Back to Search
Start Over
[LSTM-XGBoost Based RR Intervals Time Series Prediction Method in Hypertensive Patients].
- Source :
-
Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation [Zhongguo Yi Liao Qi Xie Za Zhi] 2024 Jul 30; Vol. 48 (4), pp. 392-395. - Publication Year :
- 2024
-
Abstract
- Objective: The prediction of RR intervals in hypertensive patients can help clinicians to analyze and warn patients' heart condition.<br />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.<br />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.<br />Conclusion: LSTM-XGBoost model provides a method for predicting RR intervals in hypertensive patients, which has potential clinical feasibility.
- Subjects :
- Humans
Neural Networks, Computer
Heart Rate
Algorithms
Hypertension
Subjects
Details
- Language :
- Chinese
- ISSN :
- 1671-7104
- Volume :
- 48
- Issue :
- 4
- Database :
- MEDLINE
- Journal :
- Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation
- Publication Type :
- Academic Journal
- Accession number :
- 39155251
- Full Text :
- https://doi.org/10.12455/j.issn.1671-7104.230728