ArticlePloS one2014
Automated detection of off-label drug use.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Medication-indication knowledge bases: a systematic review and critical appraisal.Journal of the American Medical Informatics Association : JAMIA · 2015Pooled it
- Paediatric rare diseases: Can large language models assist off-label prescribing?British journal of clinical pharmacology · 2025Article
- Drug repurposing for glomerular diseases: an underutilized resource.Nature reviews. Nephrology · 2024Review
- Biological therapy for ocular Behçet's disease with off-label drug prescription in Turkey.European journal of hospital pharmacy : science and practice · 2023Review
- Hierarchical Concept Relations Improve Detection of Off-Label Drug Use in Electronic Health Records Data.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2023Article
- An updated, computable MEDication-Indication resource for biomedical research.Scientific reports · 2021Article
- Application of natural language processing techniques to identify off-label drug usage from various online health communities.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Building a best-in-class automated de-identification tool for electronic health records through ensemble learning.Patterns (New York, N.Y.) · 2021Article
- Article
- Repositioned Drugs for Inflammatory Diseases such as Sepsis, Asthma, and Atopic Dermatitis.Biomolecules & therapeutics · 2020Review
- Protected Health Information filter (Philter): accurately and securely de-identifying free-text clinical notes.NPJ digital medicine · 2020Article
- The use or generation of biomedical data and existing medicines to discover and establish new treatments for patients with rare diseases - recommendations of the IRDiRC Data Mining and Repurposing Task Force.Orphanet journal of rare diseases · 2019Article
- Profiling off-label prescriptions in cancer treatment using social health networks.JAMIA open · 2019Article
- Auto-Generated Physiological Chain Data for an Ontological Framework for Pharmacology and Mechanism of Action to Determine Suspected Drugs in Cases of Dysuria.Drug safety · 2019Article
- Developing a Data-driven Medication Indication Knowledge Base using a Large Scale Medical Claims Database.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2019Article
- PathFX provides mechanistic insights into drug efficacy and safety for regulatory review and therapeutic development.PLoS computational biology · 2018Article
- A scientist engineer's contribution to therapeutic discovery and development.Experimental biology and medicine (Maywood, N.J.) · 2018Article
- Review
- Capturing the Patient's Perspective: a Review of Advances in Natural Language Processing of Health-Related Text.Yearbook of medical informatics · 2017Review
- Synergistic drug combinations from electronic health records and gene expression.Journal of the American Medical Informatics Association : JAMIA · 2017Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
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.
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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.