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MedScan, a natural language processing engine for MEDLINE abstracts
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MOTIVATION: The importance of extracting biomedical information from scientific publications is well recognized. A number of information extraction systems for the biomedical domain have been reported, but none of them have become widely used in practical applications. Most proposals to date make rather simplistic assumptions about the syntactic aspect of natural language. There is an urgent need for a system that has broad coverage and performs well in real-text applications. RESULTS: We present a general biomedical domain-oriented NLP engine called MedScan that efficiently processes sentences from MEDLINE abstracts and produces a set of regularized logical structures representing the meaning of each sentence. The engine utilizes a specially developed context-free grammar and lexicon. Preliminary evaluation of the system's performance, accuracy, and coverage exhibited encouraging results. Further approaches for increasing the coverage and reducing parsing ambiguity of the engine, as well as its application for information extraction are discussed.
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Metadata
- Authors
- Novichkova, S., Egorov, S., & Daraselia, N.
- Affiliations
- Ariadne Diagnostics (United States) (US)
- Corresponding author
- Novichkova, Svetlana
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2003-09-01
- Volume
- 19
- Issue
- 13
- Pages
- 1699-1706
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2140601463
- Cited by
- 236
- References
- 18
- Citations by year
- 2026: 3 · 2025: 2 · 2024: 5 · 2023: 4 · 2022: 13 · 2021: 6 · 2020: 7 · 2019: 10 · 2018: 6 · 2017: 5 · 2016: 11 · 2015: 7 · 2014: 14 · 2013: 12 · 2012: 12
- FWCI
- 5.30
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Bioinformatics (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Biomedical Text Mining and Ontologies
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Text Analysis Techniques
- Keywords
- MEDLINE abstracts, biomedical information extraction, natural language processing, context-free grammars, vocabulary, logical structure
- MeSH terms
- Abstracting and Indexing, Artificial Intelligence, Database Management Systems, Natural Language Processing, Pattern Recognition, Automated, Periodicals as Topic, Subject Headings, MEDLINE, Information Storage and Retrieval, Vocabulary, Controlled
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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