用一夜睡眠数据，用AI判断是否吃抗抑郁药。
Transformer Model Detects Antidepressant Use From a Single Night of Sleep, Unlocking an Adherence Biomarker
- 用Transformer模型分析可穿戴设备睡眠数据
- 准确率达AUROC 0.84，跨药物类型有效
- 适合远程监测用药依从性，无需抽血
抗抑郁药不依从普遍存在，导致复发、住院、自杀风险上升及数十亿美元的可避免支出。临床亟需快速发现用药中断的工具，但现有方法或侵入性强（血检、神经影像），或依赖代理指标且不准（数药片、药店配药记录）。本文提出首个非侵入式生物标志物，仅凭一晚睡眠数据即可检测抗抑郁药服用情况。基于Transformer的模型分析消费级可穿戴设备或无接触无线传感器采集的睡眠数据，实现居家、无感、每日依从性评估。在六组数据集上，涵盖超过2万名参与者、6.2万晚睡眠记录（含1800名用药者），该标志物取得AUROC 0.84的性能，对不同药物类别具有泛化能力，剂量敏感，且不受合并精神类药物影响。纵向监测成功捕捉真实世界中的用药启动、减量和中断。该方法为客观、可扩展的依从性监控提供可能，有望改善抑郁症诊疗与预后。
原文摘要 · Abstract (English)
Antidepressant nonadherence is pervasive, driving relapse, hospitalization, suicide risk, and billions in avoidable costs. Clinicians need tools that detect adherence lapses promptly, yet current methods are either invasive (serum assays, neuroimaging) or proxy-based and inaccurate (pill counts, pharmacy refills). We present the first noninvasive biomarker that detects antidepressant intake from a single night of sleep. A transformer-based model analyzes sleep data from a consumer wearable or contactless wireless sensor to infer antidepressant intake, enabling remote, effortless, daily adherence assessment at home. Across six datasets comprising 62,000 nights from >20,000 participants (1,800 antidepressant users), the biomarker achieved AUROC = 0.84, generalized across drug classes, scaled with dose, and remained robust to concomitant psychotropics. Longitudinal monitoring captured real-world initiation, tapering, and lapses. This approach offers objective, scalable adherence surveillance with potential to improve depression care and outcomes.
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