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Development and validation of an interpretable machine learning model for predicting the risk of non-cardiac surgery postoperative heart failure: a multicenter study

BackgroundThis study developed a machine learning model to predict postoperative heart failure (HF) risk in non-cardiac surgery patients.MethodsUsing data from 489 patients (109 HF cases, 380 controls), the dataset was split 8:2 into training and testing sets, with under-sampling for class imbalance...

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Autors principals: Qing Li, Zizhou Liu, Kunlun He, Yan Zhuang, Junyan Zhang, Bing Wei, Hebin Che, Bo Zhang, Liandi Jiu, Jiayue Li, Xinyu Song, Wei Dong
Format: Artigo
Idioma:Inglês
Publicat: Frontiers Media S.A. 2025-12-01
Col·lecció:Frontiers in Medicine
Matèries:
non-cardiac surgery
heart failure
machine learning
risk prediction model
postoperative
clinical decision support
Accés en línia:https://www.frontiersin.org/articles/10.3389/fmed.2025.1666885/full
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