AIMC Topic: Pregnancy Outcome

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Unveiling social determinants of health impact on adverse pregnancy outcomes through natural language processing.

Scientific reports
Understanding the role of Social Determinants of Health (SDoH) in pregnancy outcomes is critical for improving maternal and infant health yet extracting SDoH from unstructured electronic health records remains challenging. We trained and evaluated na...

Integrating AI predictive analytics with naturopathic and yoga-based interventions in a data-driven preventive model to improve maternal mental health and pregnancy outcomes.

Scientific reports
Maternal mental health during pregnancy is a crucial area of research due to its profound impact on both maternal and child well-being. This paper proposes a comprehensive approach to predicting and monitoring psychological health risks in pregnant w...

Analysis of maternal fetal outcomes and complete blood count parameters according to the stages of placental abruption: a retrospective study.

European journal of medical research
BACKGROUND: To compare the demographic characteristics, maternal and perinatal outcomes and hemoglobin parameters according to stages diagnosed with placental abruption.

Harnessing vaginal inflammation and microbiome: a machine learning model for predicting IVF success.

NPJ biofilms and microbiomes
Humans are the only species with a commensal Lactobacillus-dominant vaginal microbiota. Reproductive tract microbes have been linked to fertility outcomes, as has intrauterine inflammation, suggesting immune response may mediate adverse outcomes. In ...

Dose-response association between OGTT and adverse perinatal outcomes after IVF treatment: A cohort study based on a twin population.

Journal of endocrinological investigation
BACKGROUND: Investigate the association between Oral Glucose Tolerance Test (OGTT) after in vitro fertilization (IVF) treatment and adverse maternal and neonatal outcomes in twin pregnancies.

Predictive modeling of pregnancy outcomes utilizing multiple machine learning techniques for in vitro fertilization-embryo transfer.

BMC pregnancy and childbirth
OBJECTIVE: This study aims to investigate the influencing factors of pregnancy outcomes during in vitro fertilization and embryo transfer (IVF-ET) procedures in clinical practice. Several prediction models were constructed to predict pregnancy outcom...

Prediction of adverse pregnancy outcomes using machine learning techniques: evidence from analysis of electronic medical records data in Rwanda.

BMC medical informatics and decision making
BACKGROUND: Despite substantial progress in maternal and neonatal health, Rwanda's mortality rates remain high, necessitating innovative approaches to meet health related Sustainable Development Goals (SDGs). By leveraging data collected from Electro...

Consecutive prediction of adverse maternal outcomes of preeclampsia, using the PIERS-ML and fullPIERS models: A multicountry prospective observational study.

PLoS medicine
BACKGROUND: Preeclampsia is a potentially life-threatening pregnancy complication. Among women whose pregnancies are complicated by preeclampsia, the Preeclampsia Integrated Estimate of RiSk (PIERS) models (i.e., the PIERS Machine Learning [PIERS-ML]...

Application of a methodological framework for the development and multicenter validation of reliable artificial intelligence in embryo evaluation.

Reproductive biology and endocrinology : RB&E
BACKGROUND: Artificial intelligence (AI) models analyzing embryo time-lapse images have been developed to predict the likelihood of pregnancy following in vitro fertilization (IVF). However, limited research exists on methods ensuring AI consistency ...