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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Main Authors: Kuang-Ming Liao, Shian-Chin Ko, Chung-Feng Liu, Kuo-Chen Cheng, Chin-Ming Chen, Mei-I Sung, Shu-Chen Hsing, Chia-Jung Chen
Format: Article
Language:English
Published: MDPI AG 2022-04-01
Series:Diagnostics
Subjects:
Online Access:https://www.mdpi.com/2075-4418/12/4/975
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author Kuang-Ming Liao
Shian-Chin Ko
Chung-Feng Liu
Kuo-Chen Cheng
Chin-Ming Chen
Mei-I Sung
Shu-Chen Hsing
Chia-Jung Chen
author_facet Kuang-Ming Liao
Shian-Chin Ko
Chung-Feng Liu
Kuo-Chen Cheng
Chin-Ming Chen
Mei-I Sung
Shu-Chen Hsing
Chia-Jung Chen
author_sort Kuang-Ming Liao
collection DOAJ
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 doaj.art-9b0f531a92364401840841f9ab06bcf42023-12-01T01:34:51ZengMDPI AGDiagnostics2075-44182022-04-0112497510.3390/diagnostics12040975Development of an Interactive AI System for the Optimal Timing Prediction of Successful Weaning from Mechanical Ventilation for Patients in Respiratory Care CentersKuang-Ming Liao0Shian-Chin Ko1Chung-Feng Liu2Kuo-Chen Cheng3Chin-Ming Chen4Mei-I Sung5Shu-Chen Hsing6Chia-Jung Chen7Department of Pulmonary Medicine, Chi Mei Medical Center, Chiali, Tainan 72263, TaiwanDepartment of Respiratory Therapy, Chi Mei Medical Center, Tainan 710402, TaiwanDepartment of Medical Research, Chi Mei Medical Center, Tainan 710402, TaiwanDepartment of Internal Medicine, Chi Mei Medical Center, Tainan 710402, TaiwanDepartment of Intensive Care Medicine, Chi Mei Medical Center, Tainan 710402, TaiwanDepartment of Respiratory Therapy, Chi Mei Medical Center, Tainan 710402, TaiwanDepartment of Respiratory Therapy, Chi Mei Medical Center, Tainan 710402, TaiwanDepartment of Information Systems, Chi Mei Medical Center, Tainan 710402, TaiwanSuccessful 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.https://www.mdpi.com/2075-4418/12/4/975artificial intelligencemachine learningweaning timingsuccessful weaningpredictionmechanical ventilation
spellingShingle Kuang-Ming Liao
Shian-Chin Ko
Chung-Feng Liu
Kuo-Chen Cheng
Chin-Ming Chen
Mei-I Sung
Shu-Chen Hsing
Chia-Jung Chen
Development of an Interactive AI System for the Optimal Timing Prediction of Successful Weaning from Mechanical Ventilation for Patients in Respiratory Care Centers
Diagnostics
artificial intelligence
machine learning
weaning timing
successful weaning
prediction
mechanical ventilation
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 artificial intelligence
machine learning
weaning timing
successful weaning
prediction
mechanical ventilation
url https://www.mdpi.com/2075-4418/12/4/975
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