Improving an Electronic Health Record-Based Clinical Prediction Model Under Label Deficiency: Network-Based Generative Adversarial Semisupervised Approach.

ABSTRACT:

Background

Observational biomedical studies facilitate a new strategy for large-scale electronic health record (EHR) utilization to support precision medicine. However, data label inaccessibility is an increasingly important issue in clinical prediction, despite the use of synthetic and semisupervised learning from data. Little research has aimed to uncover the underlying graphical structure of EHRs.

Objective

A network-based generative adversarial semisupervised method is proposed. The objective is to train clinical prediction models on label-deficient EHRs to achieve comparable learning performance to supervised methods.

Methods

Three public data sets and one colorectal cancer data set gathered from the Second Affiliated Hospital of Zhejiang University were selected

SUBMITTER: Li R 

PROVIDER: S-EPMC10337516 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature