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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

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
Source :
Diagnostics, Vol 12, Iss 4, p 975 (2022)
Publication Year :
2022
Publisher :
MDPI AG, 2022.

Abstract

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.

Details

Language :
English
ISSN :
12040975 and 20754418
Volume :
12
Issue :
4
Database :
Directory of Open Access Journals
Journal :
Diagnostics
Publication Type :
Academic Journal
Accession number :
edsdoj.9b0f531a92364401840841f9ab06bcf4
Document Type :
article
Full Text :
https://doi.org/10.3390/diagnostics12040975