Beyond Traditional Diagnostics: Transforming Patient-Side Information into Predictive Insights with Knowledge Graphs and Prototypes

TL;DR

This paper introduces KPI, a novel framework that leverages knowledge graphs, prototypes, and large language models to improve disease prediction from patient data, addressing bias and interpretability issues.

Contribution

KPI integrates medical knowledge graphs, disease prototypes, and contrastive learning with LLM-generated explanations to enhance accuracy and interpretability in patient-side disease prediction.

Findings

KPI outperforms existing methods in predictive accuracy.

KPI provides clinically valid, patient-aligned explanations.

KPI effectively handles long-tailed disease distributions.

Abstract

Predicting diseases solely from patient-side information, such as demographics and self-reported symptoms, has attracted significant research attention due to its potential to enhance patient awareness, facilitate early healthcare engagement, and improve healthcare system efficiency. However, existing approaches encounter critical challenges, including imbalanced disease distributions and a lack of interpretability, resulting in biased or unreliable predictions. To address these issues, we propose the Knowledge graph-enhanced, Prototype-aware, and Interpretable (KPI) framework. KPI systematically integrates structured and trusted medical knowledge into a unified disease knowledge graph, constructs clinically meaningful disease prototypes, and employs contrastive learning to enhance predictive accuracy, which is particularly important for long-tailed diseases. Additionally, KPI utilizes large language models (LLMs) to generate patient-specific, medically relevant explanations, thereby improving interpretability and reliability. Extensive experiments on real-world datasets demonstrate that KPI outperforms state-of-the-art methods in predictive accuracy and provides clinically valid explanations that closely align with patient narratives, highlighting its practical value for patient-centered healthcare delivery.