Cargando…
Risk factor assessments of temporomandibular disorders via machine learning
This study aimed to use artificial intelligence to determine whether biological and psychosocial factors, such as stress, socioeconomic status, and working conditions, were major risk factors for temporomandibular disorders (TMDs). Data were retrieved from the fourth Korea National Health and Nutrit...
Autores principales: | Lee, Kwang-Sig, Jha, Nayansi, Kim, Yoon-Ji |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8492627/ https://www.ncbi.nlm.nih.gov/pubmed/34611188 http://dx.doi.org/10.1038/s41598-021-98837-5 |
Ejemplares similares
-
Diagnosis of temporomandibular disorders using artificial intelligence technologies: A systematic review and meta-analysis
por: Jha, Nayansi, et al.
Publicado: (2022) -
Three-Dimensional Quantitative Assessment of Condylar Displacement and Adaptive Remodeling in Asymmetrical Mandibular Prognathism Patients After Sagittal Split Ramus Osteotomy
por: Jha, Nayansi, et al.
Publicado: (2023) -
Machine Learning on Early Diagnosis of Depression
por: Lee, Kwang-Sig, et al.
Publicado: (2022) -
Machine learning-based risk factor analysis of necrotizing enterocolitis in very low birth weight infants
por: Cho, Hannah, et al.
Publicado: (2022) -
Machine learning-based risk factor analysis of adverse birth outcomes in very low birth weight infants
por: Cho, Hannah, et al.
Publicado: (2022)