Explainable machine learning and online calculators to predict heart failure mortality in intensive care units.

Journal: ESC heart failure

PMID: 39300773

Abstract

AIMS: This study aims to develop explainable machine learning models and clinical tools for predicting mortality in patients in the intensive care unit (ICU) with heart failure (HF).

Authors

An-Tian Chen

Department of Cardiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College/National Center for Cardiovascular Diseases, Beijing, China.
Yuhui Zhang

Department of Mathematical and Computing Science, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan. Electronic address: zhang.y.av@m.titech.ac.jp.
Jian Zhang

College of Pharmacy, Ningxia Medical University, Yinchuan, NingxiaHui Autonomous Region, China.

Keywords

Aged Female Follow-Up Studies Heart Failure Hospital Mortality Humans Intensive Care Units Machine Learning Male Middle Aged Nomograms Prognosis Retrospective Studies Risk Assessment Survival Rate

External Resources

View on PubMed Access via DOI PubMed (39300773)

Explainable machine learning and online calculators to predict heart failure mortality in intensive care units.

Abstract

Authors

Keywords

External Resources

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