AIMC Topic: Premature Birth

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Quantifying the Impacts of Pre- and Post-Conception TSH Levels on Birth Outcomes: An Examination of Different Machine Learning Models.

Frontiers in endocrinology
BACKGROUND: While previous studies identified risk factors for diverse pregnancy outcomes, traditional statistical methods had limited ability to quantify their impacts on birth outcomes precisely. We aimed to use a novel approach that applied differ...

Machine learning guided postnatal gestational age assessment using new-born screening metabolomic data in South Asia and sub-Saharan Africa.

BMC pregnancy and childbirth
BACKGROUND: Babies born early and/or small for gestational age in Low and Middle-income countries (LMICs) contribute substantially to global neonatal and infant mortality. Tracking this metric is critical at a population level for informed policy, ad...

Prediction of preterm birth based on machine learning using bacterial risk score in cervicovaginal fluid.

American journal of reproductive immunology (New York, N.Y. : 1989)
PROBLEM: Preterm birth (PTB) is a major cause of increased morbidity and mortality in newborns. The main cause of spontaneous PTB (sPTB) is the activation of an inflammatory response as a result of ascending genital tract infection. Despite various s...

Prediction of obstetrical and fetal complications using automated electronic health record data.

American journal of obstetrics and gynecology
An increasing number of delivering women experience major morbidity and mortality. Limited work has been done on automated predictive models that could be used for prevention. Using only routinely collected obstetrical data, this study aimed to devel...

Intelligent system based on data mining techniques for prediction of preterm birth for women with cervical cerclage.

Computational biology and chemistry
Preterm birth, defined as a delivery before 37 weeks' gestation, continues to affect 8-15% of all pregnancies and is associated with significant neonatal morbidity and mortality. Effective prediction of timing of delivery among women identified to be...

Artificial Neural Network Analysis of Spontaneous Preterm Labor and Birth and Its Major Determinants.

Journal of Korean medical science
BACKGROUND: Little research based on the artificial neural network (ANN) is done on preterm birth (spontaneous preterm labor and birth) and its major determinants. This study uses an ANN for analyzing preterm birth and its major determinants.

Utilizing Deep Learning and Genome Wide Association Studies for Epistatic-Driven Preterm Birth Classification in African-American Women.

IEEE/ACM transactions on computational biology and bioinformatics
Genome-Wide Association Studies (GWAS) are used to identify statistically significant genetic variants in case-control studies. The main objective is to find single nucleotide polymorphisms (SNPs) that influence a particular phenotype (i.e., disease ...

Application of machine-learning to predict early spontaneous preterm birth among nulliparous non-Hispanic black and white women.

Annals of epidemiology
PURPOSE: Spontaneous preterm birth is a leading cause of perinatal mortality in the United States, occurring disproportionately among non-Hispanic black women compared to other race-ethnicities. Clinicians lack tools to identify first-time mothers at...