AIMC Topic: Pre-Eclampsia
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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...
Machine Learning Algorithms Versus Classical Regression Models in Pre-Eclampsia Prediction: A Systematic Review.
Current hypertension reports
May 28, 2024
PURPOSE OF REVIEW: Machine learning (ML) approaches are an emerging alternative for healthcare risk prediction. We aimed to synthesise the literature on ML and classical regression studies exploring potential prognostic factors and to compare predict...
Validation of the first-trimester machine learning model for predicting pre-eclampsia in an Asian population.
International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics
Apr 26, 2024
OBJECTIVES: To evaluate the performance of an artificial intelligence (AI) and machine learning (ML) model for first-trimester screening for pre-eclampsia in a large Asian population.
The role of cell-free DNA biomarkers and patient data in the early prediction of preeclampsia: an artificial intelligence model.
American journal of obstetrics and gynecology
Mar 2, 2024
BACKGROUND: Accurate individualized assessment of preeclampsia risk enables the identification of patients most likely to benefit from initiation of low-dose aspirin at 12 to 16 weeks of gestation when there is evidence for its effectiveness, and ena...
Prediction of preeclampsia from retinal fundus images via deep learning in singleton pregnancies: a prospective cohort study.
Journal of hypertension
Jan 15, 2024
INTRODUCTION: Early prediction of preeclampsia (PE) is of universal importance in controlling the disease process. Our study aimed to assess the feasibility of using retinal fundus images to predict preeclampsia via deep learning in singleton pregnan...
Identification and verification of diagnostic biomarkers based on mitochondria-related genes related to immune microenvironment for preeclampsia using machine learning algorithms.
Frontiers in immunology
Jan 8, 2024
Preeclampsia is one of the leading causes of maternal and fetal morbidity and mortality worldwide. Preeclampsia is linked to mitochondrial dysfunction as a contributing factor in its progression. This study aimed to develop a novel diagnostic model b...
Novelelectronic health records applied for prediction of pre-eclampsia: Machine-learning algorithms.
Pregnancy hypertension
Oct 28, 2021
OBJECTIVE: To predict risk of pre-eclampsia (PE) in women using machine learning (ML) algorithms, based on electronic health records (EHR) collected at the early second trimester.
GestAltNet: aggregation and attention to improve deep learning of gestational age from placental whole-slide images.
Laboratory investigation; a journal of technical methods and pathology
Mar 5, 2021
The placenta is the first organ to form and performs the functions of the lung, gut, kidney, and endocrine systems. Abnormalities in the placenta cause or reflect most abnormalities in gestation and can have life-long consequences for the mother and ...
Integrated Learning: Screening Optimal Biomarkers for Identifying Preeclampsia in Placental mRNA Samples.
Computational and mathematical methods in medicine
Feb 23, 2021
Preeclampsia (PE) is a maternal disease that causes maternal and child death. Treatment and preventive measures are not sound enough. The problem of PE screening has attracted much attention. The purpose of this study is to screen placental mRNA to o...
Integrated analysis of multiple microarray studies to identify novel gene signatures in preeclampsia.
Placenta
Feb 2, 2021
INTRODUCTION: Preeclampsia (PE) is one of the major causes of maternal and fetal morbidity and mortality in pregnancy worldwide. However, the intrinsic molecular mechanisms underlying the pathogenesis of PE have not yet been fully elucidated.
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