Author, as appears in the article.: Fabregat A; Magret M; Ferré JA; Vernet A; Guasch N; Rodríguez A; Gómez J; Bodí M
Department: Medicina i Cirurgia Bioquímica i Biotecnologia Enginyeria Mecànica
URV's Author/s: Bodi Saera, Maria Amparo / Fabregat Tomàs, Alexandre / Ferré Vidal, Josep Anton / Gómez Alvarez, Josep / Magret Iglesias, Mònica / Vernet Peña, Antonio
Keywords: Support vector machine Reintubation Machine learning Invasive mechanical ventilation Gradient boosting Extubation Clinical decision support tool
Abstract: © 2020 Elsevier B.V. Background and Objective: To increase the success rate of invasive mechanical ventilation weaning in critically ill patients using Machine Learning models capable of accurately predicting the outcome of programmed extubations. Methods: The study population was adult patients admitted to the Intensive Care Unit. Target events were programmed extubations, both successful and failed. The working dataset is assembled by combining heterogeneous data including time series from Clinical Information Systems, patient demographics, medical records and respiratory event logs. Three classification learners have been compared: Logistic Discriminant Analysis, Gradient Boosting Method and Support Vector Machines. Standard methodologies have been used for preprocessing, hyperparameter tuning and resampling. Results: The Support Vector Machine classifier is found to correctly predict the outcome of an extubation with a 94.6% accuracy. Contrary to current decision-making criteria for extubation based on Spontaneous Breathing Trials, the classifier predictors only require monitor data, medical entry records and patient demographics. Conclusions: Machine Learning-based tools have been found to accurately predict the extubation outcome in critical patients with invasive mechanical ventilation. The use of this important predictive capability to assess the extubation decision could potentially reduce the rate of extubation failure, currently at 9%. With about 40% of critically ill patients eventually receiving invasive mechanical ventilation during their stay and given the serious potential complications associated to reintubation, the excellent predictive ability of the model presented here suggests that Machine Learning techniques could significantly improve the clinical outcomes of critical patients.
Thematic Areas: Software Saúde coletiva Psicología Odontología Medicina iii Medicina ii Medicina i Medical informatics Matemática / probabilidade e estatística Interdisciplinar Health informatics General medicine Engineering, biomedical Engenharias iv Engenharias iii Engenharias ii Educação física Computer science, theory & methods Computer science, interdisciplinary applications Computer science applications Ciências biológicas ii Ciências biológicas i Ciências ambientais Ciências agrárias i Ciência da computação Biotecnología
licence for use: https://creativecommons.org/licenses/by/3.0/es/
Author's mail: josep.gomez@urv.cat monica.magret@urv.cat alexandre.fabregat@urv.cat anton.vernet@urv.cat josep.a.ferre@urv.cat mariaamparo.bodi@urv.cat mariaamparo.bodi@urv.cat
Author identifier: 0000-0002-0573-7621 0000-0002-9534-9920 0000-0002-6032-2605 0000-0002-7028-1368 0000-0002-0831-0885 0000-0001-7652-8379 0000-0001-7652-8379
Record's date: 2024-07-27
Papper version: info:eu-repo/semantics/acceptedVersion
Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
Papper original source: Computer Methods And Programs In Biomedicine. 200 (105869): 105869-
APA: Fabregat A; Magret M; Ferré JA; Vernet A; Guasch N; Rodríguez A; Gómez J; Bodí M (2021). A Machine Learning decision-making tool for extubation in Intensive Care Unit patients. Computer Methods And Programs In Biomedicine, 200(105869), 105869-. DOI: 10.1016/j.cmpb.2020.105869
Entity: Universitat Rovira i Virgili
Journal publication year: 2021
Publication Type: Journal Publications
Repositori URV