[基于 LSTM-XGBoost 的高血压患者 RR 间期时间序列预测方法]。

Q4 Medicine 中国医疗器械杂志 Pub Date : 2024-07-30 DOI:10.12455/j.issn.1671-7104.230728

Wenjie Yu, Hongwen Chen, Hongliang Qi, Zhilin Pan, Hanwei Li, Debin Hu

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引用次数: 0

摘要

目的预测高血压患者的RR间期有助于临床医生分析和预警患者的心脏状况：以 8 例患者数据为样本，采用长短期记忆网络（LSTM）和梯度提升树（XGBoost）对患者的 RR 间期进行预测，并通过反方差法将两种模型的预测结果进行合并，以克服单一模型预测的缺点：结果：与单一模型相比，所提出的组合模型在预测8名患者的RR间期方面有不同程度的改善：LSTM-XGBoost模型为预测高血压患者的RR间期提供了一种方法，具有潜在的临床可行性。

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[LSTM-XGBoost Based RR Intervals Time Series Prediction Method in Hypertensive Patients].

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.

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