arXiv 1812.005962018-12-03cs.LGq-bio.QMstat.ML

深度学习方法预测冠状动脉旁路移植术后30天再入院

Deep Learning Approach for Predicting 30 Day Readmissions after Coronary Artery Bypass Graft Surgery

Georgia State University（佐治亚州立大学）；Emory University（埃默里大学）；University of North Georgia（北佐治亚大学）

Ramesh B. Manyam, Yanqing Zhang, William B. Keeling, Jose Binongo, Michael Kayatta, Seth Carter

备注：: Machine Learning for Health (ML4H) Workshop at NeurIPS 2018 arXiv:1811.07216

摘要：

冠状动脉旁路移植术（CABG）术后出院后30天内的再次入院是医疗保健成本的主要贡献者。开发了许多预测模型来识别再入院的风险因素。然而，大多数现有的模型使用统计分析技术与数据在出院。我们提出了一个集成模型，使用出院前围手术期数据和机器学习生存分析技术来预测CABG再入院。首先，我们将51个潜在的再入院风险变量应用Cox比例风险（CPH）生存回归单变量分析。其中14个结果是显著的（p值< 0.05），有助于再入院。随后，我们将这14个预测因子应用于多变量CPH模型和CPH模型DeepSurv的深度学习神经网络（NN）表示。我们用453例孤立的成人CABG病例验证了这种新的整体模型。14个围手术期风险变量中有9个被确定为最显著的风险比（HR）大于1.0。然后使用训练和验证数据集评估CPH，DeepSurv和集成模型的一致性指数指标。我们的集成模型在c统计方面产生了有希望的结果，因为我们提高了迭代次数和数据集大小。使用机器学习生存分析技术，通过围手术期出院前数据可以更有效地预测孤立CABG患者30天全因再入院。预测精度水平可以通过深度学习算法进一步提高。

英文摘要：

Hospital Readmissions within 30 days after discharge following Coronary Artery Bypass Graft (CABG) Surgery are substantial contributors to healthcare costs. Many predictive models were developed to identify risk factors for readmissions. However, majority of the existing models use statistical analysis techniques with data available at discharge. We propose an ensembled model to predict CABG readmissions using pre-discharge perioperative data and machine learning survival analysis techniques. Firstly, we applied fifty one potential readmission risk variables to Cox Proportional Hazard (CPH) survival regression univariate analysis. Fourteen of them turned out to be significant (with p value < 0.05), contributing to readmissions. Subsequently, we applied these 14 predictors to multivariate CPH model and Deep Learning Neural Network (NN) representation of the CPH model, DeepSurv. We validated this new ensembled model with 453 isolated adult CABG cases. Nine of the fourteen perioperative risk variables were identified as the most significant with Hazard Ratios (HR) of greater than 1.0. The concordance index metrics for CPH, DeepSurv, and ensembled models were then evaluated with training and validation datasets. Our ensembled model yielded promising results in terms of c-statistics, as we raised the the number of iterations and data set sizes. 30 day all-cause readmissions among isolated CABG patients can be predicted more effectively with perioperative pre-discharge data, using machine learning survival analysis techniques. Prediction accuracy levels could be improved further with deep learning algorithms.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。