Predicting self-intercepted medication ordering errors using machine learning

Current approaches to understanding medication ordering errors rely on relatively small manually captured error samples. These approaches are resource-intensive, do not scale for computerized provider order entry (CPOE) systems, and are likely to miss important risk factors associated with medicatio...

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Detalles Bibliográficos
Autores principales: King, Christopher Ryan, Abraham, Joanna, Fritz, Bradley A., Cui, Zhicheng, Galanter, William, Chen, Yixin, Kannampallil, Thomas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8279397/
https://www.ncbi.nlm.nih.gov/pubmed/34260662
http://dx.doi.org/10.1371/journal.pone.0254358

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8279397/
https://www.ncbi.nlm.nih.gov/pubmed/34260662
http://dx.doi.org/10.1371/journal.pone.0254358

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