Machine Learning for Healthcare: On the Verge of a Major Shift in Healthcare Epidemiology

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 re...

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Bibliographic Details
Published in:Clinical infectious diseases 2018-01, Vol.66 (1), p.149-153
Main Authors: Wiens, Jenna, Shenoy, Erica S
Format: Article
Language:English
Subjects:
Artificial intelligence
Communicable Diseases - epidemiology
Computer applications
Data processing
Electronic Data Processing
Electronic health records
Epidemiologic Methods
Epidemiology
Health care
Humans
Identification methods
Infectious diseases
Invited
Learning algorithms
Machine Learning
Pathogens
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Summary: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.
ISSN:1058-4838
1537-6591
1537-6591
DOI:10.1093/cid/cix731