Digital Phenotyping of Nonalcoholic Fatty Liver Disease

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1 2 PROJECT SUMMARY/ABSTRACT 3 4 One of the most critical gaps in management of nonalcoholic fatty liver disease (NAFLD) is the lack of effective 5 methods of early identification in the population. The objective of this study is to leverage data and analytics to 6 improve healthcare outcomes by early detection and risk stratification of NAFLD, before onset of liver-related 7 complications. Artificial intelligence applications in large electronic health records have the potential to identify 8 disease traits before onset of disease. The central hypothesis of this proposal is that targeted screening with 9 machine-learning models applied to large integrated healthcare datasets can identify individuals with NAFLD 10 and, more specifically, those with a progressive phenotype. We will test the central hypothesis in 2 specific 11 AIMs. First, we will train a machine learning model of NAFLD prediction using multiple longitudinal data points 12 of all health-care encounters of a well-characterized population-based cohort of individuals diagnosed with 13 NAFLD in reference to individuals without NAFLD from the general population. We hypothesize that 14 unsupervised machine learning can identify complex processes and patterns without a human's guidance and 15 discover early comorbidity clusters (“latent traits” present prior to NAFLD development) that reflect a phenotype 16 at risk to develop NAFLD later in life. Second, we will test and optimize the model for the prediction of patient 17 outcomes (development of cirrhosis, liver-related complications and death) in the NAFLD cohort. We 18 hypothesize that machine learning approaches could be used to further stratify patients into subgroups with 19 different disease trajectories, with the goal of identifying those individuals at risk of progressive NAFLD and 20 liver-related outcomes. The research proposed in this application is innovative because it expands the 21 analytical toolbox beyond conventional methods to identify individuals with NAFLD using all health-encounters 22 of a large, well-characterized population-based cohort with long follow-up. This proposal is significant because 23 it addresses a critical need of identification and management of the most prevalent chronic liver disease and 24 offers a practical solution to large scale implementation of screening and risk-stratification strategies using 25 routinely collected data. The ultimate goal of this proposal is to improve the population health in obesity- 26 associated diseases.