COVID-19; ICU; kNN-LS-SVM; vital signs prediction; Humans; Intensive Care Units; SARS-CoV-2; Vital Signs; Oxygen Saturation; Analytical Chemistry; Information Systems; Atomic and Molecular Physics, and Optics; Biochemistry; Instrumentation; Electrical and Electronic Engineering
Abstract :
[en] This study introduces machine learning predictive models to predict the future values of the monitored vital signs of COVID-19 ICU patients. The main vital sign predictors include heart rate, respiration rate, and oxygen saturation. We investigated the performances of the developed predictive models by considering different approaches. The first predictive model was developed by considering the following vital signs: heart rate, blood pressure (systolic, diastolic and mean arterial, pulse pressure), respiration rate, and oxygen saturation. Similar to the first approach, the second model was developed using the same vital signs, but it was trained and tested based on a leave-one-subject-out approach. The third predictive model was developed by considering three vital signs: heart rate (HR), respiration rate (RR), and oxygen saturation (SpO2). The fourth model was a leave-one-subject-out model for the three vital signs. Finally, the fifth predictive model was developed based on the same three vital signs, but with a five-minute observation rate, in contrast with the aforementioned four models, where the observation rate was hourly to bi-hourly. For the five models, the predicted measurements were those of the three upcoming observations (on average, three hours ahead). Based on the obtained results, we observed that by limiting the number of vital sign predictors (i.e., three vital signs), the prediction performance was still acceptable, with the average mean absolute percentage error (MAPE) being 12%,5%, and 21.4% for heart rate, oxygen saturation, and respiration rate, respectively. Moreover, increasing the observation rate could enhance the prediction performance to be, on average, 8%,4.8%, and 17.8% for heart rate, oxygen saturation, and respiration rate, respectively. It is envisioned that such models could be integrated with monitoring systems that could, using a limited number of vital signs, predict the health conditions of COVID-19 ICU patients in real-time.
Disciplines :
Cardiovascular & respiratory systems
Author, co-author :
Amer, Ahmed Youssef Ali ; 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, Ziekenhuis Oost-Limburg, 3600 Genk, Belgium ; Department of Anesthesiology, Ziekenhuis Oost-Limburg, 3600 Genk, Belgium ; Department of Cardiology and 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, Ziekenhuis Oost-Limburg, 3600 Genk, Belgium ; Department of Anesthesiology, Ziekenhuis Oost-Limburg, 3600 Genk, Belgium ; Department of Cardiology and Future Health, Ziekenhuis Oost-Limburg, 3600 Genk, Belgium
Dreesen, Pauline ; 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, Ziekenhuis Oost-Limburg, 3600 Genk, Belgium ; Department of Anesthesiology, Ziekenhuis Oost-Limburg, 3600 Genk, Belgium ; Department of Cardiology and Future Health, Ziekenhuis Oost-Limburg, 3600 Genk, Belgium
de Korte-de Boer, Dianne ; Department of Anesthesiology and Pain Management, Maastricht University Medical Centre+, 6229 HX Maastricht, The Netherlands
van Rosmalen, Frank ; Department of Intensive Care, Maastricht University Medical Centre+, 6229 HX Maastricht, The Netherlands
van Bussel, Bas C T ; Department of Intensive Care, Maastricht University Medical Centre+, 6229 HX Maastricht, The Netherlands
Smit-Fun, Valérie ; Department of Anesthesiology and Pain Management, Maastricht University Medical Centre+, 6229 HX Maastricht, The Netherlands
Duflot, Patrick ; Centre Hospitalier Universitaire de Liège - CHU > > Secteur Appui méthodologique aux Projets GSI et Planification (APP)
GUIOT, Julien ; Centre Hospitalier Universitaire de Liège - CHU > > Service de pneumologie - allergologie
van der Horst, Iwan C C ; Department of Intensive Care, Maastricht University Medical Centre+, 6229 HX Maastricht, The Netherlands
Mesotten, Dieter; 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, Ziekenhuis Oost-Limburg, 3600 Genk, Belgium ; Department of Anesthesiology, Ziekenhuis Oost-Limburg, 3600 Genk, Belgium ; Department of Cardiology and Future Health, Ziekenhuis Oost-Limburg, 3600 Genk, Belgium
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, Ziekenhuis Oost-Limburg, 3600 Genk, Belgium ; Department of Anesthesiology, Ziekenhuis Oost-Limburg, 3600 Genk, Belgium ; Department of Cardiology and Future Health, Ziekenhuis Oost-Limburg, 3600 Genk, 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
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