Development and Validation of a Machine Learning-Based Screening Algorithm to Predict High-Risk Hepatitis C Infection.

Background: Amid the opioid epidemic in the United States, hepatitis C virus (HCV) infections are rising, with one-third of individuals with infection unaware due to the asymptomatic nature. This study aimed to develop and validate a machine learning (ML)-based algorithm to screen individuals at high risk of HCV infection. Methods: We conducted prognostic modeling using the 2016-2023 OneFlorida+ database of all-payer electronic health records. The study included individuals aged ≥18 years who were tested for HCV antibodies, RNA, or genotype. We identified 275 features of HCV, including sociodemographic and clinical characteristics, during a 6-month period before the test result date. Four ML algorithms-elastic net (EN), random forest (RF), gradient boosting machine (GBM), and deep neural network (DNN)-were developed and validated to predict HCV infection. We stratified patients into deciles based on predicted risk. Results: Among 445 624 individuals, 11 823 (2.65%) tested positive for HCV. Training (75%) and validation (25%) samples had similar characteristics (mean, standard deviation age, 45 [16] years; 62.86% female; 54.43% White). The GBM model ( Conclusions: ML algorithms effectively predicted and stratified HCV infection risk, offering a promising targeted screening tool for clinical settings.