LSTM, TCN, and TFT predict sleep disorders from a 400-patient dataset with reported accuracies between 85 and 93 percent, and SHAP, temporal attention, and counterfactuals are offered as interpretability aids.
Temporal fusion transformers for interpretable multi-horizon time series forecasting,
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Adopting Trustworthy AI for Sleep Disorder Prediction: Deep Time Series Analysis with Temporal Attention Mechanism and Counterfactual Explanations
LSTM, TCN, and TFT predict sleep disorders from a 400-patient dataset with reported accuracies between 85 and 93 percent, and SHAP, temporal attention, and counterfactuals are offered as interpretability aids.