Automated detection of off-label drug use.
Off-label drug use, defined as use of a drug in a manner that deviates from its approved use defined by the drug's FDA label, is problematic because such uses have not been evaluated for safety and efficacy. Studies estimate that 21% of prescriptions are off-label, and only 27% of those have ev...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2014-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC3929699?pdf=render |
| _version_ | 1811194162389712896 |
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| author | Kenneth Jung Paea LePendu William S Chen Srinivasan V Iyer Ben Readhead Joel T Dudley Nigam H Shah |
| author_facet | Kenneth Jung Paea LePendu William S Chen Srinivasan V Iyer Ben Readhead Joel T Dudley Nigam H Shah |
| author_sort | Kenneth Jung |
| collection | DOAJ |
| description | Off-label drug use, defined as use of a drug in a manner that deviates from its approved use defined by the drug's FDA label, is problematic because such uses have not been evaluated for safety and efficacy. Studies estimate that 21% of prescriptions are off-label, and only 27% of those have evidence of safety and efficacy. We describe a data-mining approach for systematically identifying off-label usages using features derived from free text clinical notes and features extracted from two databases on known usage (Medi-Span and DrugBank). We trained a highly accurate predictive model that detects novel off-label uses among 1,602 unique drugs and 1,472 unique indications. We validated 403 predicted uses across independent data sources. Finally, we prioritize well-supported novel usages for further investigation on the basis of drug safety and cost. |
| first_indexed | 2024-04-12T00:21:18Z |
| format | Article |
| id | doaj.art-80dea625c4fb4e498a30e576c2453ec4 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-12T00:21:18Z |
| publishDate | 2014-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-80dea625c4fb4e498a30e576c2453ec42022-12-22T03:55:42ZengPublic Library of Science (PLoS)PLoS ONE1932-62032014-01-0192e8932410.1371/journal.pone.0089324Automated detection of off-label drug use.Kenneth JungPaea LePenduWilliam S ChenSrinivasan V IyerBen ReadheadJoel T DudleyNigam H ShahOff-label drug use, defined as use of a drug in a manner that deviates from its approved use defined by the drug's FDA label, is problematic because such uses have not been evaluated for safety and efficacy. Studies estimate that 21% of prescriptions are off-label, and only 27% of those have evidence of safety and efficacy. We describe a data-mining approach for systematically identifying off-label usages using features derived from free text clinical notes and features extracted from two databases on known usage (Medi-Span and DrugBank). We trained a highly accurate predictive model that detects novel off-label uses among 1,602 unique drugs and 1,472 unique indications. We validated 403 predicted uses across independent data sources. Finally, we prioritize well-supported novel usages for further investigation on the basis of drug safety and cost.http://europepmc.org/articles/PMC3929699?pdf=render |
| spellingShingle | Kenneth Jung Paea LePendu William S Chen Srinivasan V Iyer Ben Readhead Joel T Dudley Nigam H Shah Automated detection of off-label drug use. PLoS ONE |
| title | Automated detection of off-label drug use. |
| title_full | Automated detection of off-label drug use. |
| title_fullStr | Automated detection of off-label drug use. |
| title_full_unstemmed | Automated detection of off-label drug use. |
| title_short | Automated detection of off-label drug use. |
| title_sort | automated detection of off label drug use |
| url | http://europepmc.org/articles/PMC3929699?pdf=render |
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