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Lessons Learned from the Usability Evaluation of a Simulated Patient Dialogue System
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In: ISSN: 0148-5598 ; EISSN: 1573-689X ; Journal of Medical Systems ; https://hal.archives-ouvertes.fr/hal-03452553 ; Journal of Medical Systems, Springer Verlag (Germany), 2021, 45 (7), ⟨10.1007/s10916-021-01737-4⟩ (2021)
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A clinical trials corpus annotated with UMLS entities to enhance the access to evidence-based medicine
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A clinical trials corpus annotated with UMLS entities to enhance the access to evidence-based medicine
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In: BMC Med Inform Decis Mak (2021)
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Abstract:
BACKGROUND: The large volume of medical literature makes it difficult for healthcare professionals to keep abreast of the latest studies that support Evidence-Based Medicine. Natural language processing enhances the access to relevant information, and gold standard corpora are required to improve systems. To contribute with a new dataset for this domain, we collected the Clinical Trials for Evidence-Based Medicine in Spanish (CT-EBM-SP) corpus. METHODS: We annotated 1200 texts about clinical trials with entities from the Unified Medical Language System semantic groups: anatomy (ANAT), pharmacological and chemical substances (CHEM), pathologies (DISO), and lab tests, diagnostic or therapeutic procedures (PROC). We doubly annotated 10% of the corpus and measured inter-annotator agreement (IAA) using F-measure. As use case, we run medical entity recognition experiments with neural network models. RESULTS: This resource contains 500 abstracts of journal articles about clinical trials and 700 announcements of trial protocols (292 173 tokens). We annotated 46 699 entities (13.98% are nested entities). Regarding IAA agreement, we obtained an average F-measure of 85.65% (±4.79, strict match) and 93.94% (±3.31, relaxed match). In the use case experiments, we achieved recognition results ranging from 80.28% (±00.99) to 86.74% (±00.19) of average F-measure. CONCLUSIONS: Our results show that this resource is adequate for experiments with state-of-the-art approaches to biomedical named entity recognition. It is freely distributed at: http://www.lllf.uam.es/ESP/nlpmedterm_en.html. The methods are generalizable to other languages with similar available sources.
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Keyword:
Research Article
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URL: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7898014/
http://www.ncbi.nlm.nih.gov/pubmed/33618727
https://doi.org/10.1186/s12911-021-01395-z
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Designing a virtual patient dialogue system based on terminology-rich resources: Challenges and evaluation
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Designing a virtual patient dialogue system based on terminology-rich resources: challenges and evaluation
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In: ISSN: 1351-3249 ; EISSN: 1469-8110 ; Natural Language Engineering ; https://hal.archives-ouvertes.fr/hal-02358021 ; Natural Language Engineering, Cambridge University Press (CUP), 2019, pp.1-38 (2019)
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Biomedical Term Extraction: NLP Techniques in Computational Medicine
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Biomedical Term Extraction: NLP Techniques in Computational Medicine
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Collecting and POS-tagging a lexical resource of Japanese biomedical terms from a corpus ; Recogida y etiquetado morfológico de un lexicón de términos biomédicos en japonés a partir de corpus
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Errores léxicos en el español oral no nativo: análisis de la interlengua basado en corpus ; Lexical errors in non-native oral Spanish: a corpus-based error analysis
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Campillos Llanos, Leonardo. - : Universidad de Alicante. Departamento de Filología Española, Lingüística General y Teoría de la Literatura, 2014
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Collecting and POS-tagging a lexical resource of Japanese biomedical terms from a corpus ; Recogida y etiquetado morfológico de un lexicón de términos biomédicos en japonés a partir de corpus
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