with cancer study," Cancer 2005.
104(12): pp. 2872-81, herein incorporated by reference in its
entirety).
[0147] One area that appears to have no consideration to date is
the application of computational linguistics to understand what
terminal patients and parents (family members) are saying as death
approaches and how this differs from the non care patients. This
analysis relies on information extraction and natural language
processing.
II.A. Information Extraction--Thought Markers
[0148] The goal of information extraction systems is to extract
facts related to a particular domain from natural language texts.
Texts that are inherently ambiguous, because of hyperbole or
metaphors, often cause the accuracy of an information exatraction
system to decline. Information Extraction extracts data that are
either nomothetic or idiographic. Nomothetic data represents
statistical-type data, like age, gender, cholesterol levels, and so
forth. Extracting information like the frequency of a rash
occurring when a child is prescribed carbamazepine for epilepsy is
a straightforward task as long as the nomothetic data are
available. Ideographic data describe an individual's subjective
characteristics like emotions, feelings, and so forth. Extracting
information like the frequency of rash occurrences by an epileptic
adolescent on carbamazepine is, on the other hand, not as straight
forward.
[0149] Since the early 2000s there has been increased attention
focused on ideographic information extraction. This focus has
concentrated on topics like polarity (positive or negative)
(Turney, P. and M. Littman, "Measuring praise and criticism:
Inference of semantic orientation from association," ACM
Transactions on Information Systems-TOIS, 2003. 21(4): pp. 315-346;
and Dave, K. S. Lawrence, and D. Pennock, "Mining the peanut
gallery: Opinion extraction and semantic classification of product
reviews," 2003: ACM New York, N.Y., USA), hostility (Spertus, E.,
"Smokey: Automatic recognition of hostile messages. 1997: JOHN
WILEY & SONS LTD.), multi-document summarization (Yu, H. and V.
Hatzivassiloglou, "Towards answering opinion questions: Separating
facts from opinions and identifying the polarity of opinion
sentences," 2003), and tracking sentiments toward events (Tong. R.,
"An operational system for detecting and tracking opinions in
on-line discussions. 2001; and Suh, E., E. Diener, and F. Fujita,
"Events and subjective well-being: Only recent events matter,"
Journal of personality and social psychology, 1996, 70(5): pp.
1091-1102) and subsequently there have been hundreds of papers
published on the subject (see above and Das, S. and M. Chen,
"Yahoo! for Amazon: Sentiment extraction from small talk on the
web," Management Science, 2007. 53(9): pp. 1375-1388). The
references listed above are incorporated by reference in their
entirety. Factors behind this interest include: the rise of machine
learning methods in natural language processing and information
retrieval; the availability of datasets for machine learning
algorithms to be trained on, due to the blossoming of the World
Wide Web and, specifically, the development of review-aggregation
web-sites; and, of course realization of the fascinating
intellectual challenges and commercial and intelligence
applications. The present example implementation focuses on
tracking sentiments about a major life event, in this case
death.
II.B. BioMarkers
[0150] This example implementation may monitor, in an embodiment, a
number of chemical based biomarkers. Each one has been shown to
potentially change as death approaches. As discussed above, some
biomarkers that may be related to tracking death are: C-reactive
protein, NGal, cystatin C, albumin, and various cytokines.
[0151] A wide range of biomarkers, reflecting activity in a number
of biological systems (e.g. neuroendocrine, immune, cardiovascular,
and metabolic), have been found to prospectively predict
disability, morbidity, and mortality in older adult populations.
For example, Clarke, et al identified a correlation between
biomarkers of inflammation (C-reactive protein, fibrinogen and
total/HDL-C) and vascular and non-vascular mortality in older men.
Shlipak, et al. showed that higher cystatin C levels were directly
associated, in a dose-response manner, with a higher risk of death
from all causes. Gruenewald, et al studied 13 different biomarkers
in the elderly over a 12 year period (n=1189). Using recursive
partitioning methods they found that most all were associated with
high-risk pathways and combinations of biomarkers were associated
with mortality. Wang, et al measured 10 biomarkers in 3209 patients
attending routine examination cycle of the Framingham Heart study
for the prediction of the first major cardiovascular events and
death. They found that using the 10 contemporary biomarkers adds
only moderately to standard risk factors. Finally, Zethelius, et
al. studied the incremental usefulness of adding multiple
biomarkers from different disease pathways for predicting the risk
of cardiovascular death. They found that the simultaneous addition
of several biomarkers improves the risk stratification for death
from cardiovascular causes. Additionally, some of these cytokines
have been linked to major mood disorders and suicidal and
non-suicidal tendencies (TNF a, IL-6, and IL-10).
[0152] The present example implementation provides a system and
method that integrates biomarkers and thought-marker analysis to
result in a better understanding how to increase the quality of
care for terminally ill patients.
[0153] FIG. 6 provides a flow diagram representing an embodiment of
a method according to the present exemplary implementation. This
method may be operating from one or more memory devices including
computer-readable instructions configured to instruct a
computerized system to perform the method, and the method may be
operating on a computerized system including one or more computer
servers (or other available devices) accessible over a computer
network such as the Internet or over some other data network. The
method may include the following operations, which do not
necessarily need to be performed in the stated order. Operation 70
involves receiving biomarker data obtained from an analysis of a
subject's biological sample. Operation 72 involves receiving
thought-marker data obtained pertaining to one or more of the
subject's recorded thoughts, spoken words, transcribed speech,
writings and/or facial expressions. Operation 74 involves
generating a biomarker score associated with end-of-life treatment
relevance from the biomarker data. Operation 76 involves generating
a thought-marker score associated with end-of-life treatment
relevance from the thought-marker data. Operation 78 involves
calculating an end-of-life treatment score based, at least in part,
upon the biomarker score and the thought-marker score.
[0154] In an embodiment, the step of generating the biomarker score
may include a step of accessing a level of one or more chemical
based biomarkers from the biological sample that have been shown to
change as the subject nears death. Alternatively, or in addition,
the step of generating the biomarker score includes a step of
assessing a level of C-reactive protein, NGal, cystatin, albumin,
IL-6 cytokine, IL-2 cytokine, IFN-.gamma. cytokine, IL-4 cytokine
and/or TGF-.beta.1 cytokine biomarkers from the biological
sample.
[0155] In an embodiment, the step of generating a thought-marker
score includes a step of determining a correlation between (a) the
one or more of the human subject's recorded thoughts, spoken words,
transcribed speech, writings and facial expressions; and (b) a
corpus of thought data collected pertaining, at least in part, to
the end-of-life treatment relevance.
[0156] In an embodiment, the step of generating a biomarker score
includes a step of calculating a composite score related to two or
more biological markers associated with the end-of-life treatment
relevance from the biomarker data.
[0157] In an embodiment, the step of calculating the end-of-life
treatment score includes steps of: (a) normalizing the biomarker
score, (b) normalizing the thought-marker scores, and (c)
calculating a mean of at least the normalized biomarker score and
the thought-marker scores. Furthermore, these normalizing steps may
normalize between a scale of 0.0 and 1.0 and/or a scale of 0 and
10'' where N is an integer (e.g. between 0 and 10, between 0 and
100, between 0 and 1,000 and so forth).
III. Assessment of Neuropsychiatric Conditions
[0158] Based upon the above, it will be readily apparent that many
neuropsychiatric conditions may be readily assessed based upon the
implementation of the methodologies and systems provided herein.
Examples of such other neuropsychiatric conditions may include,
without limitation: a risk that a subject may perform or repeat a
criminal act and/or a harmful act, a risk of the subject having a
psychiatric illness (such as schizophrenia), and a risk of a
subject feigning a psychiatric illness.
[0159] Such a method for assessing such neuropsychiatric conditions
may be operating from one or more memory devices including
computer-readable instructions configured to instruct a
computerized system to perform the method, and the method may be
operating on a computerized system including one or more computer
servers (or other available devices) accessible over a computer
network such as the Internet or over some other data network. The
method may include the following operations as shown in FIG. 7,
which do not necessarily need to be performed in the stated order.
Such operations may include an operation 80 of receiving biomarker
data associated from an analysis of the subject's biological
sample. An operation 82 may involve receiving thought-marker data
obtained pertaining to one or more of the subject's recorded
thoughts, spoken words, transcribed speech, and writings. An
operation 84 may include generating a biomarker score associated
with the neuropsychiatric condition from the biomarker data. An
operation 86 may include generating a thought-marker score
associated with the neuropsychiatric condition from the
thought-marker data. An operation 88 may involve calculating a
neuropsychiatric condition score based, at least in part, upon the
biomarker score and the thought-marker score.
[0160] In an embodiment, the step of generating the biomarker score
may include a step of assessing a level of at least a cytokine, a
metabolite, a polymorphism, a genotype, a polypeptide, and an mRNA
of the human subject. For example, the step of generating the
biomarker score may include a step of assessing a level of a
hydroxyindoleaceticacid (5HIAA).
[0161] In an embodiment, the step of generating a thought-marker
score includes a step of determining a correlation between (a) the
human subject's recorded thoughts, spoken words, transcribed speech
and/or writings; and (b) a corpus of thought data collected
pertaining, at least in part, to the neuropsychiatric condition.
Further, this correlation may be determined, at least in part,
utilizing natural language processing and/or machine learning
algorithms.
[0162] In an embodiment, the method may further include a step of
receiving clinical data of the subject associated with the
neuropsychiatric condition; may include a step of generating a
clinical data score from the clinical data; and the step of
calculating in neuropsychiatric condition score may be based, at
least in further part, upon the clinical data score. Further, the
clinical data of the subject associated with the neuropsychiatric
condition may include at least a portion of medical patient record
data associated with the subject; may include demographic data
associated with the subject; and/or may include interview and/or
survey data obtained from the subject. With this embodiment, it is
possible that the step of calculating a neuropsychiatric condition
score may include steps of (a) normalizing the biomarker score, (b)
normalizing the thought-marker score, (c) normalizing the clinical
data score and (d) calculating a mean of at least the normalized
biomarker, thought marker and clinical data scores. Further, the
normalizing steps normalize between a numerical scale of 0.0 to 1.0
and/or a scale of 0 and 10.sup.N, wherein N is an integer. Further,
the step of generating a clinical data score may include a step of
calculating a percentage of risks associated with the
neuropsychiatric condition from the subject compared to a
predetermined set of risks associated with the neuropsychiatric
condition.
[0163] In an embodiment, the step of generating a biomarker score
includes a step of calculating a composite score related to two or
more biological markers associated with the neuropsychiatric
condition from the biomarker data.
[0164] In an embodiment, the step of calculating a neuropsychiatric
condition score includes steps of (a) normalizing the biomarker
score, (b) normalizing the thought marker score and (c) calculating
a mean of at least the normalized biomarker and the thought marker
scores.
[0165] In an embodiment, the method further includes a step of
automatically recommending, based upon the calculated
neuropsychiatric condition score, a subject's treatment regimen, a
subject's counseling session, a subject's intervention program
and/or a subject's care program.
[0166] Following from the above disclosure, it should be apparent
to those of ordinary skill in the art that, while the methods and
apparatuses herein described constitute exemplary embodiments of
the present invention, it is to be understood that the inventions
contained herein are not limited to the above precise embodiment
and that changes may be made without departing from the scope of
the invention. Likewise, it is to be understood that it is not
necessary to meet any or all of the identified advantages or
objects of the invention disclosed herein in order to fall within
the scope of the invention, since inherent and/or unforeseen
advantages of the present invention may exist even though they may
not have been explicitly discussed herein.
* * * * *