A Conjoint Graph Representation Learning Framework for Hypertension Comorbidity Risk Prediction

TL;DR

This paper introduces a novel graph-based framework for predicting hypertension-related comorbidities, leveraging disease networks and features to improve early risk detection and understand disease progression pathways.

Contribution

It develops a conjoint graph learning framework that constructs disease networks, extracts features, and predicts comorbidity risks with higher accuracy than existing models.

Findings

Network features are crucial for prediction accuracy.

The framework outperforms other models in risk prediction.

Analysis reveals potential pathways of disease progression.

Abstract

The comorbidities of hypertension impose a heavy burden on patients and society. Early identification is necessary to prompt intervention, but it remains a challenging task. This study aims to address this challenge by combining joint graph learning with network analysis. Motivated by this discovery, we develop a Conjoint Graph Representation Learning (CGRL) framework that: a) constructs two networks based on disease coding, including the patient network and the disease difference network. Three comorbidity network features were generated based on the basic difference network to capture the potential relationship between comorbidities and risk diseases; b) incorporates computational structure intervention and learning feature representation, CGRL was developed to predict the risks of diabetes and coronary heart disease in patients; and c) analysis the comorbidity patterns and exploring the pathways of disease progression, the pathological pathogenesis of diabetes and coronary heart disease may be revealed. The results show that the network features extracted based on the difference network are important, and the framework we proposed provides more accurate predictions than other strong models in terms of accuracy.