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Development of an Interactive AI System for the Optimal Timing Prediction of Successful Weaning from Mechanical Ventilation for Patients in Respiratory Care Centers

Successful weaning from prolonged mechanical ventilation (MV) is an important issue in respiratory care centers (RCCs). Delayed or premature extubation increases both the risk of adverse outcomes and healthcare costs. However, the accurate evaluation of the timing of successful weaning from MV is ve...

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Autores principales: Liao, Kuang-Ming, Ko, Shian-Chin, Liu, Chung-Feng, Cheng, Kuo-Chen, Chen, Chin-Ming, Sung, Mei-I, Hsing, Shu-Chen, Chen, Chia-Jung
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9030191/
https://www.ncbi.nlm.nih.gov/pubmed/35454023
http://dx.doi.org/10.3390/diagnostics12040975
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author Liao, Kuang-Ming
Ko, Shian-Chin
Liu, Chung-Feng
Cheng, Kuo-Chen
Chen, Chin-Ming
Sung, Mei-I
Hsing, Shu-Chen
Chen, Chia-Jung
author_facet Liao, Kuang-Ming
Ko, Shian-Chin
Liu, Chung-Feng
Cheng, Kuo-Chen
Chen, Chin-Ming
Sung, Mei-I
Hsing, Shu-Chen
Chen, Chia-Jung
author_sort Liao, Kuang-Ming
collection PubMed
description Successful weaning from prolonged mechanical ventilation (MV) is an important issue in respiratory care centers (RCCs). Delayed or premature extubation increases both the risk of adverse outcomes and healthcare costs. However, the accurate evaluation of the timing of successful weaning from MV is very challenging in RCCs. This study aims to utilize artificial intelligence algorithms to build predictive models for the successful timing of the weaning of patients from MV in RCCs and to implement a dashboard with the best model in RCC settings. A total of 670 intubated patients in the RCC in Chi Mei Medical Center were included in the study. Twenty-six feature variables were selected to build the predictive models with artificial intelligence (AI)/machine-learning (ML) algorithms. An interactive dashboard with the best model was developed and deployed. A preliminary impact analysis was then conducted. Our results showed that all seven predictive models had a high area under the receiver operating characteristic curve (AUC), which ranged from 0.792 to 0.868. The preliminary impact analysis revealed that the mean number of ventilator days required for the successful weaning of the patients was reduced by 0.5 after AI intervention. The development of an AI prediction dashboard is a promising method to assist in the prediction of the optimal timing of weaning from MV in RCC settings. However, a systematic prospective study of AI intervention is still needed.
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spelling pubmed-90301912022-04-23 Development of an Interactive AI System for the Optimal Timing Prediction of Successful Weaning from Mechanical Ventilation for Patients in Respiratory Care Centers Liao, Kuang-Ming Ko, Shian-Chin Liu, Chung-Feng Cheng, Kuo-Chen Chen, Chin-Ming Sung, Mei-I Hsing, Shu-Chen Chen, Chia-Jung Diagnostics (Basel) Article Successful weaning from prolonged mechanical ventilation (MV) is an important issue in respiratory care centers (RCCs). Delayed or premature extubation increases both the risk of adverse outcomes and healthcare costs. However, the accurate evaluation of the timing of successful weaning from MV is very challenging in RCCs. This study aims to utilize artificial intelligence algorithms to build predictive models for the successful timing of the weaning of patients from MV in RCCs and to implement a dashboard with the best model in RCC settings. A total of 670 intubated patients in the RCC in Chi Mei Medical Center were included in the study. Twenty-six feature variables were selected to build the predictive models with artificial intelligence (AI)/machine-learning (ML) algorithms. An interactive dashboard with the best model was developed and deployed. A preliminary impact analysis was then conducted. Our results showed that all seven predictive models had a high area under the receiver operating characteristic curve (AUC), which ranged from 0.792 to 0.868. The preliminary impact analysis revealed that the mean number of ventilator days required for the successful weaning of the patients was reduced by 0.5 after AI intervention. The development of an AI prediction dashboard is a promising method to assist in the prediction of the optimal timing of weaning from MV in RCC settings. However, a systematic prospective study of AI intervention is still needed. MDPI 2022-04-13 /pmc/articles/PMC9030191/ /pubmed/35454023 http://dx.doi.org/10.3390/diagnostics12040975 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Liao, Kuang-Ming
Ko, Shian-Chin
Liu, Chung-Feng
Cheng, Kuo-Chen
Chen, Chin-Ming
Sung, Mei-I
Hsing, Shu-Chen
Chen, Chia-Jung
Development of an Interactive AI System for the Optimal Timing Prediction of Successful Weaning from Mechanical Ventilation for Patients in Respiratory Care Centers
title Development of an Interactive AI System for the Optimal Timing Prediction of Successful Weaning from Mechanical Ventilation for Patients in Respiratory Care Centers
title_full Development of an Interactive AI System for the Optimal Timing Prediction of Successful Weaning from Mechanical Ventilation for Patients in Respiratory Care Centers
title_fullStr Development of an Interactive AI System for the Optimal Timing Prediction of Successful Weaning from Mechanical Ventilation for Patients in Respiratory Care Centers
title_full_unstemmed Development of an Interactive AI System for the Optimal Timing Prediction of Successful Weaning from Mechanical Ventilation for Patients in Respiratory Care Centers
title_short Development of an Interactive AI System for the Optimal Timing Prediction of Successful Weaning from Mechanical Ventilation for Patients in Respiratory Care Centers
title_sort development of an interactive ai system for the optimal timing prediction of successful weaning from mechanical ventilation for patients in respiratory care centers
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9030191/
https://www.ncbi.nlm.nih.gov/pubmed/35454023
http://dx.doi.org/10.3390/diagnostics12040975
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