Developing deep learning-based strategies to predict the risk of hepatocellular carcinoma among patients with nonalcoholic fatty liver disease from electronic health records.

ABSTRACT:

Background

Deep learning models showed great success and potential when applied to many biomedical problems. However, the accuracy of deep learning models for many disease prediction problems is affected by time-varying covariates, rare incidence, and covariate imbalance when using structured electronic health records data. The situation is further exasperated when predicting the risk of one disease on condition of another disease, such as the hepatocellular carcinoma risk among patients with nonalcoholic fatty liver disease due to slow, chronic progression, the scarce of data with both disease conditions and the sex bias of the diseases.

Objective

The goal of this study is to investigate the extent to which time-varying covariates, rare incidence, and covariate imbalance in

SUBMITTER: Li Z 

PROVIDER: S-EPMC10680899 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature