Machine learning prediction of preterm birth in women under 35 using routine biomarkers in a retrospective cohort study

Xiaojing Teng; Mengting Liu; Zhiyi Wang; Xueyan Dong

doi:10.1038/s41598-025-92814-y

Machine learning prediction of preterm birth in women under 35 using routine biomarkers in a retrospective cohort study

X Xiaojing Teng M Mengting Liu (Center of Nanomaterials for Renewable Energy, State Key Laboratory of Electrical Insulation and Power Equipment, School of Electrical Engineering) Z Zhiyi Wang X Xueyan Dong

Download PDF View Original

APA MLA Chicago BibTeX RIS

Article Details

Journal Scientific Reports

Volume / Issue Vol. 15, Issue 1

Published March 25, 2025

DOI 10.1038/s41598-025-92814-y

ISSN 2045-2322

Publisher Nature Portfolio

Journal Info

Scientific Reports

Nature Portfolio

ISSN: 2045-2322 Open Access Life Sciences

Authors (4)

X

Xiaojing Teng

M

Mengting Liu

Center of Nanomaterials for Renewable Energy, State Key Laboratory of Electrical Insulation and Power Equipment, School of Electrical Engineering

Z

Zhiyi Wang

X

Xueyan Dong

Machine learning prediction of preterm birth in women under 35 using routine biomarkers in a retrospective cohort study

Article Details

Journal Info

Authors (4)

Related Articles from this Journal

AI models of unstable flow exhibit hallucination

The MCAA-YOLO + XPBI integrated model provides a hybrid intelligent measurement method for predicting the body weight of Hu sheep

Application of iron nano-biofertilizers as a sustainable approach to enhance growth and micronutrient uptake in soybean

Adolescents’ status-dependent public and private conformity to prosocial and antisocial decisions in a public goods game

An explainable hierarchical capsule network framework for supporting expert sensory evaluation of herbal medicines with natural language generation