Machine learning and artificial intelligence: applications in healthcare epidemiology

Artificial intelligence (AI) refers to the performance of tasks by machines ordinarily associated with human intelligence. Machine learning (ML) is a subtype of AI; it refers to the ability of computers to draw conclusions (ie, learn) from data without being directly programmed. ML builds from tradi...

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Bibliographic Details
Published in:Antimicrobial stewardship & healthcare epidemiology : ASHE 2021-01, Vol.1 (1), p.e28-e28, Article e28
Main Authors: Hamilton, Alisa J., Strauss, Alexandra T., Martinez, Diego A., Hinson, Jeremiah S., Levin, Scott, Lin, Gary, Klein, Eili Y.
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial intelligence
Bias
Clinical outcomes
Computers
Datasets
Decision making
Decision trees
Dependent variables
Disease prevention
Electronic health records
Emergency medical care
Emergency medical services
Epidemiology
Generalized linear models
Health care
Machine learning
Patients
Review
Statistical methods
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Summary:Artificial intelligence (AI) refers to the performance of tasks by machines ordinarily associated with human intelligence. Machine learning (ML) is a subtype of AI; it refers to the ability of computers to draw conclusions (ie, learn) from data without being directly programmed. ML builds from traditional statistical methods and has drawn significant interest in healthcare epidemiology due to its potential for improving disease prediction and patient care. This review provides an overview of ML in healthcare epidemiology and practical examples of ML tools used to support healthcare decision making at 4 stages of hospital-based care: triage, diagnosis, treatment, and discharge. Examples include model-building efforts to assist emergency department triage, predicting time before septic shock onset, detecting community-acquired pneumonia, and classifying COVID-19 disposition risk level. Increasing availability and quality of electronic health record (EHR) data as well as computing power provides opportunities for ML to increase patient safety, improve the efficiency of clinical management, and reduce healthcare costs.
ISSN:2732-494X
2732-494X
DOI:10.1017/ash.2021.192