Optimized Affective Computing Measures of Social Processes and Negative Valence in Youth Psychopathology

青年精神病理学中社会过程和负价的优化情感计算措施

基本信息

  • 批准号:
    10594051
  • 负责人:
  • 金额:
    $ 75.93万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2021
  • 资助国家:
    美国
  • 起止时间:
    2021-04-02 至 2026-01-31
  • 项目状态:
    未结题

项目摘要

ABSTRACT Difficulties with emotion expression and social behavior characterize multiple psychiatric conditions and negatively impact child development. However, existing measurement tools for indexing social-emotional function are imprecise and subjective, or require specialized training that is costly and time-intensive, prohibiting widespread implementation. The imprecision of existing tools has a major negative impact not only on research, but on the ability to assess and treat individuals with mental health concerns – especially among underserved and under-resourced populations. Here, we propose to address this problem by quantifying social and emotional behavior using novel biobehavioral markers derived from computer vision (facial expression analysis) and computational linguistics (social/sentiment analysis). Our team has successfully used these markers to predict the presence of autism spectrum disorder (ASD) with 91% accuracy. In this proposal, we determine the extent to which our markers can serve as continuous measures of social behavior and negative emotion to advance clinical phenotyping and interventions. The proposal brings together two high-bandwidth clinical research programs at the Children’s Hospital of Philadelphia and Baylor College of Medicine to collect data on 750 adolescents (ages 12-17 inclusive) with ASD, a primary anxiety or depressive disorder, or without any developmental/psychiatric condition. At a single assessment, all youth will participate in an extensive clinical phenotyping battery consisting of validated clinical interviews and child-/parent-report scales assessing converging and diverging mental health constructs, and three tasks eliciting positive/negative emotion, social stress, and mild frustration. A subsample of 150 adolescents will be reassessed 6-10 weeks later to allow retest/stability analyses. A novel camera apparatus will capture naturalistic synchronized verbal and nonverbal signals from dyads. Our analytic approach combines state-of-the-art machine learning, computational linguistics, and computer vision – including facial emotion recognition methods that rival several commonly used alternatives. The ultimate goal of this proposal is to develop valid and objective measures of the Social and Negative Valence Systems using novel biobehavioral markers in a large transdiagnostic sample of youth. Secondary goals are to develop easy-to-follow methods to widely disseminate our tools and procedures, and to characterize individual variability in these key RDoC metrics by age, gender, race/ethnicity, and diagnosis. The achievement of these goals will provide researchers with sorely needed measures of social and emotional behavior, and provide clinicians with a new set of tools for identifying and tracking youth in need of mental health treatment.
摘要

项目成果

期刊论文数量(0)
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JOHN David HERRINGTON其他文献

JOHN David HERRINGTON的其他文献

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{{ truncateString('JOHN David HERRINGTON', 18)}}的其他基金

Ethical Perspectives Towards Using Smart Contracts for Patient Consent and Data Protection of Digital Phenotype Data in Machine Learning Environments
在机器学习环境中使用智能合约获得患者同意和数字表型数据数据保护的伦理视角
  • 批准号:
    10599498
  • 财政年份:
    2022
  • 资助金额:
    $ 75.93万
  • 项目类别:
Enhancing the Cloud-Readiness of Perceptual Computing Through Data Standardization Software
通过数据标准化软件增强感知计算的云就绪性
  • 批准号:
    10609245
  • 财政年份:
    2022
  • 资助金额:
    $ 75.93万
  • 项目类别:
Ethical and Human Factors Impacting Successful Translation of Perceptual Computing to Improve Clinical Care
影响感知计算成功转化以改善临床护理的伦理和人为因素
  • 批准号:
    10680488
  • 财政年份:
    2022
  • 资助金额:
    $ 75.93万
  • 项目类别:
Ethical and Human Factors Impacting Successful Translation of Perceptual Computing to Improve Clinical Care
影响感知计算成功转化以改善临床护理的伦理和人为因素
  • 批准号:
    10502082
  • 财政年份:
    2022
  • 资助金额:
    $ 75.93万
  • 项目类别:
Optimized Affective Computing Measures of Social Processes and Negative Valence in Youth Psychopathology
青年精神病理学中社会过程和负价的优化情感计算措施
  • 批准号:
    10183399
  • 财政年份:
    2021
  • 资助金额:
    $ 75.93万
  • 项目类别:
Optimized Affective Computing Measures of Social Processes and Negative Valence in Youth Psychopathology
青年精神病理学中社会过程和负价的优化情感计算措施
  • 批准号:
    10382366
  • 财政年份:
    2021
  • 资助金额:
    $ 75.93万
  • 项目类别:

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