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Apgar Score

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Application of machine learning in identifying risk factors for low APGAR scores.

BMC pregnancy and childbirth 40340577
BACKGROUND: Identifying the risk factors for low APGAR scores at birth is critical for improving neonatal outcomes and guiding clinical interventions.
Adult Apgar Score Female Gestational Age Humans Infant, Newborn Machine Learning Pregnancy Risk Factors
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Prediction of vaginal birth after cesarean deliveries using machine learning.

American journal of obstetrics and gynecology 32007491
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 deliver...
Adult Apgar Score Area Under Curve Cesarean Section Delivery, Obstetric Extraction, Obstetrical Feasibility Studies Female Fetal Weight Gestational Age Head Humans Infant, Newborn Machine Learning Organ Size Parity Pregnancy Retrospective Studies Risk Assessment Risk Factors ROC Curve Tertiary Care Centers Trial of Labor Uterine Rupture Vaginal Birth after Cesarean
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Quantifying the Impacts of Pre- and Post-Conception TSH Levels on Birth Outcomes: An Examination of Different Machine Learning Models.

Frontiers in endocrinology 34777251
BACKGROUND: While previous studies identified risk factors for diverse pregnancy outcomes, traditional statistical methods had limited ability to quantify their impacts on birth outcomes precisely. We aimed to use a novel approach that applied differ...
Adult Apgar Score Birth Weight China Female Humans Labor, Induced Machine Learning Pregnancy Premature Birth Thyrotropin
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Prediction of low Apgar score at five minutes following labor induction intervention in vaginal deliveries: machine learning approach for imbalanced data at a tertiary hospital in North Tanzania.

BMC pregnancy and childbirth 35365129
BACKGROUND: Prediction of low Apgar score for vaginal deliveries following labor induction intervention is critical for improving neonatal health outcomes. We set out to investigate important attributes and train popular machine learning (ML) algorit...
Apgar Score Delivery, Obstetric Female Humans Infant, Newborn Labor, Induced Machine Learning Pregnancy Tanzania Tertiary Care Centers
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Validation of a machine learning algorithm for identifying infants at risk of hypoxic ischaemic encephalopathy in a large unseen data set.

Archives of disease in childhood. Fetal and neonatal edition 39251344
OBJECTIVE: To validate a hypoxic ischaemic encephalopathy (HIE) prediction algorithm to identify infants at risk of HIE immediately after birth using readily available clinical data.
Algorithms Apgar Score Female Humans Hypoxia-Ischemia, Brain Infant, Newborn Machine Learning Male Risk Assessment
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