@article{zhoroev_hamilton_warrick_2024, title={Data-Driven Insights into Labor Progression with Gaussian Processes}, volume={11}, ISSN={["2306-5354"]}, DOI={10.3390/bioengineering11010073}, abstractNote={Clinicians routinely perform pelvic examinations to assess the progress of labor. Clinical guidelines to interpret these examinations, using time-based models of cervical dilation, are not always followed and have not contributed to reducing cesarean-section rates. We present a novel Gaussian process model of labor progress, suitable for real-time use, that predicts cervical dilation and fetal station based on clinically relevant predictors available from the pelvic exam and cardiotocography. We show that the model is more accurate than a statistical approach using a mixed-effects model. In addition, it provides confidence estimates on the prediction, calibrated to the specific delivery. Finally, we show that predicting both dilation and station with a single Gaussian process model is more accurate than two separate models with single predictions.}, number={1}, journal={BIOENGINEERING-BASEL}, author={Zhoroev, Tilekbek and Hamilton, Emily F. and Warrick, Philip A.}, year={2024}, month={Jan} }