用可穿戴数据生成个人化假设场景，预测生活方式改变的生理影响。
Personalized Counterfactual Framework: Generating Potential Outcomes from Wearable Data
- 通过相似患者数据增强个体数据，再用时序算法挖掘变量影响关系。
- 预测心率误差仅4.71 bpm，假设干预结果可信度达0.9643中位数。
- 适合个性化健康管理、慢性病预防及临床研究中的个体化决策支持。
可穿戴传感器数据为个性化健康监测提供了可能，但从复杂的长期数据流中提取可操作洞察仍具挑战。本文提出一种框架，从多变量可穿戴数据中学习个性化反事实模型，以探索假设性场景下个体特定的潜在结果。方法首先通过多模态相似性分析，将相似患者的数据增强到个体数据集；接着采用时序PC（Peter-Clark）算法变体发现预测关系，建模时间t-1的变量如何影响时间t的生理变化；再基于这些关系训练梯度提升机（Gradient Boosting Machines），量化个体特异性效应。该模型驱动反事实引擎，可预测在假设干预（如活动或睡眠改变）下的生理轨迹。评估采用一步前预测验证及干预合理性与影响评估。结果显示合理预测精度（如平均心率MAE 4.71 bpm）和高反事实可信度（中位数0.9643）。关键发现：不同个体对假设生活方式改变的响应差异显著，凸显该框架在个性化健康洞察中的潜力。本工作提供了一种探索个体健康动态并生成生活方式响应假说的工具。
原文摘要 · Abstract (English)
Wearable sensor data offer opportunities for personalized health monitoring, yet deriving actionable insights from their complex, longitudinal data streams is challenging. This paper introduces a framework to learn personalized counterfactual models from multivariate wearable data. This enables exploring what-if scenarios to understand potential individual-specific outcomes of lifestyle choices. Our approach first augments individual datasets with data from similar patients via multi-modal similarity analysis. We then use a temporal PC (Peter-Clark) algorithm adaptation to discover predictive relationships, modeling how variables at time t-1 influence physiological changes at time t. Gradient Boosting Machines are trained on these discovered relationships to quantify individual-specific effects. These models drive a counterfactual engine projecting physiological trajectories under hypothetical interventions (e.g., activity or sleep changes). We evaluate the framework via one-step-ahead predictive validation and by assessing the plausibility and impact of interventions. Evaluation showed reasonable predictive accuracy (e.g., mean heart rate MAE 4.71 bpm) and high counterfactual plausibility (median 0.9643). Crucially, these interventions highlighted significant inter-individual variability in response to hypothetical lifestyle changes, showing the framework's potential for personalized insights. This work provides a tool to explore personalized health dynamics and generate hypotheses on individual responses to lifestyle changes.
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