Open AccessBoosting framework via clinical monitoring data to predict the depth of anesthesia
Author(s) -
Yanfei Liu,
Pengcheng Lei,
Yu Wang,
Jinghai Zhou,
Jie Zhang,
Hui Cao
Publication year - 2022
Publication title -
technology and health care
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.281
H-Index - 44
eISSN - 1878-7401
pISSN - 0928-7329
DOI - 10.3233/thc-thc228045
Subject(s) - boosting (machine learning) , computer science , remifentanil , support vector machine , gradient boosting , artificial intelligence , anesthesia , machine learning , medicine , random forest , propofol
BACKGROUND: Prediction of the depth of anesthesia is a difficult job in the biomedical field. OBJECTIVE: This study aimed to build a boosting-based prediction model to predict the depth of anesthesia based on four clinical monitoring data. METHODS: Boosting is a framework algorithm that is used to train a series of weak learners into strong learners by assigning different weights according to their classification accuracy. The input of the boosting-based prediction model included four types of clinical monitoring data: electromyography, end-tidal carbon dioxide partial pressure, remifentanil dosage, and flow rate. The output was the depth of anesthesia. RESULTS: The boosting framework model built in this study achieved higher prediction accuracy and a lower discrete degree in predicting the depth of anesthesia compared with the DT-, KNN-, and SVM-based models. CONCLUSIONS: The boosting framework was used to set up a prediction model to predict the depth of anesthesia based on four clinical monitoring data. In the experiments, the boosting framework model of this study achieved higher prediction accuracy and a lower discrete degree. This model will be useful in predicting the depth of anesthesia.