Adopting Trustworthy AI for Sleep Disorder Prediction: Deep Time Series Analysis with Temporal Attention Mechanism and Counterfactual Explanations

TL;DR

This paper presents a trustworthy AI framework for sleep disorder prediction using deep time series models enhanced with explainability techniques like temporal attention and counterfactual explanations, aiming for accurate and interpretable results.

Contribution

It introduces a novel combination of deep time series models with explainability methods for sleep disorder prediction, improving trustworthiness and interpretability.

Findings

Effective sleep disorder prediction demonstrated on large dataset

Temporal attention improves model interpretability

Counterfactual explanations enhance trust in predictions

Abstract

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.