Predicting preterm birth using auto-ML frameworks: a large observational study using electronic inpatient discharge data

Predicting preterm birth using auto-ML frameworks: a large observational study using electronic inpatient discharge data

BackgroundTo develop and compare different AutoML frameworks and machine learning models to predict premature birth.MethodsThe study used a large electronic medical record database to include 715,962 participants who had the principal diagnosis code of childbirth. Three Automatic Machine Learning (A...

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Bibliographic Details
Main Authors: Deming Kong, Ye Tao, Haiyan Xiao, Huini Xiong, Weizhong Wei, Miao Cai
Format: Article
Language:English
Published: Frontiers Media S.A. 2024-01-01
Series:Frontiers in Pediatrics
Subjects:
preterm birth
machine learning
administrative data
China
autoML
Online Access:https://www.frontiersin.org/articles/10.3389/fped.2024.1330420/full

Internet

https://www.frontiersin.org/articles/10.3389/fped.2024.1330420/full

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