Computer implemented system and method for assessing a neuropsychiatric condition of a human subject
First Claim
1. One or more non-transitory computer readable memory devices including computer-readable instructions configured to instruct a computerized system to perform a method for assessing a suicide attempt risk of a human subject, the method including the steps of:
- obtaining a biological sample from the subject;
determining one or more suicide risk associated biological markers 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 to provide biomarker data for the subject;
receiving, using one or more processors, the biomarker data;
receiving, using one or more processors, thought-marker data including one or more of the subject'"'"'s recorded thoughts, spoken words, transcribed speech, and writings;
generating, using one or more processors, a numerical biomarker score from the biomarker data, said biomarker score generated bynormalizing, using the one or more processors, the one or more determined suicide risk associated biological markers,generating a normalized score for each of the markers based on the strength of the marker'"'"'s association with suicide risk andsumming the individual normalized scores for the one or more determined suicide risk associated biological markers;
generating, using one or more processors, a thought-marker score associated with the suicide attempt risk from the thought-marker data by a method comprising the steps ofdetermining a correlation between (a) the thought marker data of the subject and (b) a corpus of thought data comprising a set of suicide notes language associated with prior completions of suicides,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, andgenerating the thought-marker score based upon the strength of the correlation,andgenerating a suicide attempt risk score based, at least in part, upon the biomarker score and the thought-marker score, andgenerating, using the suicide attempt risk score, a treatment program for the subject.
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Accused Products
Abstract
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 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. 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.
19 Citations
17 Claims
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1. One or more non-transitory computer readable memory devices including computer-readable instructions configured to instruct a computerized system to perform a method for assessing a suicide attempt risk of a human subject, the method including the steps of:
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obtaining a biological sample from the subject; determining one or more suicide risk associated biological markers 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 to provide biomarker data for the subject; receiving, using one or more processors, the biomarker data; receiving, using one or more processors, thought-marker data including one or more of the subject'"'"'s recorded thoughts, spoken words, transcribed speech, and writings; generating, using one or more processors, a numerical biomarker score from the biomarker data, said biomarker score generated by normalizing, using the one or more processors, the one or more determined suicide risk associated biological markers, generating a normalized score for each of the markers based on the strength of the marker'"'"'s association with suicide risk and summing the individual normalized scores for the one or more determined suicide risk associated biological markers; generating, using one or more processors, a thought-marker score associated with the suicide attempt risk from the thought-marker data by a method comprising the steps of determining a correlation between (a) the thought marker data of the subject and (b) a corpus of thought data comprising a set of suicide notes language associated with prior completions of suicides, 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 suicide attempt risk score based, at least in part, upon the biomarker score and the thought-marker score, and generating, using the suicide attempt risk score, a treatment program for the subject. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16)
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17. One or more non-transitory computer readable memory devices including computer-readable instructions configured to instruct a computerized system to perform a method for assessing a suicide attempt risk of a human subject, the method including the steps of:
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obtaining a biological sample from the subject; determining one or more suicide risk associated biological markers based on the obtained biological sample 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; normalizing, using one or more processors, the one or more determined suicide risk associated biological markers and generating a normalized score for each based on the strength of marker'"'"'s association with suicide risk; generating, using the one or more processors, a biomarker score based on a sum of individual normalized scores for the one or more determined suicide risk associated biological markers; receiving, using the one or more 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 the one or more processors, a first query and transmitting the first query to a suicide notes database to obtain a plurality of suicide notes associated with prior completions of suicides, the one or more processors being communicatively coupled to the suicide notes database using one or more communications networks; comparing, using one or more machine learning methods, 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, the one or more machine learning methods implementing a classification algorithm including at least one of the following;
a decision tree, a classification rule, a function model, an instance-based learner method, and any combination thereof,the one or more machine learning methods including extracting and quantifying relevant content features of the one or more thought markers; and generating, based on extracting and quantifying, a heterogeneous, multidimensional feature space containing a plurality of feature values corresponding to quantified content features; normalizing the generated feature values; and generating, using the normalized generated feature values, a thought-marker score based upon a strength of the correlation, and 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, a treatment program for the subject.
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Specification