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Vital Signs Prediction and Early Warning Score Calculation Based on Continuous Monitoring of Hospitalised Patients Using Wearable Technology
In this prospective, interventional, international study, we investigate continuous monitoring of hospitalised patients’ vital signs using wearable technology as a basis for real-time early warning scores (EWS) estimation and vital signs time-series prediction. The collected continuous monitored vit...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7698871/ https://www.ncbi.nlm.nih.gov/pubmed/33218084 http://dx.doi.org/10.3390/s20226593 |
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author | Youssef Ali Amer, Ahmed Wouters, Femke Vranken, Julie de Korte-de Boer, Dianne Smit-Fun, Valérie Duflot, Patrick Beaupain, Marie-Hélène Vandervoort, Pieter Luca, Stijn Aerts, Jean-Marie Vanrumste, Bart |
author_facet | Youssef Ali Amer, Ahmed Wouters, Femke Vranken, Julie de Korte-de Boer, Dianne Smit-Fun, Valérie Duflot, Patrick Beaupain, Marie-Hélène Vandervoort, Pieter Luca, Stijn Aerts, Jean-Marie Vanrumste, Bart |
author_sort | Youssef Ali Amer, Ahmed |
collection | PubMed |
description | In this prospective, interventional, international study, we investigate continuous monitoring of hospitalised patients’ vital signs using wearable technology as a basis for real-time early warning scores (EWS) estimation and vital signs time-series prediction. The collected continuous monitored vital signs are heart rate, blood pressure, respiration rate, and oxygen saturation of a heterogeneous patient population hospitalised in cardiology, postsurgical, and dialysis wards. Two aspects are elaborated in this study. The first is the high-rate (every minute) estimation of the statistical values (e.g., minimum and mean) of the vital signs components of the EWS for one-minute segments in contrast with the conventional routine of 2 to 3 times per day. The second aspect explores the use of a hybrid machine learning algorithm of kNN-LS-SVM for predicting future values of monitored vital signs. It is demonstrated that a real-time implementation of EWS in clinical practice is possible. Furthermore, we showed a promising prediction performance of vital signs compared to the most recent state of the art of a boosted approach of LSTM. The reported mean absolute percentage errors of predicting one-hour averaged heart rate are 4.1, 4.5, and 5% for the upcoming one, two, and three hours respectively for cardiology patients. The obtained results in this study show the potential of using wearable technology to continuously monitor the vital signs of hospitalised patients as the real-time estimation of EWS in addition to a reliable prediction of the future values of these vital signs is presented. Ultimately, both approaches of high-rate EWS computation and vital signs time-series prediction is promising to provide efficient cost-utility, ease of mobility and portability, streaming analytics, and early warning for vital signs deterioration. |
format | Online Article Text |
id | pubmed-7698871 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-76988712020-11-29 Vital Signs Prediction and Early Warning Score Calculation Based on Continuous Monitoring of Hospitalised Patients Using Wearable Technology Youssef Ali Amer, Ahmed Wouters, Femke Vranken, Julie de Korte-de Boer, Dianne Smit-Fun, Valérie Duflot, Patrick Beaupain, Marie-Hélène Vandervoort, Pieter Luca, Stijn Aerts, Jean-Marie Vanrumste, Bart Sensors (Basel) Article In this prospective, interventional, international study, we investigate continuous monitoring of hospitalised patients’ vital signs using wearable technology as a basis for real-time early warning scores (EWS) estimation and vital signs time-series prediction. The collected continuous monitored vital signs are heart rate, blood pressure, respiration rate, and oxygen saturation of a heterogeneous patient population hospitalised in cardiology, postsurgical, and dialysis wards. Two aspects are elaborated in this study. The first is the high-rate (every minute) estimation of the statistical values (e.g., minimum and mean) of the vital signs components of the EWS for one-minute segments in contrast with the conventional routine of 2 to 3 times per day. The second aspect explores the use of a hybrid machine learning algorithm of kNN-LS-SVM for predicting future values of monitored vital signs. It is demonstrated that a real-time implementation of EWS in clinical practice is possible. Furthermore, we showed a promising prediction performance of vital signs compared to the most recent state of the art of a boosted approach of LSTM. The reported mean absolute percentage errors of predicting one-hour averaged heart rate are 4.1, 4.5, and 5% for the upcoming one, two, and three hours respectively for cardiology patients. The obtained results in this study show the potential of using wearable technology to continuously monitor the vital signs of hospitalised patients as the real-time estimation of EWS in addition to a reliable prediction of the future values of these vital signs is presented. Ultimately, both approaches of high-rate EWS computation and vital signs time-series prediction is promising to provide efficient cost-utility, ease of mobility and portability, streaming analytics, and early warning for vital signs deterioration. MDPI 2020-11-18 /pmc/articles/PMC7698871/ /pubmed/33218084 http://dx.doi.org/10.3390/s20226593 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Youssef Ali Amer, Ahmed Wouters, Femke Vranken, Julie de Korte-de Boer, Dianne Smit-Fun, Valérie Duflot, Patrick Beaupain, Marie-Hélène Vandervoort, Pieter Luca, Stijn Aerts, Jean-Marie Vanrumste, Bart Vital Signs Prediction and Early Warning Score Calculation Based on Continuous Monitoring of Hospitalised Patients Using Wearable Technology |
title | Vital Signs Prediction and Early Warning Score Calculation Based on Continuous Monitoring of Hospitalised Patients Using Wearable Technology |
title_full | Vital Signs Prediction and Early Warning Score Calculation Based on Continuous Monitoring of Hospitalised Patients Using Wearable Technology |
title_fullStr | Vital Signs Prediction and Early Warning Score Calculation Based on Continuous Monitoring of Hospitalised Patients Using Wearable Technology |
title_full_unstemmed | Vital Signs Prediction and Early Warning Score Calculation Based on Continuous Monitoring of Hospitalised Patients Using Wearable Technology |
title_short | Vital Signs Prediction and Early Warning Score Calculation Based on Continuous Monitoring of Hospitalised Patients Using Wearable Technology |
title_sort | vital signs prediction and early warning score calculation based on continuous monitoring of hospitalised patients using wearable technology |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7698871/ https://www.ncbi.nlm.nih.gov/pubmed/33218084 http://dx.doi.org/10.3390/s20226593 |
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