Predicting preterm birth through vaginal microbiota, cervical length, and WBC using a machine learning model.

ABSTRACT: An association between the vaginal microbiome and preterm birth has been reported. However, in practice, it is difficult to predict premature birth using the microbiome because the vaginal microbial community varies highly among samples depending on the individual, and the prediction rate is very low. The purpose of this study was to select markers that improve predictive power through machine learning among various vaginal microbiota and develop a prediction algorithm with better predictive power that combines clinical information. As a multicenter case-control study with 150 Korean pregnant women with 54 preterm delivery group and 96 full-term delivery group, cervicovaginal fluid was collected from pregnant women during mid-pregnancy. Their demographic profiles (age, BMI, education level

SUBMITTER: Park S 

PROVIDER: S-EPMC9378785 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature