WANG Xiaomin 1,2 , CHEN Zhu 3 , ZHANG Heng 4 , JIANG Xin 2 , LIU Qilin 2 , HUANG Jin 2
  • 1. Department of Respiratory Care, West China Hospital, Sichuan University, Chengdu 610041, P.R. China;
  • 2. Innovation Institute for Integration of Medicine and Engineering, Med-X Center for Manufacturing, West China Hospital, Sichuan University, Chengdu 610041, P.R. China;
  • 3. Department of Medical Engineering Division, West China Hospital, Sichuan University, Chengdu 610041, P.R. China;
  • 4. College of Electrical Engineering, Sichuan University, Chengdu 610065, P.R. China;
HUANG Jin, Email: michael_huangjin@163.com
Mechanical ventilation; Alarm management; Machine learning; Internet of medical things; Intensive care unit
10.7507/1672-2531.202411199
Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables References Cited by

Objective To explore the development and application of a novel ventilator alarm management model in critically ill patients receiving invasive mechanical ventilation (MV) in the intensive care unit (ICU) using machine learning (ML) and the internet of medical things (IoMT). The study aims to identify alarms’ intervention requirements. Methods A retrospective cohort study and ML analysis were conducted, including adult patients receiving invasive MV in the ICU at West China Hospital from February10, 2024, to July 22, 2024. A total of 76 ventilator alarm-related parameters were collected through the IoMT system. Feature selection was performed using a stratified approach, and six ML algorithms were applied: Gaussian Naive Bayes, K-Nearest Neighbors, Linear Discriminant Analysis, Support Vector Machine, Categorical Boosting (CatBoost), and Logistic Regression. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC-ROC). Results A total of 107 patients and their associated ventilator alarm records were included. Thirteen highly relevant features were selected from the 76 parameters for model training through stratified feature selection. The CatBoost model demonstrated the best predictive performance, with an AUC-ROC of 0.984 7 and an accuracy of 0.912 3 in the training set. External validation of the CatBoost model yielded an AUC-ROC of 0.805 4. Conclusion The CatBoost-based ML model successfully constructed in this study has high accuracy and reliability in predicting the ventilator alarms in ICU patients, providing an effective tool for ventilator alarm management. The CatBoost-based ML method exhibited remarkable efficacy in predicting the necessity of ventilator intervention in critically ill ICU patients. Further large-scale multicenter studies are recommended to validate its clinical application value and promote model optimization and implementation.

Copyright © the editorial department of Chinese Journal of Evidence-Based Medicine of West China Medical Publisher. All rights reserved