标题： 个性化反事实框架：从可穿戴数据生成潜在结果 显示英文标题

标题： Personalized Counterfactual Framework: Generating Potential Outcomes from Wearable Data

摘要： 可穿戴传感器数据为个性化健康监测提供了机会，但从其复杂、长期的数据流中得出可操作的见解是具有挑战性的。 本文介绍了一个框架，用于从多变量可穿戴数据中学习个性化的反事实模型。 这使得可以探索假设情景，以了解生活方式选择的潜在个体特定结果。 我们的方法首先通过多模态相似性分析，将个体数据集与类似患者的数据显示进行增强。 然后我们使用时间PC（Peter-Clark）算法的适应版本来发现预测关系，建模时间t-1的变量如何影响时间t的生理变化。 通过这些发现的关系训练梯度提升机，以量化个体特定的影响。 这些模型驱动一个反事实引擎，在假定干预措施（例如活动或睡眠变化）下预测生理轨迹。 我们通过一步 ahead 预测验证和评估干预措施的合理性和影响来评估该框架。 评估显示合理的预测准确性（例如，平均心率MAE为4.71 bpm）和高的反事实合理性（中位数0.9643）。 至关重要的是，这些干预措施突显了个体对假设生活方式变化的显著个体间差异，表明该框架在个性化见解方面的潜力。 这项工作提供了一个工具，用于探索个性化健康动态并生成关于个体对生活方式变化反应的假设。 显示英文摘要

摘要： 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.