Medical Test-free Disease Detection Based on Big Data

TL;DR

This paper introduces CLDD, a graph-based deep learning model that predicts numerous diseases from electronic health records without extensive medical tests, reducing costs and enhancing large-scale screening.

Contribution

The paper presents a novel collaborative learning framework that leverages disease associations and patient similarities for cost-effective disease detection from EHR data.

Findings

CLDD outperforms baselines with 6.33% higher recall and 7.63% higher precision.

It accurately predicts masked diseases in case studies.

Demonstrates potential for large-scale disease screening.

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.