Radiology Department, the Affiliated Changzhou No. 2 People's Hospital of Nanjing Medical University, Changzhou 213164, Jiangsu, China.
Cardio Thoracic Department, the Affiliated Changzhou No. 2 People's Hospital of Nanjing Medical University, Changzhou 213164, Jiangsu, China.
Comput Intell Neurosci. 2023 Jul 7;2023:9575553. doi: 10.1155/2023/9575553. eCollection 2023.
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.
本工作旨在基于人工智能（AI）和大数据技术的冠状动脉旁路移植术（CABG）风险预测模型，预测 CABG 患者的死亡率风险。收集了我院 2019 年 1 月至 2021 年 8 月 2364 例行 CABG 患者的临床资料。基于 AI 和大数据技术，分别进行业务需求分析、系统需求分析、并发症预测模块、大数据挖掘技术和模型构建；成功构建的 CABG 风险预测系统包括病例特征分析服务、风险预警服务和病例检索服务。常用的精度、召回率和 F1 评分用于评估梯度提升树（GBT）模型的质量。分析表明，GBT 模型在精度、F1 评分和受试者工作特征曲线（ROC）下面积方面表现最佳。根据 CABG 风险预测模型，1382 例患者的评分<0，463 例患者的评分 0≤评分≤2，252 例患者的评分 2<评分≤5，267 例患者的评分>5，将患者分为 4 个组：A、B、C 和 D。住院期间实际死亡人数为 25 例，住院死亡率为 1.05%。CABG 风险预测模型预测的死亡率为 2.67±1.82%（95%置信区间（CI）（2.87-2.98）），高于实际值。CABG 风险预测模型仅在 AUC=0.763>0.7 的 B 组中显示出可靠的结果。在 B 组中，实际死亡的患者为 3 例，实际死亡率为 0.33%，预测死亡率为 0.96±0.78（95%CI（0.82-0.87）），高估了 B 组患者的死亡率。成功构建了基于 AI 和大数据技术的 CABG 风险预测模型，该模型会高估中危患者的死亡率，通过不断的研究和开发创新，适用于不同类型的心脏病，为临床提供指导价值。
