Predicting self-intercepted medication ordering errors using machine learning
Predicting self-intercepted medication ordering errors using machine learning
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Publisher
United States: Public Library of Science
Journal title
Language
English
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Publisher
United States: Public Library of Science
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Scope and Contents
Contents
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 medication ordering errors. Previously, we described a data...
Alternative Titles
Full title
Predicting self-intercepted medication ordering errors using machine learning
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Record Identifier
TN_cdi_plos_journals_2551563444
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_plos_journals_2551563444
Other Identifiers
ISSN
1932-6203
E-ISSN
1932-6203
DOI
10.1371/journal.pone.0254358
