AIMC Topic: Pre-Eclampsia
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Artificial Intelligence and Machine Learning in Preeclampsia.
Arteriosclerosis, thrombosis, and vascular biology
Jan 2, 2025
Preeclampsia is a multisystem hypertensive disorder that manifests itself after 20 weeks of pregnancy, along with proteinuria. The pathophysiology of preeclampsia is incompletely understood. Artificial intelligence, especially machine learning with i...
A comprehensive and bias-free machine learning approach for risk prediction of preeclampsia with severe features in a nulliparous study cohort.
BMC pregnancy and childbirth
Dec 24, 2024
Preeclampsia is one of the leading causes of maternal morbidity, with consequences during and after pregnancy. Because of its diverse clinical presentation, preeclampsia is an adverse pregnancy outcome that is uniquely challenging to predict and mana...
Identification of hub genes, diagnostic model, and immune infiltration in preeclampsia by integrated bioinformatics analysis and machine learning.
BMC pregnancy and childbirth
Dec 21, 2024
PURPOSE: This study aimed to identify novel biomarkers for preeclampsia (PE) diagnosis by integrating Weighted Gene Co-expression Network Analysis (WGCNA) with machine learning techniques.
NMF typing and machine learning algorithm-based exploration of preeclampsia-related mechanisms on ferroptosis signature genes.
Cell biology and toxicology
Dec 21, 2024
BACKGROUND: Globally, pre-eclampsia (PE) poses a major threat to the health and survival of pregnant women and fetuses, contributing significantly to morbidity and mortality. Recent studies suggest a pathological link between PE and ferroptosis. We a...
Enhancing decision-making with linear diophantine multi-fuzzy set: application of novel information measures in medical and engineering fields.
Scientific reports
Nov 18, 2024
This study offers a comprehensive analysis of novel information for linear diophantine multi-fuzzy sets and illustrates its applications in practical scenarios. We introduce innovative similarity metrics tailored for linear diophantine multi-fuzzy se...
Development and validation of preeclampsia predictive models using key genes from bioinformatics and machine learning approaches.
Frontiers in immunology
Oct 31, 2024
BACKGROUND: Preeclampsia (PE) poses significant diagnostic and therapeutic challenges. This study aims to identify novel genes for potential diagnostic and therapeutic targets, illuminating the immune mechanisms involved.
Prediction of pre-eclampsia with machine learning approaches: Leveraging important information from routinely collected data.
International journal of medical informatics
Oct 5, 2024
BACKGROUND: Globally, pre-eclampsia (PE) is a leading cause of maternal and perinatal morbidity and mortality. PE prediction using routinely collected data has the advantage of being widely applicable, particularly in low-resource settings. Early int...
Mobil Monitoring Doppler Ultrasound (MoMDUS) study: protocol for a prospective, observational study investigating the use of artificial intelligence and low-cost Doppler ultrasound for the automated quantification of hypertension, pre-eclampsia and fetal growth restriction in rural Guatemala.
BMJ open
Sep 10, 2024
INTRODUCTION: Undetected high-risk conditions in pregnancy are a leading cause of perinatal mortality in low-income and middle-income countries. A key contributor to adverse perinatal outcomes in these settings is limited access to high-quality scree...
Pseudo-random Number Generator Influences on Average Treatment Effect Estimates Obtained with Machine Learning.
Epidemiology (Cambridge, Mass.)
Aug 16, 2024
BACKGROUND: The use of machine learning to estimate exposure effects introduces a dependence between the results of an empirical study and the value of the seed used to fix the pseudo-random number generator.
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...
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