Semi-supervised incremental learning with few examples for discovering medical association rules

BACKGROUND: Association Rules are one of the main ways to represent structural patterns underlying raw data. They represent dependencies between sets of observations contained in the data. The associations established by these rules are very useful in the medical domain, for example in the predictiv...

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Detalles Bibliográficos
Autores principales: Sánchez-de-Madariaga, Ricardo, Martinez-Romo, Juan, Escribano, José Miguel Cantero, Araujo, Lourdes
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8785547/
https://www.ncbi.nlm.nih.gov/pubmed/35073885
http://dx.doi.org/10.1186/s12911-022-01755-3

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8785547/
https://www.ncbi.nlm.nih.gov/pubmed/35073885
http://dx.doi.org/10.1186/s12911-022-01755-3

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