Mortality prediction of inpatients with NSTEMI in a premier hospital in China based on stacking model
1College of Marine Electrical Engineering, Dalian Maritime University, Dalian, China.
Insights
A novel Stacking ensemble model accurately predicts in-hospital mortality risk for non-ST-segment elevation myocardial infarction (NSTEMI) patients. This advanced AI approach offers superior performance, aiding clinicians in timely interventions and reducing patient mortality.
Area of Science:
- Cardiology
- Artificial Intelligence
- Predictive Analytics
Background:
- Acute myocardial infarction (AMI) is a major cause of hospitalization and death in China.
- Accurate prediction of inpatient mortality is critical for managing non-ST-segment elevation myocardial infarction (NSTEMI) patients.
Purpose of the Study:
- To develop and evaluate a novel Stacking ensemble model for predicting in-hospital mortality risk in NSTEMI patients.
- To improve the accuracy and timeliness of risk stratification for NSTEMI patients.
Main Methods:
- A total of 3061 NSTEMI patients were analyzed.
- A Stacking ensemble model incorporating oversampling, Recursive Feature Elimination (RFE) for feature selection, and a double-layer architecture (base models: LR, DT, SVM, RF, ADB, ET, GBDT; meta-model: XGBOOST) was developed.
- Clinical data was utilized for prediction.
Main Results:
- The Stacking model achieved an Area Under Curve (AUC) of 0.987, outperforming individual models like LR (0.934) and RF (0.948).
- The model demonstrated superior performance across Accuracy, Precision, Recall, and F1 metrics compared to single models.
- Fifty-seven statistically significant clinical features were identified and included.
Conclusions:
- The proposed Stacking model effectively integrates multiple algorithms to enhance prediction performance for NSTEMI in-hospital mortality.
- This model provides valuable insights for physicians to identify high-risk patients, facilitating early clinical interventions and potentially reducing mortality rates.
Background:
Acute myocardial infarction (AMI) remains a leading cause of hospitalization and death in China. Accurate mortality prediction of inpatient is crucial for clinical decision-making of non-ST-segment elevation myocardial infarction (NSTEMI) patients.
Methods:
In this study, a total of 3061 patients between January 1, 2017 and December 31, 2022 diagnosed with NSTEMI were enrolled in this study. A new method based on Stacking ensemble model is proposed to predict the in-hospital mortality risk of NSTEMI using clinical data. This method mainly consists of three parts. Firstly, oversampling technique was used to alleviate the class imbalance problem. Secondly, the feature selection method of Recursive Feature Elimination (RFE) was selected for effective feature selection. Finally, a unique double-layer stacking model is designed to improve the performance of the algorithm. Seven classical artificial intelligence methods of Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), Adaptive Boosting (ADB), Extra Tree (ET), and Gradient Boosting Decision Tree (GBDT) were selected as candidate models for the base model of the first layer of the model, and extreme gradient enhancement (XGBOOST) was selected as the meta-model for the second layer.
Results:
Patient were divided into the surviving group and the death group, and a total of 57 clinical features showed statistically significant for the two groups and finally included in the subsequent model. The results show that the Area Under Curve (AUC) of the Stacking model proposed in this paper is 0.987, which is higher than that of LR (0.934), DT (0.946), SVM (0.942), RF (0.948), ADB (0.949), ET (0.938) and GBDT (0.920). At the same time, the proposed Stacking model has higher performance than each single model in terms of Accuracy, Precision, Recall and F1 evaluation indicators.
Conclusions:
The Stacking model proposed in this paper can integrate the advantages of LR, DT, SVM, RF, ADB, ET and GBDT models to achieve better prediction performance. This model can provide valuable insights for physicians to identify high-risk patients more precisely and timely, thereby maximizing the potential for early clinical interventions to reduce the mortality rate.
More Related Videos
07:31Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
Published on: May 15, 2020
05:41Left Anterior Descending Coronary Artery Ligation for Ischemia-Reperfusion Research: Model Improvement via Technical Modifications and Quality Control
Published on: December 16, 2022
Related Concept Videos
01:30Acute Coronary Syndrome I: Introduction
01:30Acute Coronary Syndrome III: Diagnostic Studies
01:28Acute Coronary Syndrome IV: Interprofessional Care