基于LLM引导概念整合的移动感知数据可解释预测
Explainable Prediction from Mobile Sensing Data through LLM-guided Concept Integration
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- University of Turku（图尔库大学）
- University of California, Irvine（加利福尼亚大学尔湾分校）
机构由 AI 辅助整理，请以论文原文为准。
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中文总结 AI 辅助
针对小规模移动感知研究中预测难和可解释性差的问题，提出概念整合Transformer（CIT），利用大语言模型生成概念监督，在两个数据集上取得最优F1分数，并揭示可解释的行为生理模式。
中文摘要 AI 辅助
移动感知技术能够在日常环境中对行为和生理模式进行纵向监测。然而，在小规模队列健康感知研究中，由于任务特定的结果监督相对于异质性感知数据而言较为有限，准确预测仍然具有挑战性。可解释性同样重要，因为模型输出应反映有意义的行为和生理模式，而不仅仅是预测分数。我们开发了一种概念整合Transformer（CIT），结合LLM引导的概念监督，用于从移动感知数据中进行可解释预测。CIT利用预训练的大语言模型生成具有置信权重的基线感知概念异常目标，无需手动概念标注。在两个纵向数据集上，CIT在AFFECT数据集上取得了最高的F1分数（0.756），并在PHQ-9数据集上并列最高（0.765）。学习到的概念分数还揭示了可解释的行为和生理模式；在AFFECT中，睡眠数量和睡眠质量在高负面情绪组与低负面情绪组之间表现出最明显的差异。这些发现支持LLM引导的概念整合在小规模队列移动感知研究中实现准确且可解释的预测。
英文摘要
Mobile sensing enables longitudinal monitoring of behavioral and physiological patterns in everyday settings. However, accurate prediction remains challenging in small-cohort health-sensing studies, where task-specific outcome supervision is limited relative to heterogeneous sensing data. Interpretability is also important, as model outputs should reflect meaningful behavioral and physiological patterns rather than predictive scores alone. We develop a Concept-Integrated Transformer (CIT) with LLM-guided concept supervision for explainable prediction from mobile sensing data. CIT uses a pretrained large language model to generate baseline-aware concept abnormality targets with confidence weights without manual concept annotation. Across two longitudinal datasets, CIT achieves the highest F1 score on AFFECT (0.756) and ties for the highest on a PHQ-9 dataset (0.765). The learned concept scores also reveal interpretable behavioral and physiological patterns; in AFFECT, sleep quantity and quality show the clearest difference between high and low negative affect groups. These findings support LLM-guided concept integration for accurate and interpretable prediction in small-cohort mobile sensing studies.
