Literature DB >> 35931944

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

Hon Sen Tan1,2, Nan Liu3,4, Chin Wen Tan1,2, Alex Tiong Heng Sia1,2, Ban Leong Sng5,6.   

Abstract

Entities:  

Keywords:  artificial intelligence; neuraxial; obstetric; random forest; risk stratification

Mesh：

Year:  2022        PMID： 35931944     DOI： 10.1007/s12630-022-02294-1

Source DB:  PubMed          Journal:  Can J Anaesth        ISSN： 0832-610X            Impact factor:   6.713


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  5 in total

1.  Risk scoring for prediction of acute cardiac complications from imbalanced clinical data.

Authors:  Nan Liu; Zhi Xiong Koh; Eric Chern-Pin Chua; Licia Mei-Ling Tan; Zhiping Lin; Bilal Mirza; Marcus Eng Hock Ong
Journal:  IEEE J Biomed Health Inform       Date:  2014-11       Impact factor: 5.772

2.  Reducing breakthrough pain during labour epidural analgesia: an update.

Authors:  Hon Sen Tan; Ban Leong Sng; Alex Tiong Heng Sia
Journal:  Curr Opin Anaesthesiol       Date:  2019-06       Impact factor: 2.706

3.  AutoScore: A Machine Learning-Based Automatic Clinical Score Generator and Its Application to Mortality Prediction Using Electronic Health Records.

Authors:  Feng Xie; Bibhas Chakraborty; Marcus Eng Hock Ong; Benjamin Alan Goldstein; Nan Liu
Journal:  JMIR Med Inform       Date:  2020-10-21

4.  Prediction of breakthrough pain during labour neuraxial analgesia: comparison of machine learning and multivariable regression approaches.

Authors:  H S Tan; N Liu; R Sultana; N-L R Han; C W Tan; J Zhang; A T H Sia; B L Sng
Journal:  Int J Obstet Anesth       Date:  2020-08-25       Impact factor: 2.603

5.  Predicting disease risks from highly imbalanced data using random forest.

Authors:  Mohammed Khalilia; Sounak Chakraborty; Mihail Popescu
Journal:  BMC Med Inform Decis Mak       Date:  2011-07-29       Impact factor: 2.796
  5 in total

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