التفاصيل البيبلوغرافية
| العنوان: |
Knowledge-assisted sequential pattern analysis with heuristic parameter tuning for labor contraction prediction. |
| المؤلفون: |
Huang Z, Shyu ML, Tien JM, Vigoda MM, Birnbach DJ |
| المصدر: |
IEEE journal of biomedical and health informatics [IEEE J Biomed Health Inform] 2014 Mar; Vol. 18 (2), pp. 492-9. |
| نوع المنشور: |
Journal Article |
| اللغة: |
English |
| بيانات الدورية: |
Publisher: Institute of Electrical and Electronics Engineers Country of Publication: United States NLM ID: 101604520 Publication Model: Print Cited Medium: Internet ISSN: 2168-2208 (Electronic) Linking ISSN: 21682194 NLM ISO Abbreviation: IEEE J Biomed Health Inform Subsets: MEDLINE |
| أسماء مطبوعة: |
Original Publication: New York, NY : Institute of Electrical and Electronics Engineers, 2013- |
| مواضيع طبية MeSH: |
Models, Statistical*, Pattern Recognition, Automated/*methods , Uterine Contraction/*physiology , Uterine Monitoring/*methods, Adult ; Female ; Humans ; Labor, Induced ; Pregnancy ; Support Vector Machine |
| مستخلص: |
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. Asequential 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 leastsquares 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. |
| تواريخ الأحداث: |
Date Created: 20130924 Date Completed: 20140902 Latest Revision: 20151119 |
| رمز التحديث: |
20221213 |
| DOI: |
10.1109/JBHI.2013.2281974 |
| PMID: |
24058036 |
| قاعدة البيانات: |
MEDLINE |
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