Research Area:  Machine Learning

Abstract:

The increasing availability of electronic health data presents a major opportunity in healthcare for both discovery and practical applications to improve healthcare. However, for healthcare epidemiologists to best use these data, computational techniques that can handle large complex datasets are required. Machine learning (ML), the study of tools and methods for identifying patterns in data, can help. The appropriate application of ML to these data promises to transform patient risk stratification broadly in the field of medicine and especially in infectious diseases. This, in turn, could lead to targeted interventions that reduce the spread of healthcare-associated pathogens. In this review, we begin with an introduction to the basics of ML. We then move on to discuss how ML can transform healthcare epidemiology, providing examples of successful applications. Finally, we present special considerations for those healthcare epidemiologists who want to use and apply ML.

Keywords:  
Machine Learning
Healthcare
Epidemiology
Deep Learning

Author(s) Name:  Jenna Wiens, Erica S Shenoy

Publisher name:  Oxford University Press

DOI:  https://doi.org/10.1093/cid/cix731

Volume Information:  Volume 66, Issue 1, 1 January 2018, Pages 149–153

Paper Link:   https://academic.oup.com/cid/article/66/1/149/4085880?login=true