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

项目摘要 数字技术有可能为未得到充分服务的人提供远程和可访问的精神诊断 传统上被排除在医疗体系之外的家庭。最近的几项研究工作已经 探索了使用数字设备和自动机器进行结构化行为数据收集的使用 学习(ML)算法，以区分特定的精神疾病和神经典型对照。而这些 纯最大似然方法在二元预测的平衡分类度量上取得了90%的性能 任务，他们对多任务所需的复杂的社会行为特征进行可行量化的能力是有限的。 状态和更高的诊断精度。为了实现数字精神病学诊断的这种特异性，我建议 一种将原始众包算法整合到ML特征中的新范式转换方法 创建具有足够区分度的细微差别社会行为表示向量的提取过程 区分相关和重叠的神经精神疾病的能力，如自闭症谱系障碍和 注意力缺陷/多动障碍。众包，或使用分散的工作人员来集体工作 面向更大的任务，传统上用于标记ML的训练数据，并越来越多地被用作 开展公共卫生研究。然而，众包作为精确度的核心工具还没有得到彻底的探索。 精神病学的诊断学。在建议的范例中，每个群组工作人员将回答目标多项选择 关于每个视频的问题，将特征空间约简为对应于 视频中显示的行为。我的创新众包框架包括创建一个量化的配置文件 根据他们提出的问题类别，动态地将他们分配给标注任务 评分与临床专家一致。这一众包渠道将针对3个主要项目进行测试 数字诊断流水线的组件：(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；(3)深度学习的诊断预测。结构化数据集合 将通过参与者之间的配对社交交互发生，这些参与者通过玩社交游戏进行远程交互 在网上，当他们的网络摄像头和麦克风记录他们的行为时。群众工作人员将观看视频并 回答与视频中受试者行为有关的多项选择题。群组注释和 元数据将由通过计算提取的眼睛凝视、面部情感表情、声调 和语音定时特征。这些特征将共同用于训练深度学习模型，该模型输出 诊断类别和个体行为特征的存在的指示符(例如， 多动症和注意力不集中)。根据早前游戏的人群标签，额外的游戏将分配到 在未来的数据管理会议中，每个主题。这种模式有可能实现更细微的行为-- 基于数字诊断，致力于临床工作流，可以远程访问诊断 服务不足的人群，他们通常难以获得神经精神卫生保健。

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.