Schrempf Michael, Kramer Diether, Jauk Stefanie, Veeranki Sai P K, Leodolter Werner, Rainer Peter P
Steiermärkische Krankenanstaltengesellschaft m. b. H., Graz, Austria.
Medical University of Graz, Graz, Austria.
Stud Health Technol Inform. 2021 May 7;279:136-143. doi: 10.3233/SHTI210100.
Patients with major adverse cardiovascular events (MACE) such as myocardial infarction or stroke suffer from frequent hospitalizations and have high mortality rates. By identifying patients at risk at an early stage, MACE can be prevented with the right interventions.
The aim of this study was to develop machine learning-based models for the 5-year risk prediction of MACE.
The data used for modelling included electronic medical records of more than 128,000 patients including 29,262 patients with MACE. A feature selection based on filter and embedded methods resulted in 826 features for modelling. Different machine learning methods were used for modelling on the training data.
A random forest model achieved the best calibration and discriminative performance on a separate test data set with an AUROC of 0.88.
The developed risk prediction models achieved an excellent performance in the test data. Future research is needed to determine the performance of these models and their clinical benefit in prospective settings.
患有心肌梗死或中风等主要不良心血管事件（MACE）的患者经常住院，死亡率很高。通过早期识别高危患者，可通过适当干预措施预防MACE。
本研究的目的是开发基于机器学习的MACE 5年风险预测模型。
用于建模的数据包括超过128,000名患者的电子病历，其中29,262名患有MACE。基于过滤和嵌入方法的特征选择产生了826个用于建模的特征。使用不同的机器学习方法对训练数据进行建模。
随机森林模型在单独的测试数据集上实现了最佳校准和判别性能，AUROC为0.88。
所开发的风险预测模型在测试数据中表现出色。需要进一步的研究来确定这些模型的性能及其在前瞻性环境中的临床益处。
