Medical Test-free Disease Detection Based on Big Data
To address the high cost and limited scalability of clinical laboratory testing—particularly for screening hundreds to thousands of diseases—this paper proposes CLDD, a Graph Neural Collaborative Learning model for disease detection. CLDD reformulates disease detection as an adaptive collaborative learning task, jointly modeling disease–disease associations and patient–patient similarities, thereby eliminating reliance on disease-specific diagnostic tests. The model integrates heterogeneous features—including patient–disease interactions and demographic attributes—from electronic health records (EHRs) and employs a graph neural network for collaborative representation learning. Additionally, it incorporates an interpretable ranking mechanism to support clinical decision-making. Evaluated on the MIMIC-IV dataset (61,191 patients, 2,000 diseases), CLDD achieves absolute improvements of 6.33% in recall and 7.63% in precision over state-of-the-art baselines. It further demonstrates strong capability in recovering masked diseases and provides clinically meaningful, interpretable predictions.