Mood disorders (MDs) are among the leading causes of disease burden worldwide. Limited specialized care availability remains a major bottleneck thus hindering pre-emptive interventions. MDs manifest with changes in mood, sleep, and motor activity, observable in ecological physiological recordings thanks to recent advances in wearable technology. Therefore, near-continuous and passive collection of physiological data from wearables in daily life, analyzable with machine learning (ML), could mitigate this problem, bringing MDs monitoring outside the clinician’s office. Previous works predict a single label, either the disease state or a psychometric scale total score. However, clinical practice suggests that the same label may underlie different symptom profiles, requiring specific treatments. Here we bridge this gap by proposing a new task: inferring all items in HDRS and YMRS, the two most widely used standardized scales for assessing MDs symptoms, using physiological data from wearables. To that end, we develop a deep learning pipeline to score the symptoms of a large cohort of MD patients and show that agreement between predictions and assessments by an expert clinician is clinically significant (quadratic Cohen’s κ and macro-average F1 score both of 0.609). While doing so, we investigate several solutions to the ML challenges associated with this task, including multi-task learning, class imbalance, ordinal target variables, and subject-invariant representations. Lastly, we illustrate the importance of testing on out-of-distribution samples.

情绪障碍（MD）是全球疾病负担的主要原因之一。有限的专业护理服务仍然是一个主要瓶颈，从而阻碍了先发制人的干预措施。由于可穿戴技术的最新进展，MD 表现为情绪、睡眠和运动活动的变化，可以在生态生理记录中观察到。因此，从日常生活中的可穿戴设备中近乎连续、被动地收集生理数据，并通过机器学习 (ML) 进行分析，可以缓解这个问题，将 MD 监测带到临床医生办公室之外。以前的工作预测单个标签，要么是疾病状态，要么是心理测量量表总分。然而，临床实践表明，相同的标签可能导致不同的症状特征，需要特定的治疗。在这里，我们提出一项新任务来弥补这一差距：使用可穿戴设备的生理数据来推断 HDRS 和 YMRS（这两种最广泛使用的用于评估 MD 症状的标准化量表）中的所有项目。为此，我们开发了一个深度学习流程，对大量 MD 患者的症状进行评分，并表明专家临床医生的预测和评估之间的一致性具有临床意义（二次 Cohen κ 和宏观平均 F1 评分均为 0.609） 。在此过程中，我们研究了与此任务相关的 ML 挑战的几种解决方案，包括多任务学习、类不平衡、序数目标变量和主题不变表示。最后，我们说明了对分布外样本进行测试的重要性。