title: Prediction of uterine contractions using knowledge-assisted sequential pattern analysis.
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).
authors.affiliation: Department of Electrical and Computer Engineering, University of Miami, Coral Gables, FL 33124, USA.
authors.forename: Zifang
authors.initials: Z
authors.lastname: Huang
authors.forename: Mei-Ling
authors.initials: ML
authors.lastname: Shyu
authors.forename: James M
authors.initials: JM
authors.lastname: Tien
authors.forename: Michael M
authors.initials: MM
authors.lastname: Vigoda
authors.forename: David J
authors.initials: DJ
authors.lastname: Birnbach
mesh_terms: D016208:Databases, Factual; D005260:Female; D006801:Humans; D016018:Least-Squares Analysis; D008954:Models, Biological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D011247:Pregnancy; D060388:Support Vector Machine; D014590:Uterine Contraction / Q000502:physiology*