Establishment and validation of an interpretable machine learning-based predictive model for risk of post-PCI in-hospital heart failure in AIHD patients

This exploratory study developed a random forest (RF) model to predict the risk of post-PCI in-hospital HF in patients with AIHD. Based on the SHAP and LIME methods, the clinical interpretability of the model was significantly enhanced. Future research with larger sample sizes is warranted to optimize the training set and validate the generalizability of the model.

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Publication Details

Journal
Frontiers in Cardiovascular Medicine
Published
2026-02-27
DOI
https://doi.org/10.3389/fcvm.2026.1785285
Primary Topic
Acute Myocardial Infarction Research
Type
article
Field-Weighted Citation Impact
0.00

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article

Establishment and validation of an interpretable machine learning-based predictive model for risk of post-PCI in-hospital heart failure in AIHD patients

Qin Yang, Yigen Li, Xi Ye, Zhihang Wang et al.
Frontiers in Cardiovascular Medicine
Acute Myocardial Infarction Research
article

Establishment and validation of an interpretable machine learning-based predictive model for risk of post-PCI in-hospital heart failure in AIHD patients

Qin Yang, Yigen Li, Xi Ye, Zhihang Wang, Xinying Zhao, Huiqi Liu
article en

Abstract

This exploratory study developed a random forest (RF) model to predict the risk of post-PCI in-hospital HF in patients with AIHD. Based on the SHAP and LIME methods, the clinical interpretability of the model was significantly enhanced. Future research with larger sample sizes is warranted to optimize the training set and validate the generalizability of the model.

Frontiers in Cardiovascular MedicineVol. 13
Guangzhou University of Chinese Medicine (CN), University of Bristol (GB), First Affiliated Hospital of Guangzhou University of Chinese Medicine (CN), CM Hospital (KR)
Guangzhou University of Chinese Medicine, Guangzhou University
Openalex Percentile: Top 46%
Acute Myocardial Infarction Research
DOIOpen Access PDFOpenAlex
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Establishment and validation of an interpretable machine learning-based predictive model for risk of post-PCI in-hospital heart failure in AIHD patients — Qin Yang, Yigen Li, et al. · Frontiers in Cardiovascular Medicine (2026) | TGRS Research Map | TGRS