MCN: A comprehensive corpus for medical concept normalization
Open accessAbstract
Normalization of clinical text involves linking different ways of talking about the same clinical concept to the same term in the standardized vocabulary. To date, very few annotated corpora for normalization have been available, and existing corpora so far have been limited in scope and only dealt with the normalization of diseases and disorders. In this paper, we describe the annotation methodology we developed in order to create a new manually annotated wide-coverage corpus for clinical concept normalization, the Medical Concept Normalization (MCN) corpus. In order to ensure wider coverage, we applied normalization to the text spans corresponding to the medical problems, treatments, and tests in the named entity corpus released for the fourth i2b2/VA shared task. In contrast to previous annotation efforts, we do not assign multiple concept labels to the named entities that do not map to a unique concept in the controlled vocabulary. Nor do we leave that named entity without a concept label. Instead, our normalization method that splits such named entities, resolving some of the core ambiguity issues. Lastly, we supply a sieve-based normalization baseline for MCN which combines MetaMap with multiple exact match components. The resulting corpus consists of 100 discharge summaries and provides normalization for the total of 10,919 concept mentions, using 3792 unique concepts from two controlled vocabularies. Our inter-annotator agreement is 67.69% pre-adjudication and 74.20% post-adjudication. Our sieve-based normalization baseline for MCN achieves 77% accuracy in cross-validation. We also detail the challenges of creating a normalization corpus, including the limitations deriving from both the mention span selection and the ambiguity and inconsistency within the current standardized terminologies. In order to facilitate the development of improved concept normalization methods, the MCN corpus will be publicly released to the research community in a shared task in 2019.
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When provided with a correct text span, simple heuristic methods can achieve 77% label accuracy.
“On their clinical text corpus, Luo et al. showed that when provided with a correct text span, simple heuristic methods can achieve 77% label accuracy; the highest accuracy yet achieved on their data set is 85% [28].”
From Assessing the Impact of Automated Suggestions on Decision Making: Domain Experts Mediate Model Errors but Take Less Initiative (2021) · cites this paper as [28]
The highest accuracy yet achieved on Luo et al.'s data set is 85%.
“On their clinical text corpus, Luo et al. showed that when provided with a correct text span, simple heuristic methods can achieve 77% label accuracy; the highest accuracy yet achieved on their data set is 85% [28].”
From Assessing the Impact of Automated Suggestions on Decision Making: Domain Experts Mediate Model Errors but Take Less Initiative (2021) · cites this paper as [28]
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- Authors
- Luo, Y.-F., Sun, W., & Rumshisky, A.
- Affiliations
- University of Massachusetts Lowell (US)
- Nuance Communications (United States) (US)
- Corresponding author
- Luo, Yen-Fu
- ORCID
- Sun, Weiyi
- Venue
- Journal of Biomedical Informatics
- Publisher
- Elsevier BV
- Published
- 2019-04
- Volume
- 92
- Pages
- 103132
- ISSN
- 1532-0464 (ISSN-L)
- OpenAlex
- W2917968119
- Cited by
- 62
- References
- 29
- Citations by year
- 2026: 5 · 2025: 6 · 2024: 6 · 2023: 8 · 2022: 8 · 2021: 10 · 2020: 17 · 2019: 2
- FWCI
- 3.07
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of Biomedical Informatics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Philips HealthCare
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Medical Coding and Health Information
- Keywords
- annotated corpus, discharge summaries, named entity resolution, controlled vocabulary, MetaMap, inter-annotator agreement, terminological ambiguity, shared task
- MeSH terms
- Humans, Natural Language Processing, Information Storage and Retrieval, Vocabulary, Controlled, Electronic Health Records, Data Curation
About these checks
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