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Artificial Intelligence and Big Data Technologies in the Construction of Surgical Risk Prediction Model for Patients
Xiaoqiang Tang1, Tao Wang1, Haifeng Shi1
1Radiology Department, the Affiliated Changzhou No. 2 People's Hospital of Nanjing Medical University, Changzhou 213164, Jiangsu, China.
Insights
This study developed an artificial intelligence (AI) model using big data to predict mortality risk after coronary artery bypass grafting (CABG). The model showed promise but tended to overestimate mortality rates in intermediate-risk patients.
Area of Science:
- Cardiology
- Medical Informatics
- Artificial Intelligence
Background:
- Coronary artery bypass grafting (CABG) is a critical cardiac procedure.
- Accurate prediction of mortality risk is essential for patient management and outcomes.
- Existing risk prediction models may benefit from advancements in artificial intelligence (AI) and big data.
Purpose of the Study:
- To develop and evaluate an AI-driven risk prediction model for mortality in CABG patients.
- To leverage big data technologies for enhanced prediction accuracy.
- To provide a tool for clinical decision support in cardiac surgery.
Main Methods:
- Collected clinical data from 2,364 patients undergoing CABG between January 2019 and August 2021.
- Utilized AI and big data technologies for business and system requirement analysis.
- Developed a complication prediction module and employed big data mining for model building.
- Evaluated the gradient-boosted tree (GBT) model using precision, recall, and F1-score.
Main Results:
- The GBT model demonstrated superior performance in precision, F1-score, and area under the ROC curve.
- Patients were stratified into four risk groups (A, B, C, D) based on predicted scores.
- The overall predicted mortality rate (2.67%) was higher than the actual in-hospital mortality rate (1.05%).
- The model showed credible results in group B (intermediate risk), but still overestimated mortality (predicted 0.96% vs. actual 0.33%).
Conclusions:
- An AI and big data-based CABG risk prediction model was successfully constructed.
- The model tends to overestimate mortality risk in patients with intermediate risk.
- Further research and development are needed to refine the model for broader applicability across different heart conditions and improve accuracy.
Abstract:
The objective of this work was to predict the risk of mortality rate in patients with coronary artery bypass grafting (CABG) based on the risk prediction model of CABG using artificial intelligence (AI) and big data technologies. The clinical data of 2,364 patients undergoing CABG in our hospital from January 2019 to August 2021 were collected in this work. Based on AI and big data technology, business requirement analysis, system requirement analysis, complication prediction module, big data mining technology, and model building are carried out, respectively; the successful CABG risk prediction system includes case feature analysis service, risk warning service, and case retrieval service. The commonly used precision, recall, and F1-score were adopted to evaluate the quality of the gradient-boosted tree (GBT) model. The analysis proved that the GBT model was the best in terms of precision, F1-score, and area under the receiver operating characteristic curve (ROC). According to the CABG risk prediction model, 1,382 patients had a score of <0, 463 patients had a score of 0 ≤ score ≤ 2, 252 patients had a score of 2 < score ≤ 5, and 267 patients had a score of >5, which were stratified into four groups: A, B, C, and D. The actual number of in-hospital deaths was 25, and the in-hospital mortality rate was 1.05%. The mortality rate predicted by the CABG risk prediction model was 2.67 ± 1.82% (95% confidential interval (CI) (2.87-2.98)), which was higher than the actual value. The CABG risk prediction model showed the credible results only in group B with AUC = 0.763 > 0.7. In group B, 3 patients actually died, the actual mortality rate was 0.33%, and the predicted mortality rate was 0.96 ± 0.78 (95% CI (0.82-0.87)), which overestimated the mortality rate of patients in group B. It successfully constructed a CABG risk prediction model based on the AI and big data technologies, which would overestimate the mortality of patients with intermediate risk, and it is suitable for different types of heart diseases through continuous research and development and innovation, and provides clinical guidance value.
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