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
Brekke, I.J.; Puntervoll, L.H.; Pedersen, P.B.; Kellett, J.; Brabr, M. The value of vital sign trends in predicting and monitoring clinical deterioration: A systematic review. PLoS ONE 2019, 14, e0210875. [CrossRef] [PubMed]
Kause, J.; Smith, G.; Prytherch, D.; Parr, M.; Flabouris, A.; Hillman, K. A comparison of Antecedents to Cardiac Arrests, Deaths and Emergency Intensive care Admissions in Australia and New Zealand, and the United Kingdom—The ACADEMIA study. Resuscitation 2004, 62, 275–82. [CrossRef] [PubMed]
Barfod, C.; Lauritzen, M.M.P.; Danker, J.K.; Sölétormos, G.; Forberg, J.L.; Berlac, P.A.; Lippert, F.; Lundstrøm, L.H.; Antonsen, K.; Lange, K.H.W. Abnormal vital signs are strong predictors for intensive care unit admission and in-hospital mortality in adults triaged in the emergency department—A prospective cohort study. Scand. Trauma Resusc. Emerg. Med. 2012, 10, 20–28. [CrossRef] [PubMed]
Youssef Ali Amer, A.; Vranken, J.; Wouters, F.; Mesotten, D.; Vandervoort, P.; Storms, V.; Luca, S.; Vanrumste, B.; Aerts, J.-M. Feature engineering for ICU mortality prediction based on hourly to bi-hourly measurements. Appl. Sci. 2019, 9, 3525. [CrossRef]
Redfern Oliver C.; Pimentel, M.A.F.; David, P.; Meredith, P. Predicting in-hospital mortality and unanticipated admissions to the intensive care unit using routinely collected blood tests and vital signs: Development and validation of a multivariable model. Resuscitation 2018, 133, 75–81. [CrossRef] [PubMed]
Mahdavi, M.; Choubdar, H.; Zabeh, E.; Rieder, M.; Safavi-Naeini, S. A machine learning based exploration of COVID-19 mortality risk. PLoS ONE 2021, 16, e0252384. [CrossRef] [PubMed]
Liu, S.; Yao, J.; Motani, M. Early Prediction of Vital Signs Using Generative Boosting via LSTM Networks. In Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine (BIBM), San Diego, CA, USA, 18–21 November 2019.
Youssef Ali Amer, A.; Wouters, F.; Vranken, J.; de Korte-de Boer, D.; Smit-Fun, V.; Duflot, P.; Beaupain, M.-H.; Vandervoort, P.; Luca, S.; Aerts, J.-M.; et al. Vital Signs Prediction and Early Warning Score Calculation Based on Continuous Monitoring of Hospitalised Patients Using Wearable Technology. Sensors 2020, 20, 6593. [CrossRef] [PubMed]
Han, T.; Jiang, D.; Zhao, Q.; Wang, L.; Yin, K. Comparison of random forest, artificial neural networks and support vector machine for intelligent diagnosis of rotating machinery. Trans. Inst. Meas. Control 2018, 40, 2681–2693. [CrossRef]
Youssef Ali Amer, A.; Aerts, J.; Vanrumste, B.; Luca, S. A Localised Learning Approach Applied to Human Activity Recognition. IEEE Intell. Syst. 2020, 99, 58–71.
Youssef Ali Amer, A. Localised Least Squares Support Vector Machines with Application to Weather Forecasting. Master’s Thesis, KU Leuven, Leuven, Belgium, 2016.
Cheng, H.; Tan, P.-N.; Jin, R.Localized support vector machine and its efficient algorithm. In Proceedings of the SIAM International Conference on Data Mining, Minneapolis, MN, USA, 26–28 April 2007.
Cheng, H.; Tan, P.; Jin, R. Efficient algorithm for localized support vector machine. IEEE Trans. Knowl. Data Eng. 2010, 22, 537–549. [CrossRef]
Bottou, L.; Vapnik, V. Local Learning Algorithms. Neural Comput. 1992, 4, 888–900. [CrossRef]
Yang, H.; Huang, K.; King, I.; Lyu, M.R. Localized support vector regression for time series prediction. Neurocomputing 2009, 72, 10–12. [CrossRef]
Suykens, J.A.K.; Vandewalle, J. Least Squares Support Vector Machine Classifiers. Neural Process. Lett. 1999, 9, 293–300. [CrossRef]
Suykens, J.A.K.; De Brabanter, J.; Lukas, L.; Vandewalle, J. Weighted least squares support vector machines: Robustness and sparse approximation. Neurocomputing 2002, 48, 85–105. [CrossRef]
Cayton, L. Fast nearest neighbor retrieval for bregman divergences. In Proceedings of the 25th International Conference on Machine Learning, Helsinki, Finland, 5–9 July 2008; pp. 112–119, ISBN 9781605582054. [CrossRef]