ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2019
Evaluating the Impact of Dictionary Updates on Automatic Annotations Based on Clinical NLP Systems.
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 5 papers.
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5 citing papers in PubMed.
- I-NOWJ: adaptive context selection and knowledge-guided learning for substance use named entity recognition.Database : the journal of biological databases and curation · 2026Article
- Evaluating Large Language Models in extracting cognitive exam dates and scores.PLOS digital health · 2024Article
- Evaluating Large Language Models in Extracting Cognitive Exam Dates and Scores.medRxiv : the preprint server for health sciences · 2024Article
- TAX-Corpus: Taxonomy based Annotations for Colonoscopy Evaluation.Biomedical engineering systems and technologies, international joint conference, BIOSTEC ... revised selected papers. BIOSTEC (Conference) · 2022Article
- The h-ANN Model: Comprehensive Colonoscopy Concept Compilation Using Combined Contextual Embeddings.Biomedical engineering systems and technologies, international joint conference, BIOSTEC ... revised selected papers. BIOSTEC (Conference) · 2022Article
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6 authors.
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Abstract
Concept encoding, which maps text spans to concepts in standard terminologies, is a critical component in clinical natural language processing (NLP) systems to allow semantic interoperability with other clinical applications. A majority of clinical NLP systems adopt dictionary or lexicon based approaches and the performance of concept encoding is often evaluated using a human created gold standard generated with reference to the most up-to-date standard terminologies available at the time of gold standard creation. With the advance of medical science, standard terminologies or dictionaries can evolve. However, it remains unknown whether the dictionary updates will impact the performance of concept encoding. In this study, we evaluated the annotation performance of two clinical NLP systems, cTAKES and MedXN based on updated dictionaries to gain further insights. Specifically, we compared the automatic annotation results with previously manually generated gold standards. The results of our study demonstrate the annotation changes based on dictionary updates in clinical NLP systems and that it is necessary to do temporal management for gold standards, which raises the need for appropriate terminology management tools for back version compatibility to update gold standards.
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31259028PMC6568114What OpenQuestion holds
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