AIMC Topic: Pregnancy in Obesity

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Prediction of obstetrical and fetal complications using automated electronic health record data.

American journal of obstetrics and gynecology Oct 22, 2020
An increasing number of delivering women experience major morbidity and mortality. Limited work has been done on automated predictive models that could be used for prevention. Using only routinely collected obstetrical data, this study aimed to devel...
Humans Machine Learning Retrospective Studies Electronic Health Records Pregnancy Female Premature Birth Parity Reproducibility of Results Adult Time Factors Blood Pressure Pre-Eclampsia Maternal Age Postpartum Hemorrhage Hypoxia-Ischemia, Brain Stillbirth Clinical Decision Rules Pregnancy in Obesity Routinely Collected Health Data Uterine Rupture Obstetric Labor Complications
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Comparison of Machine Learning Methods and Conventional Logistic Regressions for Predicting Gestational Diabetes Using Routine Clinical Data: A Retrospective Cohort Study.

Journal of diabetes research Jun 12, 2020
BACKGROUND: Gestational diabetes mellitus (GDM) contributes to adverse pregnancy and birth outcomes. In recent decades, extensive research has been devoted to the early prediction of GDM by various methods. Machine learning methods are flexible predi...
Humans Machine Learning Retrospective Studies Pregnancy Female Reproducibility of Results Young Adult Adult Blood Glucose Cohort Studies Logistic Models Decision Trees Glycated Hemoglobin Cholesterol Uric Acid Diabetes, Gestational Lipoproteins, HDL Maternal Age Triglycerides Clinical Decision Rules Pregnancy in Obesity Routinely Collected Health Data
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