用时序注意力与反事实解释提升睡眠障碍预测的准确性与可信度
Adopting Trustworthy AI for Sleep Disorder Prediction: Deep Time Series Analysis with Temporal Attention Mechanism and Counterfactual Explanations
- 结合TCN、LSTM与TFT三种时序模型进行睡眠数据建模
- 引入注意力机制与SHAP反事实解释，提升预测可解释性
- 适用于医疗健康领域需高可信度决策的睡眠障碍筛查场景
睡眠障碍对生活方式与健康有重大影响。通过生活方式和生理数据有效预测睡眠障碍，可为早期干预提供关键信息。本研究采用三种深度时序模型，并结合可解释性方法进行睡眠障碍预测。具体而言，方法融合了时序卷积网络（TCN）、长短期记忆网络（LSTM）和时序融合变换器（TFT）进行时序数据分析，同时引入时间注意力机制与基于SHapley加性解释（SHAP）的反事实解释，以确保预测结果可靠、准确且可解释。最终，基于大规模睡眠健康数据集的评估表明，该方法在睡眠障碍预测中具有显著效果。
原文摘要 · Abstract (English)
Sleep disorders have a major impact on both lifestyle and health. Effective sleep disorder prediction from lifestyle and physiological data can provide essential details for early intervention. This research utilizes three deep time series models and facilitates them with explainability approaches for sleep disorder prediction. Specifically, our approach adopts Temporal Convolutional Networks (TCN), Long Short-Term Memory (LSTM) for time series data analysis, and Temporal Fusion Transformer model (TFT). Meanwhile, the temporal attention mechanism and counterfactual explanation with SHapley Additive exPlanations (SHAP) approach are employed to ensure dependable, accurate, and interpretable predictions. Finally, using a large dataset of sleep health measures, our evaluation demonstrates the effect of our method in predicting sleep disorders.
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。