摘要
目的 对高血压患者的RR间期进行预测,帮助临床医生对患者心脏状况进行分析和预警。方法 以8位患者数据为样本,通过长短期记忆网络(LSTM)和梯度提升树(XGBoost)分别对患者的RR间期进行预测,将2个模型的预测结果通过方差倒数法进行组合,克服单一模型预测的劣势。结果 提出的组合模型相较于单一模型在8位患者RR间期的预测上具有不同程度的改善效果。结论 LSTM-XGBoost模型为高血压患者RR间期预测提供了方法,具有一定的临床价值。
Objective The prediction of RR intervals in hypertensive patients can help clinicians to analyze and warn patients'heart condition.Methods Using 8 patients'data as samples,the RR intervals of patients were predicted by long short-term memory network(LSTM)and gradient lift tree(XGBoost),and the prediction results of the two models were combined by the inverse variance method to overcome the disadvantage of single model prediction.Results Compared with the single model,the proposed combined model had a different degree of improvement in the prediction of RR intervals in 8 patients.Conclusion LSTM-XGBoost model provides a method for predicting RR intervals in hypertensive patients,which has potential clinical feasibility.
作者
喻文杰
陈宏文
齐宏亮
潘智林
李翰威
胡德斌
YU Wenjie;CHEN Hongwen;QI Hongliang;PAN Zhilin;LI Hanwei;HU Debin(School of Biomedical Engineering,Southern Medical University,Guangzhou,510515;Nanfang Hospital,Southern Medical University,Guangzhou,510515)
出处
《中国医疗器械杂志》
2024年第4期392-395,共4页
Chinese Journal of Medical Instrumentation
基金
国家卫生健康委医院管理研究所医学工程研究项目(2022MEA108)
国家重点研发计划(2023YFC2414601)
广东省重点领域研发计划(2019B111103001)。
关键词
RR间期
长短期记忆网络
梯度提升
时序预测
高血压
RR intervals
long short-term memory network
gradient lift tree
time series forecasting
hypertension
