early warning score; kNN-LS-SVM; time-series prediction; vital signs; wearable technology; Oxygen; Hospitalization; Humans; Oxygen/blood; Prospective Studies; Respiratory Rate; Early Warning Score; Monitoring, Physiologic; Vital Signs; Wearable Electronic Devices; Continuous monitoring; Hybrid machine learning; International studies; Mean absolute percentage error; Prediction performance; Real-time estimation; Real-time implementations; Time series prediction; Analytical Chemistry; Information Systems; Atomic and Molecular Physics, and Optics; Biochemistry; Instrumentation; Electrical and Electronic Engineering
Abstract :
[en] 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.
Disciplines :
Computer science
Author, co-author :
Youssef Ali Amer, Ahmed ; E-MEDIA, STADIUS, Department of Electrical Engineering (ESAT), Campus Group T, KU Leuven, 3000 Leuven, Belgium ; Measure, Model & Manage Bioresponses (M3-BIORES), Department of Biosystems, KU Leuven, 3000 Leuven, Belgium
Wouters, Femke ; Limburg Clinical Research Center/Mobile Health Unit, Faculty of Medicine and Life Sciences, Hasselt University, 3500 Hasselt, Belgium ; Limburg Clinical Research Center/Mobile Health Unit, Department of Anesthesiology, Department of Cardiology and Department Future Health, Ziekenhuis Oost-Limburg, 3600 Genk, Belgium
Vranken, Julie ; Limburg Clinical Research Center/Mobile Health Unit, Faculty of Medicine and Life Sciences, Hasselt University, 3500 Hasselt, Belgium ; Limburg Clinical Research Center/Mobile Health Unit, Department of Anesthesiology, Department of Cardiology and Department Future Health, Ziekenhuis Oost-Limburg, 3600 Genk, Belgium
de Korte-de Boer, Dianne; Department of Anesthesiology and Pain Management, Maastricht UMC+, 6229 HX Maastricht, The Netherlands
Smit-Fun, Valérie ; Department of Anesthesiology and Pain Management, Maastricht UMC+, 6229 HX Maastricht, The Netherlands
Duflot, Patrick ; Centre Hospitalier Universitaire de Liège - CHU > > Secteur Appui méthodologique aux Projets GSI et Planification (APP)
BEAUPAIN, Marie-Hélène ; Centre Hospitalier Universitaire de Liège - CHU > > Unité équipe mobile Sart Tilman
Vandervoort, Pieter; Limburg Clinical Research Center/Mobile Health Unit, Faculty of Medicine and Life Sciences, Hasselt University, 3500 Hasselt, Belgium ; Limburg Clinical Research Center/Mobile Health Unit, Department of Anesthesiology, Department of Cardiology and Department Future Health, Ziekenhuis Oost-Limburg, 3600 Genk, Belgium
Luca, Stijn ; Department of Data Analysis and Mathematical Modelling, Ghent University, 9000 Ghent, Belgium
Aerts, Jean-Marie ; Measure, Model & Manage Bioresponses (M3-BIORES), Department of Biosystems, KU Leuven, 3000 Leuven, Belgium
Vanrumste, Bart; E-MEDIA, STADIUS, Department of Electrical Engineering (ESAT), Campus Group T, KU Leuven, 3000 Leuven, Belgium
Language :
English
Title :
Vital Signs Prediction and Early Warning Score Calculation Based on Continuous Monitoring of Hospitalised Patients Using Wearable Technology.
This research is funded by a European Union Grant through wearIT4health project. The wearIT4health project is being carried out within the context of the Interreg V-A Euregio Meuse-Rhine programme, with EUR 2,3 million coming from the European Regional Development Fund (ERDF). With the investment of EU funds in Interreg projects, the European Union directly invests in economic development, innovation, territorial development, social inclusion, and education in the Euregio Meuse-Rhine.Funding: This research is funded by a European Union Grant through wearIT4health project. The wearIT4health project is being carried out within the context of the Interreg V-A Euregio Meuse-Rhine programme, with EUR 2,3 million coming from the European Regional Development Fund (ERDF). With the investment of EU funds in Interreg projects, the European Union directly invests in economic development, innovation, territorial development, social inclusion, and education in the Euregio Meuse-Rhine.
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