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

项目摘要 数字技术有可能为服务不足的人提供远程和可访问的精神病诊断 传统上被排除在医疗体系之外的家庭。最近的几项研究成果 探索使用数字设备结合自动机器的结构化行为数据收集 学习（ML）算法来区分特定的精神病状况与神经典型对照。虽然这些 纯ML方法在二元预测的平衡分类度量上实现了>90%的性能 任务，他们的能力是有限的，可行的量化复杂的，社会行为特征所需的多， 更高的诊断精度。为了实现数字精神病诊断的这种特异性，我建议 一种新的范式转换方法，将原始的众包算法融入ML功能 提取过程，以创建具有足够区分性的细微差别的社会行为的表示向量 区分相关和重叠的神经精神疾病的能力，如自闭症谱系障碍， 注意力缺陷/多动障碍。众包，或使用分布式工作人员集体工作 面向更大的任务，传统上用于标记ML的训练数据，并且越来越多地被用作工具， 进行公共卫生研究然而，众包还没有被彻底探索，作为一个核心工具，在精度 精神病学的诊断在所提出的范例中，每个人群工作者将回答有针对性的多项选择 关于每个视频的问题，将特征空间减少到对应于每个视频的社交丰富特征向量。 视频中的行为。我的创新众包框架包括创建一个量化的个人资料， 每个群组工作人员的动态分配他们的标签任务的基础上，他们的问题类别， 根据临床专家的评分。该众包管道将针对3个主要 数字诊断流水线的组成部分：（1）来自每个参与者的游戏化结构化视频数据管理， (2)行为特征提取，以及（3）深度学习的诊断预测。结构化数据收集 将通过参与者之间的配对社交互动发生，这些参与者通过玩社交游戏进行远程互动 而他们的网络摄像头和麦克风记录他们的行为。人群工作人员将观看视频， 回答与视频中受试者行为有关的多项选择题。人群注释和 元数据将用计算提取的眼睛注视，面部情感表达，音调， 和语音定时特征。这些特征将共同用于训练深度学习模型， 诊断类别和个体行为特征存在的指示符（例如， 多动和注意力分散）。根据早期游戏的人群标签，额外的游戏将被分配到 每个主题在未来的数据策展会议。这种模式有可能实现更微妙的行为- 基于数字诊断，致力于临床工作流程，可以提供远程访问诊断， 通常难以获得神经精神保健的服务不足的人群。

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.