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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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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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Abstract:
International audience ; Virtual patient software allows health professionals to practice their skills by interacting with tools simulating clinical scenarios. A natural language dialogue system can provide natural interaction for medical history taking. However, the large number of concepts and terms in the medical domain makes the creation of such a system a demanding task. We designed a dialogue system that stands out from current research by its ability to handle a wide variety of medical specialties and clinical cases. To address the task, we designed a patient record model, a knowledge model for the task, and a termino-ontological model that hosts structured thesauri with linguistic, terminological and ontological knowledge. We used a frame- and rule-based approach and terminology-rich resources to handle the medical dialogue. This work focuses on the termino-ontological model, the challenges involved and how the system manages resources for the French language. We adopted a comprehensive approach to collect terms and ontological knowledge, and dictionaries of affixes, synonyms and derivational variants. Resources include domain lists containing over 161,000 ters, and dictionaries with over 959,000 word/concept entries. We assessed our approach by having 71 participants (39 medical doctors and 32 non-medical evaluators) interact with the system and use 35 cases from 18 specialities. We conducted a quantitative evaluation of all components by analysing interaction logs (11,834turns). Natural language understanding achieved an F-measure of 95.8 per cent. Dialogue management provided on average 74.3 (±9.5) per cent of correct answers. We performed a qualitative evaluation by collecting 171 five-point Likert scale questionnaires. All evaluated aspects obtained mean scores above the Likert mid-scale point. We analysed the vocabulary coverage with regard to unseen cases: the system covered 97.8 per cent of theirterms. Evaluations showed that the system achieved high vocabulary coverage on unseen cases and was assessed as relevant for the task.
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Keyword:
[INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]; [INFO]Computer Science [cs]; dialogue system; terminology resources; virtual patient
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URL: https://hal.archives-ouvertes.fr/hal-02358021/file/pg_nle_final.pdf
https://hal.archives-ouvertes.fr/hal-02358021/document
https://hal.archives-ouvertes.fr/hal-02358021
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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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