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.
Learning Objective 1: Utilize longitudinal lab data from patient electronic health records to:
(i) improve existing machine learning classifiers for non-alcoholic steatohepatitis (NASH), a chronic liver condition
(ii) generate clinicaly interpretable, non-invasive, scalable, and accurate predictors for NASH.
Suruchi Fialoke, Pfizer
Anders Malarstig, Pfizer
Melissa Miller, Pfizer
Alexandra Dumitriu (Presenter)