Quantitative Analysis of Anesthesia Recovery Time by Machine Learning Prediction Models

Shumin Yang, Huaying Li, Zhizhe Lin, Youyi Song, Cheng Lin and Teng Zhou
Additional contact information
Shumin Yang: Department of Computer Science, Shantou University, Shantou 515041, China
Huaying Li: Department of Computer Science, Shantou University, Shantou 515041, China
Zhizhe Lin: Office of Emergency Management, Shantou Central Hospital, Shantou 515041, China
Youyi Song: Center for Smart Health, School of Nursing, The Hong Kong Polytechnic University, Hong Kong, China
Cheng Lin: Joint Shantou International Eye Center, Shantou University and the Chinese University of Hong Kong, Shantou 515041, China
Teng Zhou: Department of Computer Science, Shantou University, Shantou 515041, China

Mathematics, 2022, vol. 10, issue 15, 1-14

Abstract: It is significant for anesthesiologists to have a precise grasp of the recovery time of the patient after anesthesia. Accurate prediction of anesthesia recovery time can support anesthesiologist decision-making during surgery to help reduce the risk of surgery in patients. However, effective models are not proposed to solve this problem for anesthesiologists. In this paper, we seek to find effective forecasting methods. First, we collect 1824 patient anesthesia data from the eye center and then performed data preprocessing. We extracted 85 variables to predict recovery time from anesthesia. Second, we extract anesthesia information between variables for prediction using machine learning methods, including Bayesian ridge, lightGBM, random forest, support vector regression, and extreme gradient boosting. We also design simple deep learning models as prediction models, including linear residual neural networks and jumping knowledge linear neural networks. Lastly, we perform a comparative experiment of the above methods on the dataset. The experiment demonstrates that the machine learning method performs better than the deep learning model mentioned above on a small number of samples. We find random forest and XGBoost are more efficient than other methods to extract information between variables on postoperative anesthesia recovery time.

Keywords: anesthesia technology; anesthesia recovery modeling; machine learning; deep learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/10/15/2772/pdf (application/pdf)
https://www.mdpi.com/2227-7390/10/15/2772/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:10:y:2022:i:15:p:2772-:d:880567

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().