Symptom‐Driven Prediction of Chronic Multimorbidity with a Macro–Micro Heterogeneous Graph Neural Network
Year: 2026
Type: article
Abstract: 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.
Source: Advanced Science
DOI: 10.1002/advs.78348
Institutions: Guangzhou University of Chinese Medicine, State Administration of Traditional Chinese Medicine of the People's Republic of China, Jinan University, Shandong Management University, Guangdong University Of Finances and Economics, Guangdong University of Finance, Xidian University, Shandong University of Traditional Chinese Medicine, Traditional Chinese Medicine Bureau of Guangdong Province, Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Linyi People's Hospital
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $6,730
OpenAlex ID: W7222881124