AIMC Topic: Pregnancy Complications

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Predicting Unfavorable Pregnancy Outcomes in Polycystic Ovary Syndrome (PCOS) Patients Using Machine Learning Algorithms.

Medicina (Kaunas, Lithuania)
: Polycystic ovary syndrome (PCOS) is a complex disorder that can negatively impact the obstetrical outcomes. The aim of this study was to determine the predictive performance of four machine learning (ML)-based algorithms for the prediction of adver...

A machine learning model to predict the risk of perinatal depression: Psychosocial and sleep-related factors in the Life-ON study cohort.

Psychiatry research
Perinatal depression (PND) is a common complication of pregnancy associated with serious health consequences for both mothers and their babies. Identifying risk factors for PND is key to early detect women at increased risk of developing this conditi...

Predicting first time depression onset in pregnancy: applying machine learning methods to patient-reported data.

Archives of women's mental health
PURPOSE: To develop a machine learning algorithm, using patient-reported data from early pregnancy, to predict later onset of first time moderate-to-severe depression.

Machine learning techniques for prediction in pregnancy complicated by autoimmune rheumatic diseases: Applications and challenges.

International immunopharmacology
Autoimmune rheumatic diseases are chronic conditions affecting multiple systems and often occurring in young women of childbearing age. The diseases and the physiological characteristics of pregnancy significantly impact maternal-fetal health and pre...

Prediction of metabolic syndrome following a first pregnancy.

American journal of obstetrics and gynecology
BACKGROUND: The prevalence of metabolic syndrome is rapidly increasing in the United States. We hypothesized that prediction models using data obtained during pregnancy can accurately predict the future development of metabolic syndrome.

Prediction of pregnancy-related complications in women undergoing assisted reproduction, using machine learning methods.

Fertility and sterility
OBJECTIVE: To use machine learning methods to develop prediction models of pregnancy complications in women who conceived with assisted reproductive techniques (ART).

Machine learning based assessment of preclinical health questionnaires.

International journal of medical informatics
BACKGROUND: Within modern health systems, the possibility of accessing a large amount and a variety of data related to patients' health has increased significantly over the years. The source of this data could be mobile and wearable electronic system...

Deep Learning Algorithm-Based Magnetic Resonance Imaging Feature-Guided Serum Bile Acid Profile and Perinatal Outcomes in Intrahepatic Cholestasis of Pregnancy.

Computational and mathematical methods in medicine
This study was aimed to explore magnetic resonance imaging (MRI) based on deep learning belief network model in evaluating serum bile acid profile and adverse perinatal outcomes of intrahepatic cholestasis of pregnancy (ICP) patients. Fifty ICP pregn...

[The role of bile acid measurement in the management of intrahepatic cholestasis of pregnancy].

Orvosi hetilap
Introduction: Intrahepatic cholestasis of pregnancy complicates 1% of pregnancies. It increases the risk of severe fetal complications significantly, including preterm delivery and stillbirth. Objective: To summarize our experience with serum total b...

Detection of maternal and fetal stress from the electrocardiogram with self-supervised representation learning.

Scientific reports
In the pregnant mother and her fetus, chronic prenatal stress results in entrainment of the fetal heartbeat by the maternal heartbeat, quantified by the fetal stress index (FSI). Deep learning (DL) is capable of pattern detection in complex medical d...