ArticleJournal of the American Medical Informatics Association : JAMIA2017
A long journey to short abbreviations: developing an open-source framework for clinical abbreviation recognition and disambiguation (CARD).
Article in Journal of the American Medical Informatics Association : JAMIA, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
28 citing papers in PubMed.
- Clinical Terminology Mapping Service Based on Information Retrieval.Methods of information in medicine · 2025Article
- Analyzing the Creation and Use of Abbreviations in Cardiology and Cardiac Imaging Society Guidelines.JACC. Advances · 2025Article
- Deciphering Abbreviations in Malaysian Clinical Notes Using Machine Learning.Methods of information in medicine · 2024Article
- Disambiguating Clinical Abbreviations by One-to-All Classification: Algorithm Development and Validation Study.JMIR medical informatics · 2024Article
- Processing of Short-Form Content in Clinical Narratives: Systematic Scoping Review.Journal of medical Internet research · 2024Article
- Identifying Medical Concepts and Semantic Types in Lay Vocabularies of Health Consumers Who are Concerned with Diabetes on Social Media Using the UMLS and NLP.Proceedings : Annual International Computer Software and Applications Conference. COMPSAC · 2024Article
- Clinical Note Structural Knowledge Improves Word Sense Disambiguation.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2024Article
- TheAnnual review of biomedical data science · 2023Review
- Deciphering clinical abbreviations with a privacy protecting machine learning system.Nature communications · 2022Article
- Artificial Intelligence Assesses Clinicians' Adherence to Asthma Guidelines Using Electronic Health Records.The journal of allergy and clinical immunology. In practice · 2022Article
- Automatically disambiguating medical acronyms with ontology-aware deep learning.Nature communications · 2021Article
- A deep database of medical abbreviations and acronyms for natural language processing.Scientific data · 2021Article
- Article
- Ontology-driven weak supervision for clinical entity classification in electronic health records.Nature communications · 2021Article
- Automated Coding of Under-Studied Medical Concept Domains: Linking Physical Activity Reports to the International Classification of Functioning, Disability, and Health.Frontiers in digital health · 2021Article
- Ambiguity in medical concept normalization: An analysis of types and coverage in electronic health record datasets.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Natural language processing (NLP) tools in extracting biomedical concepts from research articles: a case study on autism spectrum disorder.BMC medical informatics and decision making · 2020Article
- Clinical Text Data in Machine Learning: Systematic Review.JMIR medical informatics · 2020Review
- Article
- Efficient Reuse of Natural Language Processing Models for Phenotype-Mention Identification in Free-text Electronic Medical Records: A Phenotype Embedding Approach.JMIR medical informatics · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
objectiveThe goal of this study was to develop a practical framework for recognizing and disambiguating clinical abbreviations, thereby improving current clinical natural language processing (NLP) systems' capability to handle abbreviations in clinical narratives.
methodsWe developed an open-source framework for clinical abbreviation recognition and disambiguation (CARD) that leverages our previously developed methods, including: (1) machine learning based approaches to recognize abbreviations from a clinical corpus, (2) clustering-based semiautomated methods to generate possible senses of abbreviations, and (3) profile-based word sense disambiguation methods for clinical abbreviations. We applied CARD to clinical corpora from Vanderbilt University Medical Center (VUMC) and generated 2 comprehensive sense inventories for abbreviations in discharge summaries and clinic visit notes. Furthermore, we developed a wrapper that integrates CARD with MetaMap, a widely used general clinical NLP system. RESULTS AND
conclusionCARD detected 27 317 and 107 303 distinct abbreviations from discharge summaries and clinic visit notes, respectively. Two sense inventories were constructed for the 1000 most frequent abbreviations in these 2 corpora. Using the sense inventories created from discharge summaries, CARD achieved an F1 score of 0.755 for identifying and disambiguating all abbreviations in a corpus from the VUMC discharge summaries, which is superior to MetaMap and Apache's clinical Text Analysis Knowledge Extraction System (cTAKES). Using additional external corpora, we also demonstrated that the MetaMap-CARD wrapper improved MetaMap's performance in recognizing disorder entities in clinical notes. The CARD framework, 2 sense inventories, and the wrapper for MetaMap are publicly available at https://sbmi.uth.edu/ccb/resources/abbreviation.htm . We believe the CARD framework can be a valuable resource for improving abbreviation identification in clinical NLP systems.
Indexed as
Identifiers
What 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.