%0 Journal Article
%T Application of Machine Learning Methods to Predict Non-Alcoholic Steatohepatitis (NASH) in Non-Alcoholic Fatty Liver (NAFL) Patients
%A Alexandra Dumitriu
%A Anders Malarstig
%A Melissa R. Miller
%A Suruchi Fialoke
%J Archive of "AMIA Annual Symposium Proceedings".
%D 2018
%X Non-alcoholic fatty liver disease (NAFLD) is the leading cause of chronic liver disease worldwide. NAFLD patients have excessive liver fat (steatosis), without other liver diseases and without excessive alcohol consumption. NAFLD consists of a spectrum of conditions: benign steatosis or non-alcoholic fatty liver (NAFL), steatosis accompanied by inflammation and fibrosis or nonalcoholic steatohepatitis (NASH), and cirrhosis. Given a lack of clinical biomarkers and its asymptomatic nature, NASH is under-diagnosed. We use electronic health records from the Optum Analytics to (1) identify patients diagnosed with benign steatosis and NASH, and (2) train machine learning classifiers for NASH and healthy (non-NASH) populations to (3) predict NASH disease status on patients diagnosed with NAFL. Summarized temporal lab data for alanine aminotransferase, aspartate aminotransferase, and platelet counts, with basic demographic information and type 2 diabetes status were included in the models
%U https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6371264/