ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2013
Automated Detection of Systematic Off-label Drug Use in Free Text of Electronic Medical Records.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 16 citations in OpenAlex.
- Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review.JMIR medical informatics · 2023Review
- Machine and deep learning approaches for cancer drug repurposing.Seminars in cancer biology · 2021Review
- Repositioned Drugs for Inflammatory Diseases such as Sepsis, Asthma, and Atopic Dermatitis.Biomolecules & therapeutics · 2020Review
- Pathway-Based Drug-Repurposing Schemes in Cancer: The Role of Translational Bioinformatics.Frontiers in oncology · 2020Review
- Intelligent Word Embeddings of Free-Text Radiology Reports.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2017Article
- Analysis of medication and indication occurrences in clinical notes.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2014Article
- Automated detection of off-label drug use.PloS one · 2014Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Off-label use of a drug occurs when it is used in a manner that deviates from its FDA label. Studies estimate that 21% of prescriptions are off-label, with only 27% of those uses supported by evidence of safety and efficacy. We have developed methods to detect population level off-label usage using computationally efficient annotation of free text from clinical notes to generate features encoding empirical information about drug-disease mentions. By including additional features encoding prior knowledge about drugs, diseases, and known usage, we trained a highly accurate predictive model that was used to detect novel candidate off-label usages in a very large clinical corpus. We show that the candidate uses are plausible and can be prioritized for further analysis in terms of safety and efficacy.
Identifiers
24303308PMC3814472W104021040What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.