A Bayesian network perspective on neonatal pneumonia in pregnant women with diabetes mellitus

Objective To predict the influencing factors of neonatal pneumonia in pregnant women with diabetes mellitus using a Bayesian network model. By examining the intricate network connections between the numerous variables given by Bayesian networks (BN), this study aims to compare the prediction effect...

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Bibliographic Details
Published in:	BMC medical research methodology Vol. 23; no. 1; pp. 1 - 12
Main Authors:	Lin, Yue, Chen, Jia Shen, Zhong, Ni, Zhang, Ao, Pan, Haiyan
Format:	Journal Article
Language:	English
Published:	London BioMed Central 25.10.2023 BioMed Central Ltd Springer Nature B.V BMC
Subjects:	Algorithms Amniotic fluid Bacterial pneumonia Bayesian networks Bayesian statistical decision theory Care and treatment Data mining Decision trees Diabetes Diabetes in pregnancy Diabetics Diagnosis Evaluation Gestational diabetes Glucose Health aspects Health Sciences Hypotheses Infants (Newborn) K-Dependence Bayesian Classifier Machine learning Medical research Medicine Medicine & Public Health Naive Bayes network Neonatal pneumonia Newborn babies Pneumonia Pregnancy Pregnant women Probability distribution Random variables Software Statistical Theory and Methods Statistics for Life Sciences Support vector machines Theory of Medicine/Bioethics Tree Augmented Naive Bayes model Womens health China Neonatal pneumonia K-Dependence Bayesian Classifier Naive Bayes network Bayesian networks Tree Augmented Naive Bayes model
ISSN:	1471-2288, 1471-2288
Online Access:	Get full text
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