Finding Potential Adverse Events in the Unstructured Text of Electronic Health Care Records: Development of the Shakespeare Method

BACKGROUND: Big data tools provide opportunities to monitor adverse events (patient harm associated with medical care) (AEs) in the unstructured text of electronic health care records (EHRs). Writers may explicitly state an apparent association between treatment and adverse outcome (“attributed”) or...

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Detalles Bibliográficos
Autores principales: Bright, Roselie A, Rankin, Summer K, Dowdy, Katherine, Blok, Sergey V, Bright, Susan J, Palmer, Lee Anne M
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2021
Materias:
Original Paper
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10414364/
https://www.ncbi.nlm.nih.gov/pubmed/37725533
http://dx.doi.org/10.2196/27017

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10414364/
https://www.ncbi.nlm.nih.gov/pubmed/37725533
http://dx.doi.org/10.2196/27017

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