Machine learning-based prediction of coronary care unit readmission: A multihospital validation study
Fei-Fei Flora Yau1, I-Min Chiu1,2, Kuan-Han Wu1
1Department of Emergency Medicine, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, Taiwan.
Insights
Accurately predicting coronary care unit (CCU) readmissions is crucial. A machine learning gradient boosting model effectively identified high-risk patients, demonstrating strong predictive performance across multiple hospitals.
Area of Science:
- Cardiology
- Medical Informatics
- Machine Learning
Background:
- Coronary care unit (CCU) readmissions significantly impact patient outcomes and healthcare costs.
- Accurate identification of patients at high risk for CCU readmission is essential for timely intervention.
Purpose of the Study:
- To develop and externally validate a predictive model for CCU readmission.
- To utilize machine learning (ML) algorithms for enhanced patient risk stratification.
Main Methods:
- Collected patient data from electronic health records, encompassing demographics, medical history, and lab results (40 features).
- Evaluated five ML models: logistic regression, random forest, support vector machine, gradient boosting, and multilayer perceptron.
- Selected the gradient boosting model for its superior performance.
Main Results:
- The gradient boosting model achieved an area under the receiver operating characteristic curve (AUC) of 0.887 in internal validation.
- External validation across multiple centers confirmed the model's robustness with AUCs ranging from 0.852 to 0.879.
- The model demonstrated consistent high performance in predicting CCU readmissions.
Conclusions:
- Machine learning algorithms can effectively enhance patient risk stratification in healthcare settings.
- The developed predictive model shows promise for optimizing clinical interventions and reducing CCU readmission rates.
- Integration of ML in clinical practice can lead to improved patient management and resource allocation.
Objective:
Readmission to the coronary care unit (CCU) has significant implications for patient outcomes and healthcare expenditure, emphasizing the urgency to accurately identify patients at high readmission risk. This study aims to construct and externally validate a predictive model for CCU readmission using machine learning (ML) algorithms across multiple hospitals.
Methods:
Patient information, including demographics, medical history, and laboratory test results were collected from electronic health record system and contributed to a total of 40 features. Five ML models: logistic regression, random forest, support vector machine, gradient boosting, and multilayer perceptron were employed to estimate the readmission risk.
Results:
The gradient boosting model was selected demonstrated superior performance with an area under the receiver operating characteristic curve (AUC) of 0.887 in the internal validation set. Further external validation in hold-out test set and three other medical centers upheld the model's robustness with consistent high AUCs, ranging from 0.852 to 0.879.
Conclusion:
The results endorse the integration of ML algorithms in healthcare to enhance patient risk stratification, potentially optimizing clinical interventions, and diminishing the burden of CCU readmissions.
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