开发突破性疼痛风险评分：一种基于机器学习的可解释风险评分，用于预测分娩硬膜外镇痛时的突破性疼痛。 - Suppr

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超能文献

Developing the BreakThrough Pain Risk Score: an interpretable machine-learning-based risk score to predict breakthrough pain with labour epidural analgesia.

作者信息

Tan Hon Sen, Liu Nan, Tan Chin Wen, Sia Alex Tiong Heng, Sng Ban Leong

机构信息

Department of Women's Anaesthesia, KK Women's and Children's Hospital, Singapore, Singapore.

Anaesthesiology and Perioperative Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore, Singapore.

出版信息

Can J Anaesth. 2022 Oct;69(10):1315-1317. doi: 10.1007/s12630-022-02294-1. Epub 2022 Aug 5.

DOI:10.1007/s12630-022-02294-1

PMID:35931944

Abstract

摘要

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本文引用的文献

Int J Obstet Anesth. 2021 Feb;45:99-110. doi: 10.1016/j.ijoa.2020.08.010. Epub 2020 Aug 25.

AutoScore: A Machine Learning-Based Automatic Clinical Score Generator and Its Application to Mortality Prediction Using Electronic Health Records.自动评分：一种基于机器学习的自动临床评分生成器及其在使用电子健康记录进行死亡率预测中的应用。

JMIR Med Inform. 2020 Oct 21;8(10):e21798. doi: 10.2196/21798.

Reducing breakthrough pain during labour epidural analgesia: an update.减少分娩时硬膜外镇痛的突破性疼痛：更新。

Curr Opin Anaesthesiol. 2019 Jun;32(3):307-314. doi: 10.1097/ACO.0000000000000713.

Risk scoring for prediction of acute cardiac complications from imbalanced clinical data.基于不平衡临床数据预测急性心脏并发症的风险评分

IEEE J Biomed Health Inform. 2014 Nov;18(6):1894-902. doi: 10.1109/JBHI.2014.2303481.

Predicting disease risks from highly imbalanced data using random forest.基于随机森林算法从高度不平衡数据中预测疾病风险。

BMC Med Inform Decis Mak. 2011 Jul 29;11:51. doi: 10.1186/1472-6947-11-51.

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