Medical Domain Knowledge Collaborative Graph Learning for Healthcare Event Prediction

Abstract

Electronic health records have become more prevalent worldwide, and with this, the opportunity for more accurate and automated prediction of health events has grown. Such predictions are crucial for providing preventive and proactive healthcare to patients. Although various advanced methods have been explored, they often fail to fully leverage medical domain knowledge, understand interrelations between diseases and patients comprehensively, and efficiently integrate unstructured clinical notes into predictive models. To address these challenges, we propose the Medical Domain Knowledge Collaborative Graph Learning (MED-CGL) model. MED-CGL incorporates external medical knowledge bases to enhance the predictive power of unstructured clinical notes and extracts learnable features from the MIMIC-III health record dataset using medical domain knowledge and collaborative graph learning. We introduce the Enhanced Medical Knowledge Integration (EMKI) module, which employs a novel attention mechanism to connect clinical notes with disease descriptions precisely. It also enhances the system's performance by integrating medical knowledge from the semantically labelled knowledge-enhanced (SLAKE) dataset during the training phase. Furthermore, our model considers the complexities of unstructured clinical notes, providing a nuanced perspective on the interplay between diseases and patient profiles. Our experiments show that the MED-CGL model exhibited outstanding performance in diagnosis prediction, achieving an F1 score of 27.32%, and in heart failure prediction, where it attained an accuracy of 91.39%. This significant improvement demonstrates the robustness and effectiveness of our model, which is further supported by our in-depth ablation study.