ArticleTechnology and health care : official journal of the European Society for Engineering and Medicine2022
Boosting framework via clinical monitoring data to predict the depth of anesthesia.
Article in Technology and health care : official journal of the European Society for Engineering and Medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Anesthesia depth prediction from drug infusion history using hybrid AI.BMC medical informatics and decision making · 2025Article
- A machine learning screening model for identifying the risk of high-frequency hearing impairment in a general population.BMC public health · 2024Article
- The evolution of robotics: research and application progress of dental implant robotic systems.International journal of oral science · 2024Review
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Authors and funding
6 authors.
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Abstract
backgroundPrediction of the depth of anesthesia is a difficult job in the biomedical field.
objectiveThis study aimed to build a boosting-based prediction model to predict the depth of anesthesia based on four clinical monitoring data.
methodsBoosting 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.
resultsThe 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.
conclusionsThe 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.
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