ArticleJMIR medical informatics2021
Clinical Term Normalization Using Learned Edit Patterns and Subconcept Matching: System Development and Evaluation.
Article in JMIR medical informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed, 12 citations in OpenAlex.
- Symptom Terminology Normalization in Traditional Chinese Medicine: Development and Evaluation of a 2-Stage Deep Learning Framework Based on Fine-Grained Semantic Classification.JMIR medical informatics · 2026Article
- End-to-end Chinese clinical event extraction based on large language model.Scientific reports · 2025Article
- Chemical identification and indexing in PubMed full-text articles using deep learning and heuristics.Database : the journal of biological databases and curation · 2022Article
- A simple neural vector space model for medical concept normalization using concept embeddings.Journal of biomedical informatics · 2022Article
- Stroke Outcome Measurements From Electronic Medical Records: Cross-sectional Study on the Effectiveness of Neural and Nonneural Classifiers.JMIR medical informatics · 2021Article
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Abstract
backgroundClinical terms mentioned in clinical text are often not in their standardized forms as listed in clinical terminologies because of linguistic and stylistic variations. However, many automated downstream applications require clinical terms mapped to their corresponding concepts in clinical terminologies, thus necessitating the task of clinical term normalization.
objectiveIn this paper, a system for clinical term normalization is presented that utilizes edit patterns to convert clinical terms into their normalized forms.
methodsThe edit patterns are automatically learned from the Unified Medical Language System (UMLS) Metathesaurus as well as from the given training data. The edit patterns are generalized sequences of edits that are derived from edit distance computations. The edit patterns are both character based as well as word based and are learned separately for different semantic types. In addition to these edit patterns, the system also normalizes clinical terms through the subconcepts mentioned within them.
resultsThe system was evaluated as part of the 2019 n2c2 Track 3 shared task of clinical term normalization. It obtained 80.79% accuracy on the standard test data. This paper includes ablation studies to evaluate the contributions of different components of the system. A challenging part of the task was disambiguation when a clinical term could be normalized to multiple concepts.
conclusionsThe learned edit patterns led the system to perform well on the normalization task. Given that the system is based on patterns, it is human interpretable and is also capable of giving insights about common variations of clinical terms mentioned in clinical text that are different from their standardized forms.
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