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

Kuang-Ming Liao; Shian-Chin Ko; Chung-Feng Liu; Kuo-Chen Cheng; Chin-Ming Chen; Mei-I Sung; Shu-Chen Hsing; Chia-Jung Chen

doi:10.3390/diagnostics12040975

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

Diagnostics (Basel). 2022 Apr 13;12(4):975. doi: 10.3390/diagnostics12040975.

Authors

Kuang-Ming Liao¹, Shian-Chin Ko², Chung-Feng Liu³, Kuo-Chen Cheng⁴, Chin-Ming Chen⁵, Mei-I Sung², Shu-Chen Hsing², Chia-Jung Chen⁶

Affiliations

¹ Department of Pulmonary Medicine, Chi Mei Medical Center, Chiali, Tainan 72263, Taiwan.
² Department of Respiratory Therapy, Chi Mei Medical Center, Tainan 710402, Taiwan.
³ Department of Medical Research, Chi Mei Medical Center, Tainan 710402, Taiwan.
⁴ Department of Internal Medicine, Chi Mei Medical Center, Tainan 710402, Taiwan.
⁵ Department of Intensive Care Medicine, Chi Mei Medical Center, Tainan 710402, Taiwan.
⁶ Department of Information Systems, Chi Mei Medical Center, Tainan 710402, Taiwan.

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.

Keywords: artificial intelligence; dashboard; impact analysis; machine learning; mechanical ventilation; prediction; respiratory care center; successful weaning; weaning timing.