Labor contraction prediction via demographic and obstetrical information analysis

Designing an optimal dosing regimen for the systemic opioid remifentanil during labor necessitates the prediction of the pace of contractions, so that the drug can be given shortly before the pain of the contraction begins. The prediction and drug administration should be made early enough to allow for the administration of intravenous analgesia that will have maximal efficacy during contractions and little effect between contractions. Towards such a need, we propose a knowledge-assisted sequential pattern analysis framework to predict the changes in intrauterine pressure, which indicate the occurrence of labor contractions. In particular, a patient selection strategy is proposed to select a group of patients, from the stored record, who share similar demographic and obstetrical information with the current patient of interest. A sequential association rule mining approach is designed to learn the patterns of the contractions from the historical patient tracings, and to determine which demographic and obstetrical features have an impact on the contraction patterns. The promising experimental results show that the proposed framework is effective, robust, and efficient in predicting the labor contraction patterns