Anomaly Detection and Correction in Dense Functional Data Within Electronic Medical Records.

In medical research, the accuracy of data from electronic medical records (EMRs) is critical, particularly when analyzing dense functional data, where anomalies can severely compromise research integrity. Anomalies in EMRs often arise from human errors in data measurement and entry, and increase in frequency with the volume of data. Despite the established methods in computer science, anomaly detection in medical applications remains underdeveloped.

A randomized controlled trial for symptoms of anxiety and depression: Effects of a 1-day acceptance and commitment training workshop.

Background: Cognitive-behavioral therapies often are recommended for anxiety disorders. However, treatment adherence and compliance are major barriers for these treatments, which are often delivered in 10 to 12 sessions over several months. This randomized controlled trial (trial registration NCT02915874 at www.

Prevalence of maternal obesity at delivery and association with maternal and neonatal outcomes.

Introduction: Maternal obesity has been linked to adverse outcomes for mothers and their offspring, including, but not limited to gestational hypertension (gHTN), gestational diabetes (GDM), pre-eclampsia, fetal macrosomia, and emergency cesarean section. Recent investigations have also shown that obesity, as defined by a body mass index (BMI) ≥ 30, especially severe obesity (BMI ≥ 40), is a risk factor for both hospitalization and death from COVID-19.

Objectives: The objective of this study is to determine the prevalence and association of maternal obesity at delivery with adverse antenatal, intrapartum, and neonatal outcomes in a cohort of consecutive delivering patients at a tertiary care center in Iowa from May to September 2020.