Forecasting of Continuous Vital Sign Using Multivariate Auto-Regressive Models

Autor:	Soren M. Rasmussen, Jesper Molgaard, Camilla Haahr-Raunkjaer, Christian S. Meyhoff, Eske Aasvang, Helge B. D. Sorensen
Jazyk:	angličtina
Rok vydání:	2022
Předmět:	Markov processes Heart rate Data models Time series analysis Monte Carlo methods Time measurement Markov Chains Body Fluids Predictive models Respiratory Rate Heart Rate Seizures Humans Monte Carlo Method
Zdroj:	Rasmussen, S M, Molgaard, J, Haahr-Raunkjaer, C, Meyhoff, C S, Aasvang, E & Sorensen, H B D 2022, Forecasting of Continuous Vital Sign Using Multivariate Auto-Regressive Models . in 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022 . IEEE, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, vol. 2022-July, pp. 385-388, 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022, Glasgow, United Kingdom, 11/07/2022 . https://doi.org/10.1109/EMBC48229.2022.9871010 Rasmussen, S M, Mølgaard, J, Haahr-Raunkjær, C, Meyhoff, C S, Aasvang, E & Sørensen, H B D 2022, Forecasting of Continuous Vital Sign Using Multivariate Auto-Regressive Models . in Proceedings of 44 th Annual International Conference of the IEEE Engineering in Medicine & Biology Society ., 9871010, IEEE, pp. 385-388, 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Glasgow, United Kingdom, 11/07/2022 . https://doi.org/10.1109/EMBC48229.2022.9871010
Popis:	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.
Databáze:	OpenAIRE
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