Publications by authors named "P Warrick"
Article Synopsis
- * Researchers analyzed data from around 41,000 term births, comparing 374 cases of HIE, 3,056 with fetal acidosis, and 37,546 healthy infants, using a random forest classifier for prediction.
- * The system showed improved detection rates for HIE (61.8%) and fetal acidosis (48.3%) without increasing false positives in healthy infants, allowing for potential early clinical interventions.
This article describes the methods used to build a large-scale database of more than 250,000 electronic fetal monitoring (EFM) records linked to a comprehensive set of clinical information about the infant, the mother, the pregnancy, labor, and outcome. The database can be used to investigate how birth outcome is related to clinical and EFM features. The main steps involved in building the database were: (1) Acquiring the raw EFM recording and clinical records for each birth.
View Article and Find Full Text PDFBackground: The diagnosis of failure to progress, the most common indication for intrapartum cesarean delivery, is based on the assessment of cervical dilation and station over time. Labor curves serve as references for expected changes in dilation and fetal descent. The labor curves of Friedman, Zhang et al, and others are based on time alone and derived from mothers with spontaneous labor onset.
View Article and Find Full Text PDFData-Driven Insights into Labor Progression with Gaussian Processes.
Bioengineering (Basel)
January 2024
Article Synopsis
- Clinicians often perform pelvic exams to monitor labor progress, but existing guidelines don't effectively reduce cesarean-section rates.
- A new Gaussian process model for predicting cervical dilation and fetal station shows improved accuracy over previous statistical methods.
- This model provides confidence estimates for its predictions and is more effective when predicting both dilation and station together, rather than using separate models.
Article Synopsis
- This study focuses on improving the detection of fetuses at risk for fetal acidosis or hypoxic-ischemic encephalopathy (HIE) during labor by analyzing fetal heart rate (FHR) and uterine pressure (UP) signals.
- A random forest classifier was developed to give intervention recommendations based on feature data from FHR and UP collected in 20-minute intervals, showing a significant increase in identifying at-risk babies well before delivery.
- The system identified more cases of HIE and acidosis, suggesting early intervention opportunities that could lower HIE rates, despite a slight rise in cesarean section rates among healthy births.