Machine Learning in Infectious Disease for Risk Factor Identification and Hypothesis Generation: Proof of Concept Using Invasive Candidiasis
Abstract
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 Candida-positive hospital encounters in an electronic health record database of patients in the United States.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Aug 01, 2022
- Source ID
- 10.1093/ofid/ofac401
Entities
People
- D Rebecca Prevots
- Emily Ricotta
- Jeffrey R Strich
- Lisa M Mayer
- Michail S. Lionakis
- Nicholas G Evans
- Sameer S Kadri
Organizations
- Air Force Office of Scientific Research
- Davis Educational Foundation
- National Institute of Allergy and Infectious Diseases
- National Institutes of Health
- National Institutes of Health Clinical Center
- National Science Foundation
- United States Department of Energy
- University of Massachusetts Lowell