随机森林
Lasso(编程语言)
回归分析
回归
线性回归
支持向量机
计算机科学
统计
血压
血液透析
数学
医学
人工智能
内科学
万维网
作者
Jiun‐Chi Huang,Yi‐Chun Tsai,Pei-Yu Wu,Yu-Hui Lien,Chih-Yi Chien,Chih-Feng Kuo,Jeng-Fung Hung,Szu‐Chia Chen,Chao‐Hung Kuo
标识
DOI:10.1016/j.cmpb.2020.105536
摘要
Abstract Background Intradialytic hypotension (IDH) is commonly occurred and links to higher mortality among patients undergoing hemodialysis (HD). Its early prediction and prevention will dramatically improve the quality of life. However, predicting the occurrence of IDH clinically is not simple. The aims of this study are to develop an intelligent system with capability of predicting blood pressure (BP) during HD, and to further compare different machine learning algorithms for next systolic BP (SBP) prediction. Methods This study presented comprehensive comparisons among linear regression model, least absolute shrinkage and selection operator (LASSO), tree-based ensemble machine learning models (random forest [RF] and extreme gradient boosting [XGBoost]), and support vector regression to predict the BP during HD treatment based on 200 and 48 maintenance HD patients containing a total of 7,180 and 2,065 BP records for the training and test dataset, respectively. Ensemble method also was computed to obtain better predictive performance. We compared the developed models based on R2, root mean square error (RMSE) and mean absolute error (MAE). Results We found that RF (R2=0.95, RMSE=6.64, MAE=4.90) and XGBoost (R2=1.00, RMSE=1.83, MAE=1.29) had comparable predictive performance on the training dataset. However, RF (R2=0.49, RMSE=16.24, MAE=12.14) had more accurate than XGBoost (R2=0.41, RMSE=17.65, MAE=13.47) on testing dataset. Among these models, the ensemble method (R2=0.50, RMSE=16.01, MAE=11.97) had the best performance on testing dataset for next SBP prediction. Conclusions We compared five machine learning and an ensemble method for next SBP prediction. Among all studied algorithms, th e RF and the ensemble method have the better predictive performance. The prediction models using ensemble method for intradialytic BP profiling may be able to assist the HD staff or physicians in individualized care and prompt intervention for patients’ safety and improve care of HD patients.
科研通智能强力驱动
Strongly Powered by AbleSci AI