Inferring multimodal latent topics from electronic health records
Electronic Health Records (EHR) are subject to noise, biases and missing data. Here, the authors present MixEHR, a multi-view Bayesian framework related to collaborative filtering and latent topic models for EHR data integration and modeling.
Guardado en:
Autores principales: | Yue Li, Pratheeksha Nair, Xing Han Lu, Zhi Wen, Yuening Wang, Amir Ardalan Kalantari Dehaghi, Yan Miao, Weiqi Liu, Tamas Ordog, Joanna M. Biernacka, Euijung Ryu, Janet E. Olson, Mark A. Frye, Aihua Liu, Liming Guo, Ariane Marelli, Yuri Ahuja, Jose Davila-Velderrain, Manolis Kellis |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2020
|
Materias: | |
Acceso en línea: | https://doaj.org/article/f93fdf7cc75646b5afb46727b4bdaa9e |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
A multiresolution framework to characterize single-cell state landscapes
por: Shahin Mohammadi, et al.
Publicado: (2020) -
Congenital heart disease phenotyping in large claims databases
por: Chao Li, et al.
Publicado: (2021) -
NEBULA is a fast negative binomial mixed model for differential or co-expression analysis of large-scale multi-subject single-cell data
por: Liang He, et al.
Publicado: (2021) -
Cardiac index in adults with repaired tetralogy of Fallot: Are we missing the forest for the trees?
por: Maria Fadous, et al.
Publicado: (2021) -
SARS-CoV-2 gene content and COVID-19 mutation impact by comparing 44 Sarbecovirus genomes
por: Irwin Jungreis, et al.
Publicado: (2021)