ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2019
A Hybrid Normalization Method for Medical Concepts in Clinical Narrative using Semantic Matching.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- End-to-end pipeline for automated heart failure diagnosis with clinical notes using SNOMED-CT.Scientific reports · 2026Article
- Integration of multiple terminology bases: a multi-view alignment method using the hierarchical structure.Bioinformatics (Oxford, England) · 2023Article
- Query bot for retrieving patients' clinical history: A COVID-19 use-case.Journal of biomedical informatics · 2021Article
- Neural Machine Translation-Based Automated Current Procedural Terminology Classification System Using Procedure Text: Development and Validation Study.JMIR formative research · 2021Article
- Semantic Search for Large Scale Clinical Ontologies.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2021Article
- Unified Medical Language System resources improve sieve-based generation and Bidirectional Encoder Representations from Transformers (BERT)-based ranking for concept normalization.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Clinical concept normalization with a hybrid natural language processing system combining multilevel matching and machine learning ranking.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- The 2019 National Natural language processing (NLP) Clinical Challenges (n2c2)/Open Health NLP (OHNLP) shared task on clinical concept normalization for clinical records.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- RadLex Normalization in Radiology Reports.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2020Article
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Authors and funding
3 authors.
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Abstract
Normalization maps clinical terms in medical notes to standardized medical vocabularies. In order to capture semantic similarity between different surface expressions of the same clinical concept, we develop a hybrid normalization system that incorporates a deep learning model to complement the traditional dictionary lookup ap- proach. We evaluate our system against the ShARe/CLEF 2013 challenge data in which 30% of the mentions have no concept mapping. When evaluating against the mentions which may be normalized to existing concepts, our hybrid system achieves 90.6% accuracy, obtaining a statistically significant improvement of 2.6% over a strong edit-distance and dictionary lookup combined baseline. Our analysis of semantic similarity between concepts and mentions reveals existing inconsistencies in ShARe/CLEF data, as well as problematic ambiguities in the UMLS. Our results suggest the potential of the proposed deep learning approach to further improve the performance of normalization by utilizing semantic similarity.
Identifiers
31259030PMC6568138What 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.