Analysis of epidemiological association patterns of serum thyrotropin by combining random forests and Bayesian networks

BACKGROUND: Approaching epidemiological data with flexible machine learning algorithms is of great value for understanding disease-specific association patterns. However, it can be difficult to correctly extract and understand those patterns due to the lack of model interpretability. METHOD: We here...

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Detalles Bibliográficos
Autores principales: Becker, Ann-Kristin, Ittermann, Till, Dörr, Markus, Felix, Stephan B., Nauck, Matthias, Teumer, Alexander, Völker, Uwe, Völzke, Henry, Kaderali, Lars, Nath, Neetika
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9302835/
https://www.ncbi.nlm.nih.gov/pubmed/35862421
http://dx.doi.org/10.1371/journal.pone.0271610

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9302835/
https://www.ncbi.nlm.nih.gov/pubmed/35862421
http://dx.doi.org/10.1371/journal.pone.0271610

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