A prediction model using machine-learning algorithm for assessing intrathecal hyperbaric bupivacaine dose during cesarean section.

Journal: BMC anesthesiology

PMID: 33853548

Abstract

BACKGROUND: The intrathecal hyperbaric bupivacaine dosage for cesarean section is difficult to predetermine. This study aimed to develop a decision-support model using a machine-learning algorithm for assessing intrathecal hyperbaric bupivacaine dose based on physical variables during cesarean section.

Authors

Chang-Na Wei

Department of Anesthesia, Jiaxing University Affiliated Women and Children Hospital, Jiaxing, Zhejiang Province, China.
Li-Ying Wang

Natural Medicine Research Center, College of Veterinary Medicine, Sichuan Agricultural University Chengdu 611130, China.
Xiang-Yang Chang

Department of Anesthesia, Jiaxing University Affiliated Women and Children Hospital, Jiaxing, Zhejiang Province, China.
Qing-He Zhou

Department of Anesthesia, Affiliated Hospital of Jiaxing University, Jiaxing, Zhejiang Province, China. jxxmxy@163.com.

Keywords

Adult Algorithms Anesthesia, Obstetrical Anesthesia, Spinal Anesthetics, Local Bupivacaine Cesarean Section Decision Support Techniques Female Humans Machine Learning Pregnancy Prospective Studies

External Resources

View on PubMed Access via DOI PubMed (33853548)

A prediction model using machine-learning algorithm for assessing intrathecal hyperbaric bupivacaine dose during cesarean section.

Abstract

Authors

Keywords

External Resources

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