TY - JOUR
T1 - Machine learning and artificial intelligence
T2 - Applications in healthcare epidemiology
AU - Hamilton, Alisa J.
AU - Strauss, Alexandra T.
AU - Martinez, Diego A.
AU - Hinson, Jeremiah S.
AU - Levin, Scott
AU - Lin, Gary
AU - Klein, Eili Y.
N1 - Publisher Copyright:
© The Author(s), 2021. Published by Cambridge University Press on behalf of The Society for Healthcare Epidemiology of America.
PY - 2021/10/7
Y1 - 2021/10/7
N2 - 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.
AB - 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.
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U2 - 10.1017/ash.2021.192
DO - 10.1017/ash.2021.192
M3 - Review article
C2 - 36168500
AN - SCOPUS:85129922750
SN - 2732-494X
VL - 1
JO - Antimicrobial Stewardship and Healthcare Epidemiology
JF - Antimicrobial Stewardship and Healthcare Epidemiology
IS - 1
M1 - e28
ER -