DAPNet: multi-view graph contrastive network incorporating disease clinical and molecular associations for disease progression prediction.

ABSTRACT:

Background

Timely and accurate prediction of disease progress is crucial for facilitating early intervention and treatment for various chronic diseases. However, due to the complicated and longitudinal nature of disease progression, the capacity and completeness of clinical data required for training deep learning models remains a significant challenge. This study aims to explore a new method that reduces data dependency and achieves predictive performance comparable to existing research.

Methods

This study proposed DAPNet, a deep learning-based disease progression prediction model that solely utilizes the comorbidity duration (without relying on multi-modal data or comprehensive medical records) and disease associations from biomedical knowledge graphs to deliver high-perfo

SUBMITTER: Tian H 

PROVIDER: S-EPMC11575134 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature