Scalable assessments of mental illness remain a critical roadblock toward accessible and equitable care. Here, we show that everyday human-computer interactions encode mental health with biomarker accuracy. We introduce MAILA, a MAchine-learning framework for Inferring Latent mental states from digital Activity. We trained MAILA on 18,200 cursor and touchscreen recordings labelled with 1.3 million mental-health self-reports collected from 9,500 participants. MAILA tracks dynamic mental states along 13 clinically relevant dimensions, resolves circadian fluctuations and experimental manipulations of arousal and valence, achieves near-ceiling accuracy at the group level, and captures information about mental health that is only partially reflected in verbal self-report. By extracting signatures of psychological function that have so far remained untapped, MAILA establishes human-computer interactions as a new modality for scalable digital phenotyping of mental health.

翻译：精神疾病的可扩展评估仍然是实现可及且公平护理的关键障碍。本文证明，日常人机交互能以生物标志物般的精度编码心理健康信息。我们提出MAILA，一种从数字活动中推断潜在心理状态的机器学习框架。我们在来自9500名参与者的18,200个光标和触摸屏记录上训练MAILA，这些记录附有130万份心理健康自评报告。MAILA可沿13个临床相关维度追踪动态心理状态，解析昼夜节律波动及对唤醒度和效价的实验操作，在群体水平达到近天花板精度，并能捕获仅部分反映在言语自评中的心理健康信息。通过提取迄今为止尚未被开发的心理学功能特征，MAILA将人机交互确立为一种可扩展的心理健康数字表型新模态。