ORIGINAL RESEARCH article
Front. Cardiovasc. Med.
Sec. Hypertension
Volume 12 - 2025 | doi: 10.3389/fcvm.2025.1568907
Predicting mortality in critically ill patients with hypertension using machine learning and deep learning models
Provisionally accepted- Northwestern University, Evanston, United States
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Accurate prediction of mortality in critically ill patients with hypertension admitted to the Intensive Care Unit (ICU) is essential for guiding clinical decision-making and improving patient outcomes. Traditional prognostic tools often fall short in capturing the complex interactions between clinical variables in this high-risk population. Recent advances in machine learning (ML) and deep learning (DL) offer the potential for developing more sophisticated and accurate predictive models.This study aims to evaluate the performance of various ML and DL models in predicting mortality among critically ill patients with hypertension, with a particular focus on identifying key clinical predictors and assessing the comparative effectiveness of these models.We conducted a retrospective analysis of 30,096 critically ill patients with hypertension admitted to the ICU. Various ML models, including logistic regression, decision trees, and support vector machines, were compared with advanced DL models, including 1D convolutional neural networks (CNNs) and long short-term memory (LSTM) networks. Model performance was evaluated using area under the receiver operating characteristic curve (AUC) and other performance metrics. SHapley Additive exPlanations (SHAP) values were used to interpret model outputs and identify key predictors of mortality.The 1D CNN model with an initial selection of predictors achieved the highest AUC (0.7744), outperforming both traditional ML models and other DL models. Key clinical predictors of mortality identified across models included the APS-III score, age, and length of ICU stay. The SHAP analysis revealed that these predictors had a substantial influence on model predictions, underscoring their importance in assessing mortality risk in this patient population.Deep learning models, particularly the 1D CNN, demonstrated superior predictive accuracy compared to traditional ML models in predicting mortality among critically ill patients with hypertension. The integration of these models into clinical workflows could enhance the early identification of high-risk patients, enabling more targeted interventions and improving patient outcomes. Future research should focus on the prospective validation of these models and the ethical considerations associated with their implementation in clinical practice.
Keywords: Hypertension, Intensive Care Unit, Mortality prediction, machine learning, deep learning, Convolutional Neural Networks, SHAP analysis
Received: 30 Jan 2025; Accepted: 14 Jul 2025.
Copyright: © 2025 Ye. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Jiancheng Ye, Northwestern University, Evanston, United States
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