Crowd-Powered Machine Learning to Diagnose ASD and ADHD in Adolescents from Digital Social Interactions

众包机器学习通过数字社交互动诊断青少年 ASD 和 ADHD

• 批准号: 10682965
• 负责人: Peter Washington
• 金额: $ 130.95万
• 依托单位: UNIVERSITY OF HAWAII AT MANOA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-21 至 2026-09-20
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10682965
• 关键词: Adolescent; Algorithms; Attention deficit hyperactivity disorder; Autism Diagnosis; Behavior; Behavioral; Categories; Characteristics; Childhood; Classification; Clinical; Complex; Coupled; Crowding; Data; Data Collection; Devices; Diagnosis; Diagnostic; Evaluation; Eye; Face; Family; Future; Health Insurance; Healthcare; Healthcare Systems; Hyperactivity; Individual; Internet; Label; Left; Machine Learning; Metadata; Output; Participant; Performance; Play; Population; Process; Psychiatry; Public Health; Research; Running; Social Behavior; Social Interaction; Specificity; Speech; Structure; Testing; Training; Underserved Population; Waiting Lists; Work; autism spectrum disorder; barrier to care; comorbidity; crowdsourcing; data curation; deep learning; deep learning model; digital; digital technology; feature extraction; gaze; innovation; machine learning algorithm; microphone; neuropsychiatry; novel; personalized diagnostics; remote interaction; showing emotion; social; structured data; tool; vector

Project Summary Digital technologies have the potential to provide remote and accessible psychiatric diagnostics to underserved families who have traditionally been left out of the healthcare system. Several recent research efforts have explored the use of structured behavioral data collection using digital devices coupled with automatic machine learning (ML) algorithms to distinguish a particular psychiatric condition from neurotypical controls. While these pure ML approaches have achieved performances >90% on balanced classification metrics on binary prediction tasks, there are limits to their ability to feasibly quantify the complex, social behavioral features needed for multi- condition and higher precision diagnostics. To enable such specificity for digital psychiatric diagnostics, I propose a novel paradigm-shifting approach which incorporates original crowdsourcing algorithms into the ML feature extraction process to create representation vectors of nuanced social behaviors with sufficient discriminative power to distinguish related and overlapping neuropsychiatric conditions such as Autism Spectrum Disorder and Attention-Deficit/Hyperactivity Disorder. Crowdsourcing, or the use of distributed workers to collectively work towards a larger task, is traditionally used to label training data for ML and is increasingly leveraged as a tool to run public health studies. However, crowdsourcing has yet to be thoroughly explored as a central tool in precision diagnostics for psychiatry. In the proposed paradigm, each crowd worker will answer targeted multiple choice questions about each video, reducing the feature space into a socially rich feature vector corresponding to the behaviors displayed in the video. My innovative crowdsourcing framework involves creating a quantified profile of each crowd worker to dynamically assign them to labeling tasks based on the categories of questions they rate in accordance with clinical experts. This crowdsourcing pipeline will be tested with respect to 3 major components of the digital diagnostics pipeline: (1) gamified structured video data curation from each participant, (2) behavioral feature extraction, and (3) diagnostic prediction with deep learning. The structured data collection will occur through paired social interactions between participants who remotely interact by playing social games on the web while their webcam and microphone record their behaviors. Crowd workers will watch the videos and answer multiple choice questions pertaining to the subject’s behavior in the video. The crowd annotations and metadata will be supplemented with computationally extracted eye gaze, facial emotion expression, vocal pitch, and speech timing features. These features will be collectively used to train a deep learning model which outputs both diagnostic categories and indicators of the presence of individual behavioral characteristics (e.g., hyperactivity and distractibility). Based on crowd labels of earlier games, additional games will be assigned to each subject in future data curation sessions. This paradigm has the potential to enable more nuanced behavior- based digital diagnostics, working towards a clinical workflow which can provide remote access to diagnoses for underserved populations who typically struggle to obtain neuropsychiatric healthcare.

项目总结