Explainable AI Based Multi-Task Learning Method for Stroke Prognosis

Abstract

Predicting the health status of stroke patients at different stages of the disease is a critical clinical task. The onset and development of stroke are affected by an array of factors, encompassing genetic predisposition, environmental exposure, unhealthy lifestyle habits, and existing medical conditions. Although existing machine learning-based methods for predicting stroke patients’ health status have made significant progress, limitations remain in terms of prediction accuracy, model explainability, and system optimization. This paper proposes a multi-task learning approach based on Explainable Artificial Intelligence (XAI) for predicting the health status of stroke patients. First, we design a comprehensive multi-task learning framework that utilizes the task correlation of predicting various health status indicators in patients, enabling the parallel prediction of multiple health indicators. Second, we develop a multi-task Area Under Curve (AUC) optimization algorithm based on adaptive low-rank representation, which removes irrelevant information from the model structure to enhance the performance of multi-task AUC optimization. Additionally, the model’s explainability is analyzed through the stability analysis of SHAP values. Experimental results demonstrate that our approach outperforms comparison algorithms in key prognostic metrics F1 score and Efficiency.