[Establishing of mortality predictive model for elderly critically ill patients using simple bedside indicators and interpretable machine learning algorithms].

Journal: Zhonghua wei zhong bing ji jiu yi xue

PMID: 40017366

Abstract

OBJECTIVE: To explore the feasibility of incorporating simple bedside indicators into death predictive model for elderly critically ill patients based on interpretability machine learning algorithms, providing a new scheme for clinical disease assessment.

Authors

Yulan Meng

Department of Critical Care Medicine, Tacheng People's Hospital of Ili Kazak Autonomous Prefecture, Tacheng 834300, Xinjiang Uygur Autonomous Region, China.
Jiaxin Li

Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, China.
Xinqiang Shan

Department of Critical Care Medicine, Tacheng People's Hospital of Ili Kazak Autonomous Prefecture, Tacheng 834300, Xinjiang Uygur Autonomous Region, China.
Pengyu Lu

Department of Anesthesiology, Kaifeng Central Hospital, Kaifeng 475000, Henan, China.
Wei Huang

Shaanxi Institute of Flexible Electronics, Northwestern Polytechnical University, 710072 Xi'an, China.

Keywords

Aged Algorithms APACHE Critical Illness Female Hospital Mortality Humans Intensive Care Units Machine Learning Male Organ Dysfunction Scores Prognosis Retrospective Studies

External Resources

View on PubMed Access via DOI PubMed (40017366)

[Establishing of mortality predictive model for elderly critically ill patients using simple bedside indicators and interpretable machine learning algorithms].

Abstract

Authors

Keywords

External Resources

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