Leveraging structured and unstructured electronic health record data to detect reasons for suboptimal statin therapy use in patients with atherosclerotic cardiovascular disease

OBJECTIVE: To determine whether natural language processing (NLP) of unstructured medical text can improve identification of ASCVD patients not using high-intensity statin therapy (HIST) due to statin-associated side effects (SASEs) and other reasons. METHODS: Reviewers annotated reasons for not pre...

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Detalles Bibliográficos
Autores principales: Gobbel, Glenn T., Matheny, Michael E., Reeves, Ruth R., Akeroyd, Julia M., Turchin, Alexander, Ballantyne, Christie M., Petersen, Laura A., Virani, Salim S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Original Research Contribution
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8671496/
https://www.ncbi.nlm.nih.gov/pubmed/34950914
http://dx.doi.org/10.1016/j.ajpc.2021.100300

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8671496/
https://www.ncbi.nlm.nih.gov/pubmed/34950914
http://dx.doi.org/10.1016/j.ajpc.2021.100300

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