使用多元自回归模型预测连续生命体征。

Forecasting of Continuous Vital Sign Using Multivariate Auto-Regressive Models.

出版信息

Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:385-388. doi: 10.1109/EMBC48229.2022.9871010.

DOI:10.1109/EMBC48229.2022.9871010

Abstract

This project assessed the use of multivariate auto-regressive (MAR) models to create forecasts of continuous vital signs in hospitalized patients. A total of 20 hours continuous (1/60Hz) heart rate and respiration rate from eight postoperative patients, where used to fit a centered MAR model for forecasting in windows of 15 minutes. The model was fitted using Markov Chain Monte Carlo sampling, and the model was evaluated on data from five additional patients. The results demonstrate an average RMSE in the forecast window of 11.4 (SD: 7.30) beats per minute for heart rate and 3.3 (SD:1.3) breaths per minute for respiration rate. These results indicate potential for forecasting vital signs in a clinical setting.

摘要

本项目评估了使用多元自回归（MAR）模型来预测住院患者连续生命体征的方法。使用了 8 名术后患者总共 20 小时的连续（1/60Hz）心率和呼吸率数据，以 15 分钟为窗口拟合中心化 MAR 模型进行预测。该模型使用马尔可夫链蒙特卡罗抽样进行拟合，并在另外 5 名患者的数据上进行了评估。结果表明，心率预测窗口的平均 RMSE 为 11.4（SD：7.30）次/分钟，呼吸率的 RMSE 为 3.3（SD：1.3）次/分钟。这些结果表明在临床环境中预测生命体征具有潜力。