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Knowledge-Assisted Sequential Pattern Analysis With Heuristic Parameter Tuning for Labor Contraction Prediction
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DOI：10.1109/JBHI.2013.2281974.png)
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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.
Keyword:
Association rulemining
labor contraction prediction
least-squares support vector machine (LS-SVM)
parameter tuning
time series prediction
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