AIMC Topic: Peripartum Period
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Novel machine learning applications in peripartum care: a scoping review.
American journal of obstetrics & gynecology MFM
Jan 23, 2025
OBJECTIVE: Machine learning (ML), a subtype of artificial intelligence (AI), presents predictive modeling and dynamic diagnostic tools to facilitate early interventions and improve decision-making. Considering the global challenges of maternal, fetal...
Disentangling the Genetic Landscape of Peripartum Depression: A Multi-Polygenic Machine Learning Approach on an Italian Sample.
Genes
Nov 26, 2024
BACKGROUND: The genetic determinants of peripartum depression (PPD) are not fully understood. Using a multi-polygenic score approach, we characterized the relationship between genome-wide information and the history of PPD in patients with mood disor...
Development and validation of an electrocardiographic artificial intelligence model for detection of peripartum cardiomyopathy.
American journal of obstetrics & gynecology MFM
Mar 4, 2024
BACKGROUND: This study used electrocardiogram data in conjunction with artificial intelligence methods as a noninvasive tool for detecting peripartum cardiomyopathy.
Electrocardiogram-based deep learning model to screen peripartum cardiomyopathy.
American journal of obstetrics & gynecology MFM
Oct 30, 2023
BACKGROUND: Peripartum cardiomyopathy, one of the most fatal conditions during delivery, results in heart failure secondary to left ventricular systolic dysfunction. Left ventricular dysfunction can result in abnormalities in electrocardiography. How...
Discovery of different metabotypes in overconditioned dairy cows by means of machine learning.
Journal of dairy science
Jul 31, 2020
Using data from targeted metabolomics in serum in combination with machine learning (ML) approaches, we aimed at (1) identifying divergent metabotypes in overconditioned cows and at (2) exploring how metabotypes are associated with lactation performa...
Predicting peripartum depression using elastic net regression and machine learning: the role of remnant cholesterol.
BMC pregnancy and childbirth
May 8, 2025
BACKGROUND: Traditional statistical methods have dominated research on peripartum depression (PPD), but innovative approaches may provide deeper insights. This study aims to predict the impact factors of PPD using elastic net regression (ENR) combine...
A scoping review and quality assessment of machine learning techniques in identifying maternal risk factors during the peripartum phase for adverse child development.
PloS one
Jan 1, 2025
Maternal exposure to environmental risk factors (e.g., heavy metal exposure) or mental health problems during the peripartum phase has been shown to lead to negative and lasting impacts on child development and life in adulthood. Given the importance...
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