Application of Extreme Learning Machine in the Survival Analysis of Chronic Heart Failure Patients With High Percentage of Censored Survival Time.
Application of Extreme Learning Machine in the Survival Analysis of Chronic Heart Failure Patients With High Percentage of Censored Survival Time.
复制标题
极限学习机在高删失生存时间百分比慢性心力衰竭患者生存分析中的应用。
DOI：
10.3389/fcvm.2021.726516
复制
发表时间：
2021
影响因子：
3.6
通讯作者：
Zhang Y
中科院分区：
文献类型：
--
作者：
Yang H;Tian J;Meng B;Wang K;Zheng C;Liu Y;Yan J;Han Q;Zhang Y
目的：探讨基于极限学习机的Cox模型在慢性心力衰竭患者生存分析中的应用。方法：收集2014年至2019年太原市两家三级甲等医院诊断为慢性心力衰竭的住院患者5279例的病历；以死亡为结果，经过特征选择后，构建Lasso Cox、随机生存森林（RSF）、基于极限学习机的Cox模型（ELM Cox）进行生存分析和预测；基于25%、50%和75%三种删失率的模拟数据探讨了三种模型的预测性能。结果：仿真结果表明，三种模型的预测性能均随着删失比例的增加而下降，其中ELM Cox模型总体表现最好；从实际慢性心力衰竭数据中筛选出的21个高影响力的生存预测因子构建的ELM Cox模型显示出最佳性能，C指数和综合Brier评分（IBS）分别为0.775（0.755，0.802）和0.166（0.150，0.182）。结论：ELM Cox模型在慢性心力衰竭患者的生存分析中表现出良好的判别性能；它对于具有高比例截尾生存时间的数据表现一致；因此，该模型可以帮助医生识别预后不良的高风险患者，并尽早对患者采取针对性的治疗措施。
Objective: To explore the application of the Cox model based on extreme learning machine in the survival analysis of patients with chronic heart failure. Methods: The medical records of 5,279 inpatients diagnosed with chronic heart failure in two grade 3 and first-class hospitals in Taiyuan from 2014 to 2019 were collected; with death as the outcome and after the feature selection, the Lasso Cox, random survival forest (RSF), and the Cox model based on extreme learning machine (ELM Cox) were constructed for survival analysis and prediction; the prediction performance of the three models was explored based on simulated data with three censoring ratios of 25, 50, and 75%. Results: Simulation results showed that the prediction performance of the three models decreased with increasing censoring proportion, and the ELM Cox model performed best overall; the ELM Cox model constructed with 21 highly influential survival predictors screened from actual chronic heart failure data showed the best performance with C-index and Integrated Brier Score (IBS) of 0.775(0.755, 0.802) and 0.166(0.150, 0.182), respectively. Conclusion: The ELM Cox model showed good discrimination performance in the survival analysis of patients with chronic heart failure; it performs consistently for data with a high proportion of censored survival time; therefore, the model could help physicians identify patients at high risk of poor prognosis and target therapeutic measures to patients as early as possible.