Cargando…
Identifying incident dementia by applying machine learning to a very large administrative claims dataset
Alzheimer's disease and related dementias (ADRD) are highly prevalent conditions, and prior efforts to develop predictive models have relied on demographic and clinical risk factors using traditional logistical regression methods. We hypothesized that machine-learning algorithms using administr...
Autores principales: | Nori, Vijay S., Hane, Christopher A., Martin, David C., Kravetz, Alexander D., Sanghavi, Darshak M. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6611655/ https://www.ncbi.nlm.nih.gov/pubmed/31276468 http://dx.doi.org/10.1371/journal.pone.0203246 |
Ejemplares similares
-
Deep neural network models for identifying incident dementia using claims and EHR datasets
por: Nori, Vijay S., et al.
Publicado: (2020) -
Predicting Onset of Dementia Using Clinical Notes and Machine Learning: Case-Control Study
por: Hane, Christopher A, et al.
Publicado: (2020) -
Machine learning models to predict onset of dementia: A label learning approach
por: Nori, Vijay S., et al.
Publicado: (2019) -
Validation of an algorithm for identifying MS cases in administrative health claims datasets
por: Culpepper, William J., et al.
Publicado: (2019) -
Predicting Diagnosis of Alzheimer’s Disease and Related Dementias Using Administrative Claims
por: Albrecht, Jennifer S., et al.
Publicado: (2018)