Real-time clinician text feeds from electronic health records
Abstract Analyses of search engine and social media feeds have been attempted for infectious disease outbreaks, but have been found to be susceptible to artefactual distortions from health scares or keyword spamming in social media or the public internet. We describe an approach using real-time aggr...
Guardado en:
Autores principales: | James T. H. Teo, Vlad Dinu, William Bernal, Phil Davidson, Vitaliy Oliynyk, Cormac Breen, Richard D. Barker, Richard J. B. Dobson |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/d131c890e49a44d29a3e707c80b44572 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Machine learning for patient risk stratification: standing on, or looking over, the shoulders of clinicians?
por: Brett K. Beaulieu-Jones, et al.
Publicado: (2021) -
Natural language word embeddings as a glimpse into healthcare language and associated mortality surrounding end of life
por: Wei Gao, et al.
Publicado: (2021) -
Training for our digital future: a human-centered design approach to graduate medical education for aspiring clinician-innovators
por: Jocelyn Carter, et al.
Publicado: (2018) -
Harnessing electronic health records to study emerging environmental disasters: a proof of concept with perfluoroalkyl substances (PFAS)
por: Mary Regina Boland, et al.
Publicado: (2021) -
Measuring the effect of Non-Pharmaceutical Interventions (NPIs) on mobility during the COVID-19 pandemic using global mobility data
por: Berber T. Snoeijer, et al.
Publicado: (2021)