Cargando…
Development and Validation of a Deep Learning Model for Detection of Allergic Reactions Using Safety Event Reports Across Hospitals
IMPORTANCE: Although critical to patient safety, health care–related allergic reactions are challenging to identify and monitor. OBJECTIVE: To develop a deep learning model to identify allergic reactions in the free-text narrative of hospital safety reports and evaluate its generalizability, efficie...
Autores principales: | Yang, Jie, Wang, Liqin, Phadke, Neelam A., Wickner, Paige G., Mancini, Christian M., Blumenthal, Kimberly G., Zhou, Li |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Medical Association
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7670315/ https://www.ncbi.nlm.nih.gov/pubmed/33196805 http://dx.doi.org/10.1001/jamanetworkopen.2020.22836 |
Ejemplares similares
-
COVID-19 Vaccination in Patients with Reported Allergic Reactions: Updated Evidence and Suggested Approach
por: Banerji, Aleena, et al.
Publicado: (2021) -
mRNA Vaccines to Prevent COVID-19 Disease and Reported Allergic Reactions: Current Evidence and Suggested Approach
por: Banerji, Aleena, et al.
Publicado: (2021) -
First-Dose mRNA COVID-19 Vaccine Allergic Reactions: Limited Role for Excipient Skin Testing
por: Wolfson, Anna R., et al.
Publicado: (2021) -
Association of Self-reported High-Risk Allergy History With Allergy Symptoms After COVID-19 Vaccination
por: Li, Lily, et al.
Publicado: (2021) -
Allergic symptoms after mRNA COVID-19 vaccination and risk of incomplete vaccination
por: Robinson, Lacey B., et al.
Publicado: (2021)