A dynamic machine learning model for prediction of NAFLD in a health checkup population: A longitudinal study.

ABSTRACT:

Background

Non-alcoholic fatty liver disease (NAFLD) is one of the most common liver diseases worldwide. Currently, most NAFLD prediction models are diagnostic models based on cross-sectional data, which failed to provide early identification or clarify causal relationships. We aimed to use time-series deep learning models with longitudinal health checkup records to predict the onset of NAFLD in the future, and update the model stepwise by incorporating new checkup records to achieve dynamic prediction.

Methods

10,493 participants with over 6 health checkup records from Beijing MJ Health Screening Center were included to conduct a retrospective cohort study, in which the constantly updated initial 5 checkup data were incorporated stepwise to predict the risk of NAFLD at and

SUBMITTER: Deng Y 

PROVIDER: S-EPMC10412833 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature