Symptom-Driven Prediction of Chronic Multimorbidity with a Macro-Micro Heterogeneous Graph Neural Network.

The growing prevalence and complexity of multimorbidity pose significant challenges to healthcare systems worldwide. Here, we developed a heterogeneous graph neural network framework for multilabel multimorbidity prediction. The framework jointly encodes symptom and diagnostic information within a heterogeneous graph and incorporates biomedical knowledge subgraphs on both the disease and symptom sides. By integrating macro-level clinical representations of symptoms and diseases with micro-level molecular knowledge, the model generates multilabel probabilities across candidate disease categories. We evaluated the framework in two complementary tasks: Patient-Level Symptom-Disease Association (Task 1) and Visit-Level Multimorbidity Completion (Task 2). In Task 1, symptoms recorded across all eligible visits within each patient's observation window were aggregated into a patient-level symptom set to predict the disease categories recorded for that patient within the same observation window. In Task 2, symptoms and a subset of recorded positive diagnoses from the same visit were used to predict the remaining disease categories recorded for that visit. Together, these two tasks provide a unified framework for patient-level symptom-disease association and visit-level multimorbidity completion. The framework characterized symptom-disease-biomedical knowledge associations and non-random patterns of disease co-occurrence, while supporting both patient-level symptom-disease association and visit-level multimorbidity completion.