Development of a Machine Learning Modeling Tool for Predicting HIV Incidence Using Public Health Data From a County in the Southern United States.

Background: Advancements in machine learning (ML) have improved the accuracy of models that predict human immunodeficiency virus (HIV) incidence. These models have used electronic medical records and registries. We aim to broaden the application of these tools by using deidentified public health datasets for notifiable sexually transmitted infections (STIs) from a southern US county known for high HIV incidence.