Development and validation of interpretable machine learning models for triage patients admitted to the intensive care unit.

Journal: PloS one

PMID: 39964993

Abstract

OBJECTIVES: Developing and validating interpretable machine learning (ML) models for predicting whether triaged patients need to be admitted to the intensive care unit (ICU).

Authors

Zheng Liu

ICSC World Laboratory, Geneva, Switzerland.
Wenqi Shu

Department of Emergency, The First Hospital of China Medical University, Shenyang, China.
Hongyan Liu

Department of Gastroenterology, The First Affiliated Hospital of Shandong First Medical University& Shandong Provincial Qianfoshan Hospital, Jinan, China.
Xuan Zhang
Wei Chong

Department of Emergency, The First Hospital of China Medical University, Shenyang, China.

Keywords

Adult Aged Algorithms Area Under Curve Bayes Theorem Female Humans Intensive Care Units Machine Learning Male Middle Aged Patient Admission ROC Curve Triage

External Resources

View on PubMed Access via DOI PubMed (39964993)

Development and validation of interpretable machine learning models for triage patients admitted to the intensive care unit.

Abstract

Authors

Keywords

External Resources

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