AI助手从可穿戴设备数据中自动筛选出与心理和代谢健康相关的生物标志物。
An AI Co-Data-Scientist for Prioritizing Candidate Biomarkers from Wearable Sensor Data
- 多智能体协作生成假设，结合统计检验与文献验证。
- 发现睡眠波动与抑郁相关，步数/静息心率比值关联胰岛素抵抗。
- 临床医生认可其优先级判断，适合医疗研究团队使用。
可穿戴设备持续产生生理与行为数据，但将其转化为临床可用的生物标志物假设仍需大量人工工作。我们提出 CoDaS——一种在人类监督下集成多智能体假设生成、确定性统计分析、对抗性验证及文献引导解释的 AI 协作数据科学家。在包含 9,279 名参与者观测的三个可穿戴数据队列中，CoDaS 经内部复制性、稳定性、鲁棒性与泄漏检测后，优先筛选出与心理健康和代谢终点相关的候选关联。系统发现与抑郁症相关的昼夜节律不稳定性信号，包括 DWB 中的睡眠时长变异（ρ = 0.252，p < 0.001）和 GLOBEM 中的入睡时间变异（ρ = 0.126，p < 0.001），并推导出与胰岛素抵抗相关的可穿戴心血管健康指数（步数/静息心率；ρ = -0.374，p < 0.001）。将这些特征加入人口学模型后，抑郁预测提升 ΔR² = 0.040，胰岛素抵抗预测提升 ΔR² = 0.021。在 12 名临床医生参与的约 25 小时评审中，临床医生对有效性判断与 CoDaS 的置信等级一致（ρ = 0.67，p = 0.005），但对附加临床价值和行动信心评分较低。CoDaS 支持可追溯、可生成假设的可穿戴候选生物标志物优先排序。
原文摘要 · Abstract (English)
Wearable devices generate continuous physiological and behavioral data, but converting these signals into clinically reviewable biomarker hypotheses remains labor-intensive. We introduce CoDaS, an AI co-data-scientist that integrates multi-agent hypothesis generation, deterministic statistical analysis, adversarial validation and literature-grounded interpretation under human oversight. Across three wearable cohorts comprising 9,279 participant-observations, CoDaS prioritized candidate associations for mental-health and metabolic endpoints after internal checks for replication, stability, robustness and leakage. The system identified related circadian-instability signals associated with depression, including sleep-duration variability in DWB ($ρ$ = 0.252, $p$ < 0.001) and sleep-onset variability in GLOBEM ($ρ$ = 0.126, $p$ < 0.001), and derived a wearable cardiovascular-fitness index associated with insulin resistance (steps/resting heart rate; $ρ$ = -0.374, $p$ < 0.001). Adding these features to demographic models produced modest gains ($ΔR^2$ = 0.040 for depression, 0.021 for insulin resistance). In a 12-clinician review totaling approximately 25 active hours, clinician validity judgments aligned with CoDaS confidence tiers ($ρ$ = 0.67, $p$ = 0.005), whereas added clinical value and confidence to act were rated lower. CoDaS supports traceable, hypothesis-generating prioritization of wearable candidate biomarkers.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。