Cargando…
Representation learning for clinical time series prediction tasks in electronic health records
BACKGROUND: Electronic health records (EHRs) provide possibilities to improve patient care and facilitate clinical research. However, there are many challenges faced by the applications of EHRs, such as temporality, high dimensionality, sparseness, noise, random error and systematic bias. In particu...
Autores principales: | Ruan, Tong, Lei, Liqi, Zhou, Yangming, Zhai, Jie, Zhang, Le, He, Ping, Gao, Ju |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6916209/ https://www.ncbi.nlm.nih.gov/pubmed/31842854 http://dx.doi.org/10.1186/s12911-019-0985-7 |
Ejemplares similares
-
Multi-layer Representation Learning and Its Application to Electronic Health Records
por: Yang, Shan, et al.
Publicado: (2021) -
Learning and visualizing chronic latent representations using electronic health records
por: Chushig-Muzo, David, et al.
Publicado: (2022) -
Forecasting dengue and influenza incidences using a sparse representation of Google trends, electronic health records, and time series data
por: Rangarajan, Prashant, et al.
Publicado: (2019) -
Deep representation learning of electronic health records to unlock patient stratification at scale
por: Landi, Isotta, et al.
Publicado: (2020) -
Multi-Task Network Representation Learning
por: Xie, Yu, et al.
Publicado: (2020)