Medical Test-free Disease Detection Based on Big Data

2025-11-26

AI Summary

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.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Accurate disease detection is of paramount importance for effective medical treatment and patient care. However, the process of disease detection is often associated with extensive medical testing and considerable costs, making it impractical to perform all possible medical tests on a patient to diagnose or predict hundreds or thousands of diseases. In this work, we propose Collaborative Learning for Disease Detection (CLDD), a novel graph-based deep learning model that formulates disease detection as a collaborative learning task by exploiting associations among diseases and similarities among patients adaptively. CLDD integrates patient-disease interactions and demographic features from electronic health records to detect hundreds or thousands of diseases for every patient, with little to no reliance on the corresponding medical tests. Extensive experiments on a processed version of the MIMIC-IV dataset comprising 61,191 patients and 2,000 diseases demonstrate that CLDD consistently outperforms representative baselines across multiple metrics, achieving a 6.33% improvement in recall and 7.63% improvement in precision. Furthermore, case studies on individual patients illustrate that CLDD can successfully recover masked diseases within its top-ranked predictions, demonstrating both interpretability and reliability in disease prediction. By reducing diagnostic costs and improving accessibility, CLDD holds promise for large-scale disease screening and social health security.

Problem

• Detects diseases without extensive medical testing
• Uses patient data and disease associations for prediction
• Improves accuracy and reduces diagnostic costs

Innovation

• Graph-based deep learning model for disease detection
• Collaborative learning using patient-disease interactions and demographics
• Reduces reliance on medical tests for large-scale screening