期刊
CLINICAL INFECTIOUS DISEASES
卷 66, 期 1, 页码 149-153出版社
OXFORD UNIV PRESS INC
DOI: 10.1093/cid/cix731
关键词
machine learning; patient risk stratification; healthcare epidemiologist; data-driven; computation
资金
- National Institute of Allergy and Infectious Diseases (NIAID) of the National Institutes of Health (NIH) [K01AI110524]
- Massachusetts General Hospital-Massachusetts Institute of Technology Grand Challenge
- National Science Foundation [IIS-1553146]
- NIAID of NIH [U01AI124255]
- NATIONAL INSTITUTE OF ALLERGY AND INFECTIOUS DISEASES [K01AI110524, U01AI124255] Funding Source: NIH RePORTER
The appropriate application of machine learning to data in healthcare has the potential to transform patient risk stratification for infectious diseases. We present an introduction to machine learning basics for the healthcare epidemiologist.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.
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