Development of a novel machine learning model to predict presence of nonalcoholic steatohepatitis.

ABSTRACT:

Objective

To develop a computer model to predict patients with nonalcoholic steatohepatitis (NASH) using machine learning (ML).

Materials and methods

This retrospective study utilized two databases: a) the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) nonalcoholic fatty liver disease (NAFLD) adult database (2004-2009), and b) the Optum® de-identified Electronic Health Record dataset (2007-2018), a real-world dataset representative of common electronic health records in the United States. We developed an ML model to predict NASH, using confirmed NASH and non-NASH based on liver histology results in the NIDDK dataset to train the model.

Results

Models were trained and tested on NIDDK NAFLD data (704 patients) and the best-performing models eva

SUBMITTER: Docherty M 

PROVIDER: S-EPMC8200272 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature