Cargando…
Deep Learning from EEG Reports for Inferring Underspecified Information
Secondary use(1)of electronic health records (EHRs) often relies on the ability to automatically identify and extract information from EHRs. Unfortunately, EHRs are known to suffer from a variety of idiosyncrasies – most prevalently, they have been shown to often omit or underspecify information. Ad...
Autores principales: | Goodwin, Travis R., Harabagiu, Sanda M. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Medical Informatics Association
2017
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5543361/ https://www.ncbi.nlm.nih.gov/pubmed/28815118 |
Ejemplares similares
-
Inferring the Interactions of Risk Factors from EHRs
por: Goodwin, Travis, et al.
Publicado: (2016) -
Active Deep Learning-Based Annotation of Electroencephalography Reports for Cohort Identification
por: Maldonado, Ramon, et al.
Publicado: (2017) -
Memory-Augmented Active Deep Learning for Identifying Relations Between Distant Medical Concepts in Electroencephalography Reports
por: Maldonado, Ramon, et al.
Publicado: (2018) -
Spontaneous attribution of underspecified belief of social partners facilitates processing shared information
por: Hegedüs, Andrea Márta, et al.
Publicado: (2022) -
Learning relevance models for patient cohort retrieval
por: Goodwin, Travis R, et al.
Publicado: (2018)