{"title":"[LSTM-XGBoost Based RR Intervals Time Series Prediction Method in Hypertensive Patients].","authors":"Wenjie Yu, Hongwen Chen, Hongliang Qi, Zhilin Pan, Hanwei Li, Debin Hu","doi":"10.12455/j.issn.1671-7104.230728","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>The prediction of RR intervals in hypertensive patients can help clinicians to analyze and warn patients' heart condition.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>Compared with the single model, the proposed combined model had a different degree of improvement in the prediction of RR intervals in 8 patients.</p><p><strong>Conclusion: </strong>LSTM-XGBoost model provides a method for predicting RR intervals in hypertensive patients, which has potential clinical feasibility.</p>","PeriodicalId":52535,"journal":{"name":"中国医疗器械杂志","volume":"48 4","pages":"392-395"},"PeriodicalIF":0.0000,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"中国医疗器械杂志","FirstCategoryId":"1087","ListUrlMain":"https://doi.org/10.12455/j.issn.1671-7104.230728","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Medicine","Score":null,"Total":0}
引用次数: 0
Abstract
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.
期刊介绍：
Chinese Journal of Medical Instrumentation mainly reports on the development, progress, research and development, production, clinical application, management, and maintenance of medical devices and biomedical engineering. Its aim is to promote the exchange of information on medical devices and biomedical engineering in China and turn the journal into a high-quality academic journal that leads academic directions and advocates academic debates.
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