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已发表论文
基于中国多中心三级医院数据开发并验证急性心肌梗死患者医疗决策延迟风险的机器学习模型
Authors Liu Y , Yu F, He M, Wang L, Wu H, Liu W, Gui P, He M, Zhang H, Chen Y
Received 17 October 2025
Accepted for publication 8 January 2026
Published 28 January 2026 Volume 2026:19 562526
DOI https://doi.org/10.2147/IJGM.S562526
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Redoy Ranjan
Yan Liu,1 Fei Yu,1 Mingxing He,2 Lijun Wang,1 Haiyuan Wu,1 Wei Liu,3 Ping Gui,1 Meizhen He,1 Hua Zhang,4 Yuanting Chen1
1Nursing Department, Shenzhen Hospital (Futian) of Guangzhou University of Chinese Medicine, Shenzhen, Guangdong, People’s Republic of China; 2Internal Medicine Intensive Care Unit, Shenzhen Hospital of Peking University, Shenzhen, Guangdong, People’s Republic of China; 3Emergency Department, Shenzhen Second People’s Hospital, Shenzhen, Guangdong, People’s Republic of China; 4International School of Nursing, Hainan Medical University, Haikou, Hainan, People’s Republic of China
Correspondence: Yuanting Chen, Shenzhen Hospital (Futian) of Guangzhou University of Chinese Medicine, No. 6001 Beihuan Avenue, Futian District, Shenzhen, People’s Republic of China, Email 605859290@qq.com Hua Zhang, Hainan Health Vocational College, No. 32 Xiuhua Road, Xiuying District, Haikou, Hainan, People’s Republic of China, Email zhanghuashelley@hotmail.com
Purpose: Timely reperfusion is critical for improving outcomes in patients with acute myocardial infarction (AMI), as every delay raises the incidence of complications and mortality. Therefore, we aimed to develop a machine-learning model that quantifies the risk of pre-hospital decision-making delay and visualizes how individual determinants modulate this risk.
Patients and Methods: This retrospective study included 594 AMI patients admitted to hospitals in Hainan from January to August 2023. Data were collected via medical systems and surveys. We used the Elastic Net and Boruta algorithms for feature selection and hyperparameter optimization with grid search and 10-fold cross-validation. Six machine learning models were developed: logistic regression, random forest, support vector machine, XGBoost, decision tree, and naive Bayes. The primary metric was the Area Under the Curve (AUC), and SHapley Additive exPlanations (SHAP) were used to assess feature importance.
Results: The medical decision-making delay rate was 61.78%, with a median decision time of 3.98 hours. All models showed good predictive performance, with the random forest model excelling, achieving an AUC of 0.91, accuracy of 0.92, recall of 0.98, F1 score of 0.93, and specificity of 0.81. SHAP analysis revealed that pain severity, disease type, and history of myocardial infarction were the most significant predictors of delay. Pain severity had a nonlinear relationship with delay risk, while disease type and prior infarction history showed complex interactions.
Conclusion: Machine learning models, especially random forest, accurately predict the risk of delayed medical decision-making in AMI patients and reliably delineate the key drivers of such delay, thereby informing targeted clinical interventions.
Keywords: machine learning, predictive model, acute myocardial infarction, AMI, medical decision-making delay
1Nursing Department, Shenzhen Hospital (Futian) of Guangzhou University of Chinese Medicine, Shenzhen, Guangdong, People’s Republic of China; 2Internal Medicine Intensive Care Unit, Shenzhen Hospital of Peking University, Shenzhen, Guangdong, People’s Republic of China; 3Emergency Department, Shenzhen Second People’s Hospital, Shenzhen, Guangdong, People’s Republic of China; 4International School of Nursing, Hainan Medical University, Haikou, Hainan, People’s Republic of China
Correspondence: Yuanting Chen, Shenzhen Hospital (Futian) of Guangzhou University of Chinese Medicine, No. 6001 Beihuan Avenue, Futian District, Shenzhen, People’s Republic of China, Email 605859290@qq.com Hua Zhang, Hainan Health Vocational College, No. 32 Xiuhua Road, Xiuying District, Haikou, Hainan, People’s Republic of China, Email zhanghuashelley@hotmail.com
Purpose: Timely reperfusion is critical for improving outcomes in patients with acute myocardial infarction (AMI), as every delay raises the incidence of complications and mortality. Therefore, we aimed to develop a machine-learning model that quantifies the risk of pre-hospital decision-making delay and visualizes how individual determinants modulate this risk.
Patients and Methods: This retrospective study included 594 AMI patients admitted to hospitals in Hainan from January to August 2023. Data were collected via medical systems and surveys. We used the Elastic Net and Boruta algorithms for feature selection and hyperparameter optimization with grid search and 10-fold cross-validation. Six machine learning models were developed: logistic regression, random forest, support vector machine, XGBoost, decision tree, and naive Bayes. The primary metric was the Area Under the Curve (AUC), and SHapley Additive exPlanations (SHAP) were used to assess feature importance.
Results: The medical decision-making delay rate was 61.78%, with a median decision time of 3.98 hours. All models showed good predictive performance, with the random forest model excelling, achieving an AUC of 0.91, accuracy of 0.92, recall of 0.98, F1 score of 0.93, and specificity of 0.81. SHAP analysis revealed that pain severity, disease type, and history of myocardial infarction were the most significant predictors of delay. Pain severity had a nonlinear relationship with delay risk, while disease type and prior infarction history showed complex interactions.
Conclusion: Machine learning models, especially random forest, accurately predict the risk of delayed medical decision-making in AMI patients and reliably delineate the key drivers of such delay, thereby informing targeted clinical interventions.
Keywords: machine learning, predictive model, acute myocardial infarction, AMI, medical decision-making delay