Machine Learning for Healthcare: On the Verge of a Major Shift in Healthcare Epidemiology
Open accessAbstract
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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Machine learning techniques are already emerging in clinical practice.
“Machine learning techniques are already emerging in clinical practice. 7”
From Clinical applications of machine learning algorithms: beyond the black box (2019) · cites this paper as [7]
Ensuring the quality, harmonization, and integration of data across multiple cohorts and sources is a major challenge.
“One major challenge is ensuring the quality, harmonization, and integration of data across multiple cohorts and sources [53].”
From Integrating machine learning and artificial intelligence in life-course epidemiology: pathways to innovative public health solutions (2024) · cites this paper as [53]
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- Authors
- Wiens, J., & Shenoy, E. S.
- Affiliations
- University of Michigan (US)
- Harvard University (US)
- Corresponding author
- Wiens, Jenna
- Venue
- Clinical Infectious Diseases
- Publisher
- Oxford University Press (OUP)
- Published
- 2017-08-21
- Volume
- 66
- Issue
- 1
- Pages
- 149-153
- ISSN
- 1058-4838 (ISSN-L)
- 1537-6591
- PMC
- PMC5850539
- OpenAlex
- W2750268731
- Cited by
- 574
- References
- 36
- Citations by year
- 2026: 57 · 2025: 89 · 2024: 85 · 2023: 70 · 2022: 89 · 2021: 85 · 2020: 61 · 2019: 23 · 2018: 13 · 2017: 1
- FWCI
- 3.99
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Clinical Infectious Diseases (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (K01AI110524)
- NIAID (U01AI124255)
- National Science Foundation (IIS-1553146)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- machine learning, hospital epidemiology, electronic health data, risk stratification, infectious diseases, nosocomial pathogens, targeted interventions, data-driven medicine
- MeSH terms
- Machine Learning, Electronic Data Processing, Communicable Diseases, Epidemiologic Methods, Humans
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