Does Machine Learning Outperform Traditional Fibrosis Scores in Predicting Liver Cirrhosis Risk? A Longitudinal EHR-Based Study

תקציר מקורי באנגליתarXiv:2601.00175v3 Announce Type: replace Abstract: Objective: Develop and evaluate machine learning (ML) models for predicting incident liver cirrhosis (LC) one and two years before diagnosis using routinely collected electronic health record (EHR) data and compare their performance with the FIB-4 and APRI clinical scores. Methods: We conducted a retrospective cohort study using de-identified EHR data from a large academic health system. Adult patients with diagnostic evidence of LC or LC-related risk conditions were identified using ICD-9/10 codes and classified into cirrhosis and non-cirrhosis cohorts. One- and two-year prediction scenarios were created using observation and prediction windows. Demographics, diagnoses, laboratory results, and vital signs from the observation window were