DocumentCode
87998
Title
Prediction of Uterine Contractions Using Knowledge-Assisted Sequential Pattern Analysis
Author
Zifang Huang ; Mei-Ling Shyu ; Tien, James M. ; Vigoda, Michael M. ; Birnbach, David J.
Author_Institution
Dept. of Electr. & Comput. Eng., Univ. of Miami, Coral Gables, FL, USA
Volume
60
Issue
5
fYear
2013
fDate
May-13
Firstpage
1290
Lastpage
1297
Abstract
The usage of the systemic opioid remifentanil in relieving the labor pain has attracted much attention recently. An optimal dosing regimen for administration of remifentanil during labor relies on anticipating the timing of uterine contractions. These predictions should be made early enough to maximize analgesia efficacy during contractions and minimize the impact of the medication between contractions. We have designed a knowledge-assisted sequential pattern analysis framework to 1) predict the intrauterine pressure in real time; 2) anticipate the next contraction; and 3) develop a sequential association rule mining approach to identify the patterns of the contractions from historical patient tracings (HT).
Keywords
obstetrics; patient care; pattern recognition; support vector machines; analgesia efficacy; historical patient tracings; intrauterine pressure; knowledge assisted sequential pattern analysis; labor pain; medication; opioid remifentanil; optimal dosing regimen; uterine contraction; Association rules; Collaboration; Itemsets; Pain; Predictive models; Time series analysis; Training; Knowledge-based systems; pattern analysis; predictive models; support vector machine (SVM); uterine contraction; Databases, Factual; Female; Humans; Least-Squares Analysis; Models, Biological; Models, Statistical; Pattern Recognition, Automated; Pregnancy; Support Vector Machines; Uterine Contraction;
fLanguage
English
Journal_Title
Biomedical Engineering, IEEE Transactions on
Publisher
ieee
ISSN
0018-9294
Type
jour
DOI
10.1109/TBME.2012.2232666
Filename
6376142
Link To Document