Knowledge-assisted sequential pattern analysis with heuristic parameter tuning for labor contraction prediction

Zifang Huang, Mei-Ling Shyu, James M. Tien, Michael M. Vigoda, David Birnbach

Research output: Contribution to journalArticle

5 Scopus citations

Abstract

The optimal dosing regimen of remifentanil for relieving labor pain should achieve maximal efficacy during contractions and little effect between contractions. Toward such a need, we propose a knowledge-assisted sequential pattern analysis with heuristic parameter tuning to predict the changes in intrauterine pressure, which indicates the occurrence of labor contractions. This enables giving the drug shortly before each contraction starts. A sequential association rule mining based patient selection strategy is designed to dynamically select data for training regression models. A novel heuristic parameter tuning method is proposed to decide the appropriate value ranges and searching strategies for both the regularization factor and the Gaussian kernel parameter of least-squares support vector machine with radial basis function (RBF) kernel, which is used as the regression model for time series prediction. The parameter tuning method utilizes information extracted from the training dataset, and it is adaptive to the characteristics of time series. The promising experimental results show that the proposed framework is able to achieve the lowest prediction errors as compared to some existing methods.

Original languageEnglish
Article number6600763
Pages (from-to)492-499
Number of pages8
JournalIEEE Journal of Biomedical and Health Informatics
Volume18
Issue number2
DOIs
StatePublished - Jan 1 2014

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Keywords

  • Association rule mining
  • labor contraction prediction
  • least-squares support vector machine (LS-SVM)
  • parameter tuning
  • time series prediction

ASJC Scopus subject areas

  • Biotechnology
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Health Information Management

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