ArticleApplied clinical informatics2015
A Preliminary Study of Clinical Abbreviation Disambiguation in Real Time.
Article in Applied clinical informatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Disambiguating Clinical Abbreviations by One-to-All Classification: Algorithm Development and Validation Study.JMIR medical informatics · 2024Article
- A case study in applying artificial intelligence-based named entity recognition to develop an automated ophthalmic disease registry.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2023Article
- Leveraging Active Learning for Failure Mode Acquisition.Sensors (Basel, Switzerland) · 2023Article
- Deciphering clinical abbreviations with a privacy protecting machine learning system.Nature communications · 2022Article
- A deep database of medical abbreviations and acronyms for natural language processing.Scientific data · 2021Article
- Complexities, variations, and errors of numbering within clinical notes: the potential impact on information extraction and cohort-identification.BMC medical informatics and decision making · 2019Article
- A method for harmonization of clinical abbreviation and acronym sense inventories.Journal of biomedical informatics · 2018Article
- Defining Phenotypes from Clinical Data to Drive Genomic Research.Annual review of biomedical data science · 2018Article
- A long journey to short abbreviations: developing an open-source framework for clinical abbreviation recognition and disambiguation (CARD).Journal of the American Medical Informatics Association : JAMIA · 2017Article
- Clinical Natural Language Processing in 2015: Leveraging the Variety of Texts of Clinical Interest.Yearbook of medical informatics · 2016Article
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Authors and funding
7 authors.
Funding
Abstract
objectiveTo save time, healthcare providers frequently use abbreviations while authoring clinical documents. Nevertheless, abbreviations that authors deem unambiguous often confuse other readers, including clinicians, patients, and natural language processing (NLP) systems. Most current clinical NLP systems "post-process" notes long after clinicians enter them into electronic health record systems (EHRs). Such post-processing cannot guarantee 100% accuracy in abbreviation identification and disambiguation, since multiple alternative interpretations exist.
methodsAuthors describe a prototype system for real-time Clinical Abbreviation Recognition and Disambiguation (rCARD) - i.e., a system that interacts with authors during note generation to verify correct abbreviation senses. The rCARD system design anticipates future integration with web-based clinical documentation systems to improve quality of healthcare records. When clinicians enter documents, rCARD will automatically recognize each abbreviation. For abbreviations with multiple possible senses, rCARD will show a ranked list of possible meanings with the best predicted sense at the top. The prototype application embodies three word sense disambiguation (WSD) methods to predict the correct senses of abbreviations. We then conducted three experments to evaluate rCARD, including 1) a performance evaluation of different WSD methods; 2) a time evaluation of real-time WSD methods; and 3) a user study of typing clinical sentences with abbreviations using rCARD.
resultsUsing 4,721 sentences containing 25 commonly observed, highly ambiguous clinical abbreviations, our evaluation showed that the best profile-based method implemented in rCARD achieved a reasonable WSD accuracy of 88.8% (comparable to SVM - 89.5%) and the cost of time for the different WSD methods are also acceptable (ranging from 0.630 to 1.649 milliseconds within the same network). The preliminary user study also showed that the extra time costs by rCARD were about 5% of total document entry time and users did not feel a significant delay when using rCARD for clinical document entry.
conclusionThe study indicates that it is feasible to integrate a real-time, NLP-enabled abbreviation recognition and disambiguation module with clinical documentation systems.
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