Development and validation of a machine learning model for predicting mortality risk in veno-arterial extracorporeal membrane oxygenation patients

This study aims to develop and validate a machine learning-based mortality risk prediction model for V-A ECMO patients to improve the precision of clinical decision-making. This multicenter retrospective cohort study included 280 patients receiving V-A ECMO from the Second Affiliated Hospital of Gua...

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Bibliographic Details
Published in:	Scientific reports Vol. 15; no. 1; pp. 41581 - 14
Main Authors:	Gao, Hanming, Huang, Xiaolin, Zhou, Kaihuan, Ling, Yicong, Chen, Yin, Mou, Chenglin, Li, Shuanglei, Lu, Junyu
Format:	Journal Article
Language:	English
Published:	London Nature Publishing Group UK 24.11.2025 Nature Publishing Group Nature Portfolio
Subjects:	692/308 692/4019 692/699 Accuracy Adult Age Albumin Calibration Cardiac arrest Clinical decision making Decision making Diabetes Diabetes mellitus Extracorporeal membrane oxygenation Extracorporeal Membrane Oxygenation - methods Extracorporeal Membrane Oxygenation - mortality Feature selection Female Health risks Heart attacks Heart failure Hospital Mortality Humanities and Social Sciences Humans Lactic acid Learning algorithms Logistic Models Logistic regression Machine Learning Male Medical prognosis Middle Aged Missing data Mortality Mortality risk Mortality risk prediction multidisciplinary Myocarditis Neural networks Oxygenation Patients Prediction models Regression analysis Retrospective Studies Risk Assessment Science Science (multidisciplinary) Sepsis Standard scores Variables Venoarterial extracorporeal membrane oxygenation Ventilation Ventilators Mortality risk prediction Logistic regression Machine learning Venoarterial extracorporeal membrane oxygenation
ISSN:	2045-2322, 2045-2322
Online Access:	Get full text
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