Cargando…
Neural-signature methods for structured EHR prediction
Models that can effectively represent structured Electronic Healthcare Records (EHR) are central to an increasing range of applications in healthcare. Due to the sequential nature of health data, Recurrent Neural Networks have emerged as the dominant component within state-of-the-art architectures....
Autores principales: | Vauvelle, Andre, Creed, Paidi, Denaxas, Spiros |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9730578/ https://www.ncbi.nlm.nih.gov/pubmed/36476601 http://dx.doi.org/10.1186/s12911-022-02055-6 |
Ejemplares similares
-
ClustEHR: a tool for generating synthetic EHR data for unsupervised learning experiments.
por: Alexander, Nonie, et al.
Publicado: (2022) -
A semi-supervised approach for rapidly creating clinical biomarker phenotypes in the UK Biobank using different primary care EHR and clinical terminology systems
por: Denaxas, Spiros, et al.
Publicado: (2020) -
Modeling EHR with the openEHR approach: an exploratory study in China
por: Min, Lingtong, et al.
Publicado: (2018) -
CODE-EHR best practice framework for the use of structured electronic healthcare records in clinical research
por: Kotecha, Dipak, et al.
Publicado: (2022) -
Mining of EHR for interface terminology concepts for annotating EHRs of COVID patients
por: Keloth, Vipina K., et al.
Publicado: (2023)