Machine Learning in Electronic Health Records: Identifying High-Risk Obstetric Patients Pre and During Labor.

Our goal is to apply artificial intelligence (AI) and statistical analysis to understand the relationship between various factors and outcomes during pregnancy and labor and delivery, in order to personalize birth management and reduce complications for both mothers and newborns. We use a structured electronic health records database with data from approximately 130,000 births to train, test and validate our models. We apply machine learning (ML) methods to predict various obstetrical outcomes before and during labor, with the aim of improving patient care management in the delivery ward.

Early developmental milestone clusters of autistic children based on electronic health records.

Autistic children vary in symptoms, co-morbidities, and response to interventions. This study aimed to identify clusters of autistic children with a distinct pattern of attaining early developmental milestones (EDMs). The clustering of 5836 autistic children was based on the attainment of 43 gross motor, fine motor, language, and social developmental milestones during the first 3 years of life as recorded in baby wellness visits.

Predicting autism traits from baby wellness records: A machine learning approach.

Timely identification of autism spectrum conditions is a necessity to enable children to receive the most benefit from early interventions. Emerging technological advancements provide avenues for detecting subtle, early indicators of autism from routinely collected health information. This study tested a model that provides a likelihood score for autism diagnosis from baby wellness visit records collected during the first 2 years of life.

A Prediction Model of Autism Spectrum Diagnosis from Well-Baby Electronic Data Using Machine Learning.

Early detection of autism spectrum disorder (ASD) is crucial for timely intervention, yet diagnosis typically occurs after age three. This study aimed to develop a machine learning model to predict ASD diagnosis using infants' electronic health records obtained through a national screening program and evaluate its accuracy. A retrospective cohort study analyzed health records of 780,610 children, including 1163 with ASD diagnoses.

Maternal hybrid immunity and risk of infant COVID-19 hospitalizations: national case-control study in Israel.

Hybrid immunity, acquired through vaccination followed or preceded by a COVID-19 infection, elicits robust antibody augmentation. We hypothesize that maternal hybrid immunity will provide greater infant protection than other forms of COVID-19 immunity in the first 6 months of life. We conducted a case-control study in Israel, enrolling 661 infants up to 6 months of age, hospitalized with COVID-19 (cases) and 59,460 age-matched non-hospitalized infants (controls) between August 24, 2021, and March 15, 2022.

Maternal third dose of BNT162b2 mRNA vaccine and risk of infant COVID-19 hospitalization.

Infants are at a higher risk of Coronavirus Disease 2019 (COVID-19)-related hospitalizations compared to older children. In this study, we investigated the effect of the recommended third maternal dose of BNT162b2 COVID-19 vaccine during pregnancy on rates of infant COVID-19-related hospitalizations. We conducted a nationwide cohort study of all live-born infants delivered in Israel between 24 August 2021 and 15 March 2022 to estimate the effectiveness of the third booster dose versus the second dose against infant COVID-19-related hospitalizations.

Neonatal intensive care admission for term neonates and subsequent childhood mortality: a retrospective linkage study.

Background: Neonatal intensive care unit (NICU) admission among term neonates is a rare event. The aim of this study was to study the association of the NICU admission of term neonates on the risk of long-term childhood mortality.

Methods: A single-center case-control retrospective study between 2005 and 2019, including all in-hospital ≥ 37 weeks' gestation singleton live-born neonates.

Partogram of Grandmultiparous Parturients: A Multicenter Cohort Study.

Sparse and conflicting data exist regarding the normal partogram of grand-multiparous (GMP, defined as parity of 6+) parturients. Customized partograms may potentially lower cesarean delivery rates for protraction disorders in this population. In this study, we aim to construct a normal labor curve of GMP women and compare it to the multiparous (MP, defined as parity of 2-5) partogram.

Effectiveness of a third BNT162b2 mRNA COVID-19 vaccination during pregnancy: a national observational study in Israel.

The Centers for Disease Control (CDC) recommend a third dose of COVID-19 vaccine for pregnant women, although data regarding effectiveness during pregnancy are lacking. This national, population-based, historical cohort study of pregnant women in Israel, delivering between August 1, 2021 and March 22, 2022, aims to analyze and compare the third and second doses' vaccine effectiveness in preventing COVID-19-related hospitalizations during pregnancy during two COVID-19 waves (Delta variant in the summer of 2021 and Omicron, BA.1, variant in the winter of 2022).

NICU Admission for Term Neonates in a Large Single-Center Population: A Comprehensive Assessment of Risk Factors Using a Tandem Analysis Approach.

Objective: Neonatal intensive care unit (NICU) admission among term neonates is associated with significant morbidity and mortality, as well as high healthcare costs. A comprehensive NICU admission risk assessment using an integrated statistical approach for this rare admission event may be used to build a risk calculation algorithm for this group of neonates prior to delivery. Methods: A single-center case−control retrospective study was conducted between August 2005 and December 2019, including in-hospital singleton live born neonates, born at ≥37 weeks’ gestation.

Chorioamnionitis and its association with neonatal and maternal adverse outcomes in women with and without epidural analgesia administration.

Objective: To evaluate neonatal fever and adverse maternal and neonatal outcomes in febrile laboring women and assess whether the time interval between epidural analgesia (EA) administration and chorioamnionitis is associated with these complications.

Methods: A retrospective cohort study at a university affiliated medical center between 2003 and 2015. Included were women who underwent term vaginal delivery attempt and diagnosed with chorioamnionitis.

Prediction of neonatal subgaleal hemorrhage using first stage of labor data: A machine-learning based model.

Background: Subgaleal hemorrhage (SGH) is a rare neonatal condition, mainly associated with instrumental delivery, mainly vacuum extractor (VE). The aim of this study was to develop a machine learning model that would allow a personalized prediction algorithm for Subgaleal hemorrhage (SGH) following vacuum extraction (VE), based on maternal and fetal variables collected during the first stage of labor.

Materials And Methods: A retrospective cohort study on data from a university affiliated hospital, recorded between January 2013 and February 2017.

Primary risk stratification for neonatal jaundice among term neonates using machine learning algorithm.

Background: Neonatal jaundice occurs in approximately 60% of term newborns. Although risk factors for neonatal jaundice have been studied, all the suggested strategies are based on various newborn tests for bilirubin levels. We aim to stratify neonates into risk groups for clinically significant neonatal jaundice using a combined data analysis approach, without serum bilirubin evaluation.

Transporting an Artificial Intelligence Model to Predict Emergency Cesarean Delivery: Overcoming Challenges Posed by Interfacility Variation.

Research using artificial intelligence (AI) in medicine is expected to significantly influence the practice of medicine and the delivery of health care in the near future. However, for successful deployment, the results must be transported across health care facilities. We present a cross-facilities application of an AI model that predicts the need for an emergency caesarean during birth.

Normal labor curve in twin gestation.

Background: Failure to progress is one of the leading indications for cesarean delivery in trials of labor in twin gestations. However, assessment of labor progression in twin labors is managed according to singleton labor curves.

Objective: This study aimed to establish a partogram for twin deliveries that reflects normal and abnormal labor progression and customized labor curves for different subgroups of twin labors.

The impact of peak and duration of maternal intrapartum fever on perinatal outcomes.

Background: Intrapartum fever is a common perinatal complication, associated with short- and long-term adverse outcomes. Limited data exist regarding the association between the peak and duration of maternal fever and the extent and severity of maternal and neonatal complications.

Objective: This study aimed to examine the association between maternal intrapartum fever peak and duration and perinatal outcomes.

Prediction of severe adverse neonatal outcomes at the second stage of labour using machine learning: a retrospective cohort study.

Objective: To create a personalised machine learning model for prediction of severe adverse neonatal outcomes (SANO) during the second stage of labour.

Design: Retrospective Electronic-Medical-Record (EMR) -based study.

Population: A cohort of 73 868 singleton, term deliveries that reached the second stage of labour, including 1346 (1.

Prediction model for obstetric anal sphincter injury using machine learning.

Introduction And Hypothesis: Obstetric anal sphincter injury (OASI) is a complication with substantial maternal morbidity. The aim of this study was to develop a machine learning model that would allow a personalized prediction algorithm for OASI, based on maternal and fetal variables collected at admission to labor.

Materials And Methods: We performed a retrospective cohort study at a tertiary university hospital.

Successful vaginal birth after cesarean in the second delivery is not associated with the stage of labor of the primary unplanned cesarean delivery.

Background: Candidates for trial of labor after cesarean must be carefully screened to maximize success and minimize morbidity. Demographic and obstetric characteristics affecting success rates must be delineated.

Objective: We examined whether the labor stage of the primary delivery in which a woman underwent an unplanned cesarean delivery would affect the likelihood that she could achieve a subsequent vaginal birth.

Risk factors for uterine incision extension during cesarean delivery.

Background: Unintentional extension of uterine incision is a known complication during cesarean delivery estimated to occur in 4-8% of cesarean deliveries. The aim of this study was to examine risk factors associated with unintentional uterine incision extension and to assess which of them are independent risk factors for this condition.

Study Design: We conducted a retrospective cohort study at a large public university tertiary referral center between 2003 and 2017.

Real-time data analysis using a machine learning model significantly improves prediction of successful vaginal deliveries.

Background: The process of childbirth is one of the most crucial events in the future health and development of the offspring. The vulnerability of parturients and fetuses during the delivery process led to the development of intrapartum monitoring methods and to the emergence of alternative methods of delivery. However, current monitoring methods fail to accurately discriminate between cases in which intervention is unnecessary, partly contributing to the high rates of cesarean deliveries worldwide.

Cervical dilation at the time of epidural catheter insertion is not associated with the degree of prolongation of the first or second stages of labor, or the rate of instrumental vaginal delivery.

Introduction: Epidural analgesia (EA) is an established option for efficient intrapartum analgesia. Meta-analyses have shown that EA differentially affects the first stage of labor but prolongs the second. The question of EA timing remains open.

Prediction of vaginal birth after cesarean deliveries using machine learning.

Background: Efforts to reduce cesarean delivery rates to 12-15% have been undertaken worldwide. Special focus has been directed towards parturients who undergo a trial of labor after cesarean delivery to reduce the burden of repeated cesarean deliveries. Complication rates are lowest when a vaginal birth is achieved and highest when an unplanned cesarean delivery is performed, which emphasizes the need to assess, in advance, the likelihood of a successful vaginal birth after cesarean delivery.

Unplanned Cesarean Delivery in the Second Stage of Labor Holds Higher Odds of Complications than in the First Stage, while Similar in Primiparas and Multiparas.

Background: While endeavors to reduce cesarean delivery (CD) rates are given priority worldwide, it is important to evaluate if these efforts place parturients and neonates at risk. CD performed in the second stage of labor carries higher risks of maternal and fetal complications and is a more challenging surgical procedure than that performed in the first stage or before labor. In a population with a low CD rate, we sought to evaluate the rate of maternal and fetal complications associated with unplanned CD (UCD) performed in the second vs.

Apparent Diffusion Coefficient Value Changes and Clinical Correlation in 90 Cases of Cytomegalovirus-Infected Fetuses with Unremarkable Fetal MRI Results.

Background And Purpose: Cytomegalovirus is the leading intrauterine infection. Fetal MR imaging is an accepted tool for fetal brain evaluation, yet it still lacks the ability to accurately predict the extent of the neurodevelopmental impairment, especially in fetal MR imaging scans with unremarkable findings. Our hypothesis was that intrauterine cytomegalovirus infection causes diffusional changes in fetal brains and that those changes may correlate with the severity of neurodevelopmental deficiencies.