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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MDPI AG
2022-04-01
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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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issn | 2075-4418 |
language | English |
last_indexed | 2024-03-09T10:56:28Z |
publishDate | 2022-04-01 |
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series | Diagnostics |
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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