ArticleJournal of the American Medical Informatics Association : JAMIA2013
Extracting drug indication information from structured product labels using natural language processing.
Article in Journal of the American Medical Informatics Association : JAMIA, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
23 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
- Evaluating NLP Approaches to Extract Drug Indications.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
- Knowledge-guided generative artificial intelligence for automated taxonomy learning from drug labels.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Using electronic health records for clinical pharmacology research: Challenges and considerations.Clinical and translational science · 2024Review
- Article
- Safer prescription of drugs: impact of the PREFASEG system to aid clinical decision-making in primary care in Catalonia.BMC medical informatics and decision making · 2021Article
- An updated, computable MEDication-Indication resource for biomedical research.Scientific reports · 2021Article
- InContext: curation of medical context for drug indications.Journal of biomedical semantics · 2021Article
- DICE: A Drug Indication Classification and Encyclopedia for AI-Based Indication Extraction.Frontiers in artificial intelligence · 2021Article
- Information Extraction From FDA Drug Labeling to Enhance Product-Specific Guidance Assessment Using Natural Language Processing.Frontiers in research metrics and analytics · 2021Article
- Exploring Novel Computable Knowledge in Structured Drug Product Labels.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2020Article
- Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities.An international journal on information fusion · 2019Article
- Formalizing drug indications on the road to therapeutic intent.Journal of the American Medical Informatics Association : JAMIA · 2017Article
- A rule-based electronic phenotyping algorithm for detecting clinically relevant cardiovascular disease cases.BMC research notes · 2017Article
- Disease-drug database for pharmacogenomic-based prescribing.Clinical pharmacology and therapeutics · 2016Article
- Automatic Classification of Structured Product Labels for Pregnancy Risk Drug Categories, a Machine Learning Approach.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2015Article
- Scaling drug indication curation through crowdsourcing.Database : the journal of biological databases and curation · 2015Article
- LabeledIn: cataloging labeled indications for human drugs.Journal of biomedical informatics · 2014Article
- The National Institutes of Health's Biomedical Translational Research Information System (BTRIS): design, contents, functionality and experience to date.Journal of biomedical informatics · 2014Article
- Text mining for adverse drug events: the promise, challenges, and state of the art.Drug safety · 2014Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
objectiveTo extract drug indications from structured drug labels and represent the information using codes from standard medical terminologies. MATERIALS AND
methodsWe used MetaMap and other publicly available resources to extract information from the indications section of drug labels. Drugs and indications were encoded by RxNorm and UMLS identifiers respectively. A sample was manually reviewed. We also compared the results with two independent information sources: National Drug File-Reference Terminology and the Semantic Medline project.
resultsA total of 6797 drug labels were processed, resulting in 19 473 unique drug-indication pairs. Manual review of 298 most frequently prescribed drugs by seven physicians showed a recall of 0.95 and precision of 0.77. Inter-rater agreement (Fleiss κ) was 0.713. The precision of the subset of results corroborated by Semantic Medline extractions increased to 0.93. DISCUSSION: Correlation of a patient's medical problems and drugs in an electronic health record has been used to improve data quality and reduce medication errors. Authoritative drug indication information is available from drug labels, but not in a format readily usable by computer applications. Our study shows that it is feasible to use publicly available natural language processing resources to extract drug indications from drug labels. The same method can be applied to other sections of the drug label-for example, adverse effects, contraindications.
conclusionsIt is feasible to use publicly available natural language processing tools to extract indication information from freely available drug labels. Named entity recognition sources (eg, MetaMap) provide reasonable recall. Combination with other data sources provides higher precision.
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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.