采用可信人工智能进行睡眠障碍预测：基于时间注意力机制与反事实解释的深度时间序列分析
Adopting Trustworthy AI for Sleep Disorder Prediction: Deep Time Series Analysis with Temporal Attention Mechanism and Counterfactual Explanations
AI总结：
本文提出结合TCN、LSTM和TFT三种深度时间序列模型，并引入时间注意力机制与SHAP反事实解释，在大规模睡眠健康数据上实现可信、可解释的睡眠障碍预测。
摘要：
睡眠障碍对生活方式和健康都有重大影响。从生活方式和生理数据中有效预测睡眠障碍可以为早期干预提供关键信息。本研究利用三种深度时间序列模型，并结合可解释性方法进行睡眠障碍预测。具体而言，我们的方法采用时间卷积网络（TCN）、长短期记忆网络（LSTM）进行时间序列数据分析，以及时间融合Transformer模型（TFT）。同时，采用时间注意力机制以及基于SHapley Additive exPlanations（SHAP）方法的反事实解释，以确保预测的可靠性、准确性和可解释性。最后，利用一个大规模睡眠健康测量数据集，我们的评估证明了该方法在预测睡眠障碍方面的效果。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
