Exploiting the interplay between cross-sectional and longitudinal data in Class III malocclusion patients

The aim of the study was to investigate how to improve the forecasting of craniofacial unbalance risk during growth among patients affected by Class III malocclusion. To this purpose we used computational methodologies such as Transductive Learning (TL), Boosting (B), and Feature Engineering (FE) in...

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Detalles Bibliográficos
Autores principales: Barelli, Enrico, Ottaviani, Ennio, Auconi, Pietro, Caldarelli, Guido, Giuntini, Veronica, McNamara, James A., Franchi, Lorenzo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6470156/
https://www.ncbi.nlm.nih.gov/pubmed/30996304
http://dx.doi.org/10.1038/s41598-019-42384-7

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6470156/
https://www.ncbi.nlm.nih.gov/pubmed/30996304
http://dx.doi.org/10.1038/s41598-019-42384-7

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