WO2019225798A1 - Procédé et dispositif de sélection d'une question dans de multiples feuilles de test psychologique sur la base d'un apprentissage automatique pour diagnostiquer rapidement les symptômes d'anxiété et de dépression - Google Patents

Procédé et dispositif de sélection d'une question dans de multiples feuilles de test psychologique sur la base d'un apprentissage automatique pour diagnostiquer rapidement les symptômes d'anxiété et de dépression Download PDF

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Publication number
WO2019225798A1
WO2019225798A1 PCT/KR2018/007056 KR2018007056W WO2019225798A1 WO 2019225798 A1 WO2019225798 A1 WO 2019225798A1 KR 2018007056 W KR2018007056 W KR 2018007056W WO 2019225798 A1 WO2019225798 A1 WO 2019225798A1
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WO
WIPO (PCT)
Prior art keywords
item selection
depression
tool
gad
interactive
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PCT/KR2018/007056
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English (en)
Korean (ko)
Inventor
정범석
채명수
윤석호
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한국과학기술원
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Priority claimed from KR1020180071641A external-priority patent/KR102111852B1/ko
Application filed by 한국과학기술원 filed Critical 한국과학기술원
Publication of WO2019225798A1 publication Critical patent/WO2019225798A1/fr

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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/16—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/20—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment

Definitions

  • the following examples relate to methods and apparatuses for classifying psychological risk groups, and more particularly, to machine-based question screening methods for rapid diagnosis of anxiety and depressive symptoms in multiple psychological papers. And to an apparatus.
  • Embodiments describe a machine learning-based question screening method and apparatus for rapid diagnosis of anxiety and depression in a plurality of psychological papers, and more specifically, a risk group classification algorithm for use in an interactive diagnostic tool of psychiatry. Develop and evaluate its performance.
  • Embodiments provide interactive diagnostic tools based on machine learning using real clinical data to screen for a variety of mental illnesses that can occur in real stress situations rather than a single disease. To provide a machine learning-based item screening method and apparatus for rapid diagnosis of anxiety and depressive symptoms in a number of psychological papers.
  • Machine learning-based item screening method for rapid diagnosis of anxiety and depressive symptoms in a plurality of psychological paper machine learning based interactive diagnosis using mental health survey data as a training data set Learning an interactive diagnosis tool; And updating status information of the subject every time the user responds to the interactive diagnostic tool.
  • the method may further include a preprocessing step of applying an Rpart tree algorithm, which is an R package to which a resampling technique is applied, for the design of the interactive diagnostic tool.
  • an Rpart tree algorithm which is an R package to which a resampling technique is applied
  • the method may further include diagnosing a mental disorder through a specific question using the interactive diagnostic tool.
  • the mental health survey data includes the Fatty Health Questionnaire-9 (PHQ-9), the Generalized Anxiety Disorder-7 (GAD-7), and the Liebowitz social anxiety scale (LSAS). It can be configured as.
  • the learning of the interactive diagnostic tool includes the Depression Screening Tool (Patient Health Questionnaire-9, PHQ-9), Generalized Anxiety Disorder-7 (GAD-7), and the Liebowitz Social Anxiety Scale (Liebowitz social). Training an decision tree to find subjects at high risk of depression, generalized anxiety disorder, and social anxiety disorder using anxiety scale (LSAS).
  • Depression Screening Tool Patient Health Questionnaire-9, PHQ-9
  • GAD-7 Generalized Anxiety Disorder-7
  • GID-7 Generalized Anxiety Disorder-7
  • Liebowitz Social Anxiety Scale Liebowitz Social Anxiety Scale
  • the interactive diagnostic tool may further include asking additional questions to determine the presence or absence of a mental illness when the decision tree is identified as a risk group.
  • Each target value of the Depression Screening Tool (Patient Health Questionnaire-9, PHQ-9), Generalized Anxiety Disorder-7 (GAD-7), and the Liebowitz social anxiety scale (LSAS)
  • the target value may be defined as the total score of each item exceeding a specific cut-off value.
  • Machine learning-based question screening device for the rapid diagnosis of anxiety and depressive symptoms in a plurality of psychological test paper according to another embodiment, the input unit for inputting the mental health survey data to the training data set; A learning unit for learning an interactive diagnosis tool based on machine learning using the training data set; And an update unit for updating the subject's status information each time the user responds to the interactive diagnostic tool.
  • the apparatus may further include a preprocessor configured to apply an Rpart tree algorithm, which is an R package to which a resampling technique is applied, for the design of the interactive diagnostic tool.
  • a preprocessor configured to apply an Rpart tree algorithm, which is an R package to which a resampling technique is applied, for the design of the interactive diagnostic tool.
  • the apparatus may further include a diagnosis unit for diagnosing a mental disease through a specific question using the interactive diagnostic tool.
  • the mental health survey data includes the Fatty Health Questionnaire-9 (PHQ-9), the Generalized Anxiety Disorder-7 (GAD-7), and the Liebowitz social anxiety scale (LSAS). It can be configured as.
  • the learning unit uses the Patient Health Questionnaire-9 (PHQ-9), the Generalized Anxiety Disorder-7 (GAD-7), and the Liebowitz Social Anxiety Scale (LSAS). Training decision trees to find people at high risk for depression, generalized anxiety, and social anxiety disorders.
  • PHQ-9 Patient Health Questionnaire-9
  • GID-7 Generalized Anxiety Disorder-7
  • LSAS Liebowitz Social Anxiety Scale
  • the learning unit may ask additional questions to determine whether there is a mental illness.
  • Each target value of the Depression Screening Tool (Patient Health Questionnaire-9, PHQ-9), Generalized Anxiety Disorder-7 (GAD-7), and the Liebowitz social anxiety scale (LSAS)
  • the target value may be defined as the total score of each item exceeding a specific cut-off value.
  • screening for various mental disorders that may occur in a real stress situation rather than a single disease by providing an interactive diagnostic tool based on machine learning using actual clinical data.
  • a number of psychological test papers can provide a machine learning-based item screening method and apparatus for rapid diagnosis of anxiety and depression.
  • a machine learning-based item screening method for rapid diagnosis of anxiety and depressive symptoms in a large number of psychological test papers that perform relatively accurate mental disease judgment with less conversation through a risk group classification algorithm for an interactive diagnostic tool can be provided.
  • FIG. 1 is a view schematically showing the structure of an item sorting apparatus according to an embodiment.
  • FIG. 2 is a flowchart illustrating a question screening method according to an exemplary embodiment.
  • FIG. 3 is a diagram illustrating a decision tree of a depression risk group according to an embodiment.
  • FIG. 4 illustrates a result of a pruning process of a decision tree of a depression risk group according to an exemplary embodiment.
  • FIG. 5 illustrates a decision tree of a GAD risk population, according to one embodiment.
  • FIG. 6 illustrates a result of a pruning process of a decision tree of a GAD risk group according to an embodiment.
  • FIG. 7 is a diagram illustrating a decision tree of an SAD risk group according to an embodiment.
  • FIG. 8 is a diagram illustrating a result of pruning a decision tree of a SAD risk group according to an embodiment.
  • the following embodiments may provide a machine learning based question screening method and apparatus for rapid diagnosis of anxiety and depressive symptoms in a plurality of psychological papers to classify a risk group of mental illness. To do this, we develop a risk group classification algorithm for use in interactive diagnostic tools in psychiatry and evaluate its performance.
  • the real clinical data may be used to provide an interactive diagnosis tool based on machine learning.
  • Interactive diagnostic tools based on machine learning can be screened for a variety of mental illnesses that can occur in real stress situations rather than a single disease, and can be designed to collect and judge appropriate additional information step by step. have.
  • FIG. 1 is a view schematically showing the structure of an item sorting apparatus according to an embodiment.
  • an item selection apparatus the structure of an item selection apparatus according to an embodiment is schematically illustrated and is a view for explaining a decision making process of an interactive diagnostic tool.
  • a machine learning-based item screening device for quickly diagnosing anxiety and depressive symptoms in a number of psychological papers will be referred to simply as an item screening device.
  • the item sorting apparatus may include an input unit 110, a learner 120, and an updater 130.
  • the item selection device may further include a preprocessor and a diagnosis unit 140.
  • the input unit 110 inputs basic information for determining whether there is a mental disease as a training data set, and may input collected mental health survey data as a training data set.
  • Mental health survey data can be collected from a subject, such as a number of psychological papers obtained from multiple subjects.
  • mental health survey data include the Patient Health Questionnaire-9 (PHQ-9), the Generalized Anxiety Disorder-7 (GAD-7), and the Liebowitz social anxiety scale (LSAS). It can be composed of).
  • the learner 120 may train an interactive diagnosis tool based on machine learning using a training data set.
  • machine learning-based interactive diagnostic tools can be designed to perform a screening test for depression, diagnose depression, and finally diagnose depression by collecting additional information about depression.
  • general anxiety disorders and social anxiety disorders may be designed to perform a screening test, diagnose each mental disorder, and collect additional information step by step to finally diagnose the mental disorder.
  • the learning unit 120 may use the depression screening tool (PHQ-9), the panic anxiety assessment (GAD-7), and the Liebowitz Social Anxiety Scale (LSAS) to determine depression, anxiety disorder, and social anxiety disorder. You can train the decision tree to find high-risk subjects. Thereafter, when the decision tree is identified as a risk group, the learning unit 120 may ask additional questions to determine whether there is a mental disease.
  • PHQ-9 depression screening tool
  • GAD-7 panic anxiety assessment
  • LSAS Liebowitz Social Anxiety Scale
  • each target value of the depression screening tool PHQ-9
  • generalized anxiety disorder assessment GAD-7
  • LSAS Liebowitz Social Anxiety Scale
  • the updater 130 updates the patient's status, and can update the subject's status information each time the user responds to the interactive diagnostic tool.
  • the item selection device may further include a preprocessor and a diagnosis unit 140.
  • the preprocessor may apply an Rpart tree algorithm, which is an R package to which the resampling technique is applied, for the design of the interactive diagnostic tool.
  • diagnosis unit 140 may diagnose a mental disease through a specific question using an interactive diagnostic tool. That is, the diagnosis unit 140 may finally diagnose the various mental diseases by identifying the optimal question. At this time, the diagnosis unit 140 may finally diagnose the mental disease through appropriate intervention.
  • mental health survey data obtained from 5858 subjects at a university may be used as a training data set for machine learning.
  • the survey may consist of the Depression Screening Tool (PHQ-9), the Global Anxiety Disability Assessment (GAD-7), and the Liebowitz Social Anxiety Scale (LSAS).
  • the Rpart tree algorithm which is an R package to which the resampling technique is applied, may be applied in the preprocessing step to solve the imbalance problem (Non-Patent Document 1).
  • Each target value may be defined as the total score of each item exceeding a specific cutoff value.
  • the accuracy, recall and all F1T scores of the trained algorithm are as follows (accuracy / recall / all F1T scores).
  • GAD Generalized Anxiety Disorder
  • FIG. 2 is a flowchart illustrating a question screening method according to an exemplary embodiment.
  • a machine learning-based item screening method for rapid diagnosis of anxiety and depression symptoms in a plurality of psychological test papers may include machine learning-based dialogue using mental health survey data as a training data set. Learning 220 the diagnostic type tool, and updating the status information of the subject every time the user responds to the interactive diagnostic tool 230.
  • the method may further include a preprocessing step 210 of applying an Rpart tree algorithm, which is an R package to which a resampling technique is applied, for the design of an interactive diagnostic tool.
  • an Rpart tree algorithm which is an R package to which a resampling technique is applied
  • the method may further include a step 240 of diagnosing a mental disease through a specific question using an interactive diagnostic tool.
  • Machine learning-based item screening method for the rapid diagnosis of anxiety and depressive symptoms in a plurality of psychological test paper according to an embodiment for rapid diagnosis of anxiety and depressive symptoms in a plurality of psychological test paper according to an embodiment described in FIG.
  • the machine learning based item selection device can be described in more detail as an example.
  • a machine learning-based item screening method for prompt diagnosis of anxiety and depressive symptoms in a plurality of psychological test papers will be briefly referred to as a question screening method.
  • the item sorting apparatus may include an input unit, a learning unit, and an update unit, and the item sorting apparatus may further include a preprocessor and a diagnostic unit.
  • the preprocessor may preprocess the Rpart tree algorithm, which is an R package to which the resampling technique is applied, for the design of the interactive diagnostic tool.
  • the learner may train the machine learning based interactive diagnostic tool by using the mental health survey data as the training data set.
  • the learning unit may receive the mental health survey data from the input unit as a training data set.
  • mental health survey data may be comprised of a depression screening tool (PHQ-9), a general anxiety disorder assessment (GAD-7), and the Liebowitz Social Anxiety Scale (LSAS).
  • the Department uses the Depression Screening Tool (PHQ-9), the Global Anxiety Disability Assessment (GAD-7), and the Liebowitz Social Anxiety Scale (LSAS) to find physicians who are at high risk for depression, generalized anxiety and social anxiety disorders. You can train the decision tree. The learning unit can then ask further questions to determine the presence of mental illness when the decision tree is identified as a risk group.
  • PHQ-9 Depression Screening Tool
  • GAD-7 Global Anxiety Disability Assessment
  • LSAS Liebowitz Social Anxiety Scale
  • Each target value of the Depression Screening Tool (PHQ-9), Global Anxiety Disability Assessment (GAD-7), and Liebowitz Social Anxiety Scale (LSAS) can be defined as the total score of each item exceeding a specific cutoff value.
  • the updater may update the subject's status information each time the user responds to the interactive diagnostic tool.
  • the diagnosis unit may diagnose a mental disease through a specific question using an interactive diagnostic tool.
  • the risk group classification algorithm for the interactive diagnostic tool enables relatively accurate mental disease determination with less conversation.
  • FIG. 3 is a diagram illustrating a decision tree of a depression risk group according to an embodiment.
  • 4 illustrates a result of a pruning process of a decision tree of a depression risk group according to an embodiment.
  • the decision tree can train the depression risk group through only two to five questions.
  • 5 illustrates a decision tree of a GAD risk population, according to one embodiment.
  • 6 illustrates a result of pruning a decision tree of a GAD risk group according to an embodiment.
  • the decision tree is a generalized anxiety disorder (GAD) risk group through only two or three questions. Can train.
  • FIG. 7 is a diagram illustrating a decision tree of an SAD risk group according to an embodiment.
  • 8 illustrates a result of pruning a decision tree of a SAD risk group according to an embodiment.
  • a risk group classification algorithm for use in an interactive diagnostic tool of psychiatry was developed and its performance was evaluated.
  • the risk group classification algorithm for the interactive diagnostic tool has relatively good results in depression, generalized anxiety disorder (GAD) and social anxiety disorder (SAD).
  • GAD generalized anxiety disorder
  • SAD social anxiety disorder
  • the developed risk group discrimination algorithm is expected to assist in making relatively accurate judgments with less dialogue with interactive diagnostic tools.
  • the apparatus described above may be implemented as a hardware component, a software component, and / or a combination of hardware components and software components.
  • the devices and components described in the embodiments include, for example, processors, controllers, arithmetic logic units (ALUs), digital signal processors, microcomputers, field programmable arrays (FPAs), It may be implemented using one or more general purpose or special purpose computers, such as a programmable logic unit (PLU), microprocessor, or any other device capable of executing and responding to instructions.
  • the processing device may execute an operating system (OS) and one or more software applications running on the operating system.
  • the processing device may also access, store, manipulate, process, and generate data in response to the execution of the software.
  • OS operating system
  • the processing device may also access, store, manipulate, process, and generate data in response to the execution of the software.
  • a processing device may be described as one being used, but a person skilled in the art will appreciate that the processing device includes a plurality of processing elements and / or a plurality of types of processing elements. It can be seen that it may include.
  • the processing device may include a plurality of processors or one processor and one controller.
  • other processing configurations are possible, such as parallel processors.
  • the software may include a computer program, code, instructions, or a combination of one or more of the above, and configure the processing device to operate as desired, or process it independently or collectively. You can command the device.
  • Software and / or data may be any type of machine, component, physical device, virtual equipment, computer storage medium or device in order to be interpreted by or to provide instructions or data to the processing device. It can be embodied in.
  • the software may be distributed over networked computer systems so that they may be stored or executed in a distributed manner.
  • Software and data may be stored on one or more computer readable recording media.
  • the method according to the embodiment may be embodied in the form of program instructions that can be executed by various computer means and recorded in a computer readable medium.
  • the computer readable medium may include program instructions, data files, data structures, etc. alone or in combination.
  • the program instructions recorded on the media may be those specially designed and constructed for the purposes of the embodiments, or they may be of the kind well-known and available to those having skill in the computer software arts.
  • Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tape, optical media such as CD-ROMs, DVDs, and magnetic disks, such as floppy disks.
  • Examples of program instructions include not only machine code generated by a compiler, but also high-level language code that can be executed by a computer using an interpreter or the like.

Abstract

L'invention concerne un procédé et un dispositif permettant de sélectionner une question dans de multiples feuilles de test psychologique sur la base d'un apprentissage automatique pour diagnostiquer rapidement les symptômes d'anxiété et de dépression. Selon un mode de réalisation, un procédé de sélection d'une question dans de multiples feuilles de test psychologique sur la base d'un apprentissage automatique pour diagnostiquer rapidement les symptômes d'anxiété et de dépression peut comprendre : l'entraînement d'un outil de diagnostic interactif basé sur un apprentissage automatique en utilisant des données de recherche de santé mentale en tant qu'ensemble de données d'apprentissage ; et la mise à jour d'informations d'état d'un sujet chaque fois que le sujet répond à l'outil de diagnostic interactif.
PCT/KR2018/007056 2018-05-23 2018-06-22 Procédé et dispositif de sélection d'une question dans de multiples feuilles de test psychologique sur la base d'un apprentissage automatique pour diagnostiquer rapidement les symptômes d'anxiété et de dépression WO2019225798A1 (fr)

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
KR10-2018-0058130 2018-05-23
KR20180058130 2018-05-23
KR10-2018-0071641 2018-06-21
KR1020180071641A KR102111852B1 (ko) 2018-05-23 2018-06-21 다수의 심리검사지에서 불안 및 우울 증세의 신속한 진단을 위한 기계 학습 기반의 문항 선별 방법 및 장치

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CN111312394A (zh) * 2020-01-15 2020-06-19 东北电力大学 一种基于组合情感的心理健康状况评估系统及其处理方法
CN112582061A (zh) * 2020-12-14 2021-03-30 首都医科大学 基于文本问答的抑郁症辅助筛查方法、系统及存储介质

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CN112582061A (zh) * 2020-12-14 2021-03-30 首都医科大学 基于文本问答的抑郁症辅助筛查方法、系统及存储介质

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