Abstract
Due to COVID-19 related complications, many of the diagnosed patients end up needing intensive care. Complications are often severe, to such an extent that mortality rates in these patients may be high. Among the wide variety of complications, we find necrotizing tracheobronchitis, which appears suddenly with the obstruction of the endotracheal tube. This complication can cause severe damage to the patient or even death. In order to help clinicians with the management of this situation, we propose a Machine Learning-based methodology for detecting and anticipating the obstruction phenomenon. Through the use of Bayesian classifiers, classifier combination, morphological filtering and a track-while-scan detection mode we are able to establish an indicator function that serves as a reference to clinicians. Our experiments show promising results and lay the foundations of an intelligent system for early detection of endotracheal obstruction.
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References
Pérez Acosta, G., Navarro Mesa, J.L., Blanco López, J., Santana Cabrera, L., Suárez Araujo, C.P., Martín González, J.C.: Prediction model of endotracheal obstruction, in patients with severe pneumonia due to COVID 19, analyzing clinical parameters using intelligent computing. In: 33rd Annual Congress of the European Society of Intensive Care Medicine, vol. 8, 2 (2020)
Pérez Acosta, G., Navarro Mesa, J.L., Blanco López, J., Santana Cabrera, L., Suárez Araujo, C.P., Martín González, J.C.: Prediction model of endotracheal obstruction, in patients with severe pneumonia due to COVID-19, analyzing ventialtory parameters using intelligent computation. In: 33rd Annual Congress of the European Society of Intensive Care Medicine (ESICM) (2020)
Pham, T., Brochard, L.J., Slutsky, A.S.: Mechanical ventilation: state of the art. Mayo Clin. Proc. 92(9), 1382–1400 (2017). https://doi.org/10.1016/j.mayocp.2017.05.004
Silva, P.L., Pelosi, P., Rocco, P.R.M.: Optimal mechanical ventilation strategies to minimize ventilator-induced lung injury in non-injured and injured lungs. Expert Rev. Resp. Med. 10(12), 1243–1245 (2016). https://doi.org/10.1080/17476348.2016.1251842
Bock, K.R., Silver, P., Rom, M., Sagy, M.: Reduction in tracheal lumen due to endotracheal intubation and its calculated clinical significance. Chest 118(2), 468–472 (2000). https://doi.org/10.1378/chest.118.2.468
PICIS: ICU Care Manager Software|Patient Care Unit System|Picis (2021). https://www.picis.com/en/solution/clinical-information-system-suite/critical-care-manager/. Accessed 25 Apr 2021
Fukunaga, K.: Introduction to Statistical Pattern Recognition, 2nd edn. Academic Press (1990)
Max, J.: Quantizing for minimum distortion. IRE Trans. Inf. Theory 6(1), 7–12 (1960). https://doi.org/10.1109/TIT.1960.1057548
Lloyd, S.P.: Least squares quantization in PCM. IEEE Trans. Inf. Theory 28(2), 129–137 (1982). https://doi.org/10.1109/TIT.1982.1056489
Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms. 2nd edn. John Wiley & Sons, Inc. (2014)
Polikar, R.: Ensemble based systems in decision making. IEEE Circuits Syst. Mag. 6(3), 21–44 (2006). https://doi.org/10.1109/MCAS.2006.1688199
Worsham, R.: The probabilities of track initiation and loss using a sliding window for track acquisition. In: IEEE National Radar Conference - Proceedings, pp. 1270–1275 (2010). https://doi.org/10.1109/RADAR.2010.5494424
Acknowledgement
This research work has been founded by the University of Las Palmas de Gran Canaria through the call “Proyectos de Investigación COVID-19”. Our project title is “Aplicación de técnicas de machine learning para la detección temprana de obstrucción del tubo endotraqueal en pacientes COVID-19 en UCI”, ref. COVID 19–11.
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Suárez-Díaz, F.J. et al. (2021). A Bayesian Classifier Combination Methodology for Early Detection of Endotracheal Obstruction of COVID-19 Patients in ICU. In: Rojas, I., Joya, G., Català, A. (eds) Advances in Computational Intelligence. IWANN 2021. Lecture Notes in Computer Science(), vol 12861. Springer, Cham. https://doi.org/10.1007/978-3-030-85030-2_5
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DOI: https://doi.org/10.1007/978-3-030-85030-2_5
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