Predicting preterm birth using auto-ML frameworks: a large observational study using electronic inpatient discharge data
Deming Kong,
Ye Tao,
Haiyan Xiao
et al.
Abstract: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 (AutoML) were used to construct machine learning models including tree-based models, ensembled models, and deep neural networks on the training sample (N = 536,971). The area under the curve (AUC) and training times w… Show more
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