Stratifying individuals into non-alcoholic fatty liver disease risk levels using time series machine learning models

Ofir Ben-Assuli, Arie Jacobi, Orit Goldman, Shani Shenhar-Tsarfaty, Ori Rogowski, David Zeltser, Itzhak Shapira, Shlomo Berliner, Shira Zelber-Sagi

Research output: Contribution to journalArticlepeer-review

Abstract

Non-alcoholic fatty liver disease (NAFLD) affects 25% of the population worldwide, and its prevalence is anticipated to increase globally. While most NAFLD patients are asymptomatic, NAFLD may progress to fibrosis, cirrhosis, cardiovascular disease, and diabetes. Research reports, with daunting results, show the challenge that NAFLD's burden causes to global population health. The current process for identifying fibrosis risk levels is inefficient, expensive, does not cover all potential populations, and does not identify the risk in time. Instead of invasive liver biopsies, we implemented a non-invasive fibrosis assessment process calculated from clinical data (accessed via EMRs/EHRs). We stratified patients' risks for fibrosis from 2007 to 2017 by modeling the risk in 5579 individuals. The process involved time-series machine learning models (Hidden Markov Models and Group-Based Trajectory Models) profiled fibrosis risk by modeling patients’ latent medical status resulted in three groups. The high-risk group had abnormal lab test values and a higher prevalence of chronic conditions. This study can help overcome the inefficient, traditional process of detecting fibrosis via biopsies (that are also medically unfeasible due to their invasive nature, the medical resources involved, and costs) at early stages. Thus longitudinal risk assessment may be used to make population-specific medical recommendations targeting early detection of high risk patients, to avoid the development of fibrosis disease and its complications as well as decrease healthcare costs.

Original languageEnglish
Article number103986
JournalJournal of Biomedical Informatics
Volume126
DOIs
StatePublished - Feb 2022

Keywords

  • Clustering models
  • Machine learning

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