Machine Learning in Infectious Disease for Risk Factor Identification and Hypothesis Generation: Proof of Concept Using Invasive Candidiasis.

Background: Machine learning (ML) models can handle large data sets without assuming underlying relationships and can be useful for evaluating disease characteristics, yet they are more commonly used for predicting individual disease risk than for identifying factors at the population level. We offer a proof of concept applying random forest (RF) algorithms to -positive hospital encounters in an electronic health record database of patients in the United States.

Methods: -positive encounters were extracted from the Cerner database; invasive infections were laboratory-positive sterile site infections.