AIMC Topic: Pregnancy
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Development and evaluation of machine learning models for predicting large-for-gestational-age newborns in women exposed to radiation prior to pregnancy.
BMC medical informatics and decision making
Jun 20, 2024
INTRODUCTION: The correlation between radiation exposure before pregnancy and abnormal birth weight has been previously proven. However, for large-for-gestational-age (LGA) babies in women exposed to radiation before becoming pregnant, there is no pr...
Predictive analysis on the factors associated with birth Outcomes: A machine learning perspective.
International journal of medical informatics
Jun 19, 2024
BACKGROUND: Recent studies reveal that around 1.9 million stillbirths occur annually worldwide, with Sub-Saharan Africa having among the highest cases. Some Sub-Saharan African countries, including Ghana, failed to meet Millennium Development Goal 5 ...
Machine learning predicts the serum PFOA and PFOS levels in pregnant women: Enhancement of fatty acid status on model performance.
Environment international
Jun 19, 2024
Human exposure to per- and polyfluoroalkyl substances (PFASs) has received considerable attention, particularly in pregnant women because of their dramatic changes in physiological status and dietary patterns. Predicting internal PFAS exposure in pre...
Machine learning analysis with population data for prepregnancy and perinatal risk factors for the neurodevelopmental delay of offspring.
Scientific reports
Jun 18, 2024
Neurodevelopmental disorders (NDD) in offspring are associated with a complex combination of pre-and postnatal factors. This study uses machine learning and population data to evaluate the association between prepregnancy or perinatal risk factors an...
Prediction of post-delivery hemoglobin levels with machine learning algorithms.
Scientific reports
Jun 17, 2024
Predicting postpartum hemorrhage (PPH) before delivery is crucial for enhancing patient outcomes, enabling timely transfer and implementation of prophylactic therapies. We attempted to utilize machine learning (ML) using basic pre-labor clinical data...
A pilot randomized trial of automatic, artificial intelligence-based vs manual, electronic medical record-based remote postpartum blood pressure monitoring.
American journal of obstetrics and gynecology
Jun 14, 2024
Diagnosis of placenta accreta spectrum using ultrasound texture feature fusion and machine learning.
Computers in biology and medicine
Jun 14, 2024
INTRODUCTION: Placenta accreta spectrum (PAS) is an obstetric disorder arising from the abnormal adherence of the placenta to the uterine wall, often leading to life-threatening complications including postpartum hemorrhage. Despite its significance,...
Machine learning, advanced data analysis, and a role in pregnancy care? How can we help improve preeclampsia outcomes?
Pregnancy hypertension
Jun 13, 2024
The value of machine learning capacity in maternal health, and in particular prediction of preeclampsia will only be realised when there are high quality clinical data provided, representative populations included, different health systems and models...
Quantitative prediction of postpartum hemorrhage in cesarean section on machine learning.
BMC medical informatics and decision making
Jun 13, 2024
BACKGROUND: Cesarean section-induced postpartum hemorrhage (PPH) potentially causes anemia and hypovolemic shock in pregnant women. Hence, it is helpful for obstetricians and anesthesiologists to prepare pre-emptive prevention when predicting PPH occ...
Prediction model of preeclampsia using machine learning based methods: a population based cohort study in China.
Frontiers in endocrinology
Jun 11, 2024
INTRODUCTION: Preeclampsia is a disease with an unknown pathogenesis and is one of the leading causes of maternal and perinatal morbidity. At present, early identification of high-risk groups for preeclampsia and timely intervention with aspirin is a...
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