Symptom‐Driven Prediction of Chronic Multimorbidity with a Macro–Micro Heterogeneous Graph Neural Network

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Abstract
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.
Funding Information
  • National Natural Science Foundation of China (72301082)
  • National Natural Science Foundation of China (82574958)
  • China Postdoctoral Science Foundation (2025M773863)

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