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Methodology minute: a machine learning primer for infection prevention and control
Timothy L Wiemken1, Ana Santos Rutschman2
1Saint Louis University, School of Medicine, Department of Health and Clinical Outcomes Research, St. Louis, MO; Saint Louis University, School of Medicine, Department of Medicine, Division of Infectious Diseases, Allergy, and Immunology, St. Louis, MO; Saint Louis University, Advanced Health Data (AHeaD) Research Institute, Center for Systems Infection Prevention, St. Louis, MO; Saint Louis University, Health Innovation and Legal Preparedness Partnership, St. Louis, MO.
Abstract:
The use of machine-learning and predictive modeling in infection prevention and control activities is increasing dramatically. In order for infection preventionists to make informed decisions on the performance of any particular model as well as to determine if the output of the model will be useful for their program needs, a suitable understanding of the creation and evaluation of these models is necessary. The purpose of this primer is to introduce the infection preventionist to the most commonly used machine-learning method in infection prevention: supervised learning.
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