Prediction of hospital-acquired influenza using machine learning algorithms: a comparative study.

Journal: BMC infectious diseases

PMID: 38698304

Abstract

BACKGROUND: Hospital-acquired influenza (HAI) is under-recognized despite its high morbidity and poor health outcomes. The early detection of HAI is crucial for curbing its transmission in hospital settings.

Authors

Younghee Cho

College of Nursing, Yonsei University, Seoul, Republic of Korea.
Hyang Kyu Lee

College of Nursing, Yonsei University, Seoul, Republic of Korea.
Joungyoun Kim

College of Engineering, University of Seoul, Seoul, Republic of Korea.
Ki-Bong Yoo

Division of Health Administration, Yonsei University, Wonju, Republic of Korea.
Jongrim Choi

College of Nursing, Keimyung University, Daegu, Republic of Korea.
Yongseok Lee

Myongji-Choonhey Rehabilitation Hospital, Seoul, Republic of Korea.
Mona Choi

College of Nursing, Yonsei University, Seoul, Republic of Korea. monachoi@yuhs.ac.

Keywords

Adult Aged Aged, 80 and over Algorithms Cross Infection Female Humans Influenza, Human Logistic Models Machine Learning Male Middle Aged Neural Networks, Computer Retrospective Studies ROC Curve Young Adult

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View on PubMed Access via DOI PubMed (38698304)

Prediction of hospital-acquired influenza using machine learning algorithms: a comparative study.

Abstract

Authors

Keywords

External Resources

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