Collaborative Graph Learning with Auxiliary Text for Temporal Event Prediction in Healthcare
This work addresses the problem of improving health event predictions for healthcare providers, but it appears incremental as it builds on existing graph learning methods with text integration.
The paper tackles the challenge of accurately predicting temporal health events by addressing issues like utilizing disease domain knowledge, collaborative learning of patient-disease representations, and incorporating unstructured text, resulting in competitive performance compared to state-of-the-art models on two healthcare problems.
Accurate and explainable health event predictions are becoming crucial for healthcare providers to develop care plans for patients. The availability of electronic health records (EHR) has enabled machine learning advances in providing these predictions. However, many deep learning based methods are not satisfactory in solving several key challenges: 1) effectively utilizing disease domain knowledge; 2) collaboratively learning representations of patients and diseases; and 3) incorporating unstructured text. To address these issues, we propose a collaborative graph learning model to explore patient-disease interactions and medical domain knowledge. Our solution is able to capture structural features of both patients and diseases. The proposed model also utilizes unstructured text data by employing an attention regulation strategy and then integrates attentive text features into a sequential learning process. We conduct extensive experiments on two important healthcare problems to show the competitive prediction performance of the proposed method compared with various state-of-the-art models. We also confirm the effectiveness of learned representations and model interpretability by a set of ablation and case studies.