Cited 0 times in Scipus Cited Count

Development of a Machine Learning Model for Predicting Weaning Outcomes Based Solely on Continuous Ventilator Parameters during Spontaneous Breathing Trials

DC Field Value Language
dc.contributor.authorPark, JE-
dc.contributor.authorKim, DY-
dc.contributor.authorPark, JW-
dc.contributor.authorJung, YJ-
dc.contributor.authorLee, KS-
dc.contributor.authorPark, JH-
dc.contributor.authorSheen, SS-
dc.contributor.authorPark, KJ-
dc.contributor.authorSunwoo, MH-
dc.contributor.authorChung, WY-
dc.date.accessioned2023-12-11T05:42:42Z-
dc.date.available2023-12-11T05:42:42Z-
dc.date.issued2023-
dc.identifier.urihttp://repository.ajou.ac.kr/handle/201003/32036-
dc.description.abstractDiscontinuing mechanical ventilation remains challenging. We developed a machine learning model to predict weaning outcomes using only continuous monitoring parameters obtained from ventilators during spontaneous breathing trials (SBTs). Patients who received mechanical ventilation in the medical intensive care unit at a tertiary university hospital from 2019–2021 were included in this study. During the SBTs, three waveforms and 25 numerical data were collected as input variables. The proposed convolutional neural network (CNN)-based weaning prediction model extracts features from input data with diverse lengths. Among 138 enrolled patients, 35 (25.4%) experienced weaning failure. The dataset was randomly divided into training and test sets (8:2 ratio). The area under the receiver operating characteristic curve for weaning success by the prediction model was 0.912 (95% confidence interval [CI], 0.795–1.000), with an area under the precision-recall curve of 0.767 (95% CI, 0.434–0.983). Furthermore, we used gradient-weighted class activation mapping technology to provide visual explanations of the model’s prediction, highlighting influential features. This tool can assist medical staff by providing intuitive information regarding readiness for extubation without requiring any additional data collection other than SBT data. The proposed predictive model can assist clinicians in making ventilator weaning decisions in real time, thereby improving patient outcomes.-
dc.language.isoen-
dc.titleDevelopment of a Machine Learning Model for Predicting Weaning Outcomes Based Solely on Continuous Ventilator Parameters during Spontaneous Breathing Trials-
dc.typeArticle-
dc.identifier.pmid37892893-
dc.identifier.urlhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10604888-
dc.contributor.affiliatedAuthorPark, JE-
dc.contributor.affiliatedAuthorPark, JW-
dc.contributor.affiliatedAuthorJung, YJ-
dc.contributor.affiliatedAuthorLee, KS-
dc.contributor.affiliatedAuthorPark, JH-
dc.contributor.affiliatedAuthorSheen, SS-
dc.contributor.affiliatedAuthorPark, KJ-
dc.contributor.affiliatedAuthorChung, WY-
dc.type.localJournal Papers-
dc.identifier.doi10.3390/bioengineering10101163-
dc.citation.titleBioengineering (Basel, Switzerland)-
dc.citation.volume10-
dc.citation.number10-
dc.citation.date2023-
dc.citation.startPage1163-
dc.citation.endPage1163-
dc.identifier.bibliographicCitationBioengineering (Basel, Switzerland), 10(10). : 1163-1163, 2023-
dc.identifier.eissn2306-5354-
dc.relation.journalidJ023065354-
Appears in Collections:
Journal Papers > School of Medicine / Graduate School of Medicine > Pulmonary & Critical Care Medicine
Files in This Item:
37892893.pdfDownload

qrcode

해당 아이템을 이메일로 공유하기 원하시면 인증을 거치시기 바랍니다.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Browse