Translating medical terminology and bilingual terminography

Peters, Pam; Yan, Qi; Ding, Jun

doi:10.1007/s40607-018-0037-y

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…Through knowledge engineering, accurate and complete medical knowledge bases can be established to promote the popularization of medical knowledge among the population [90]- [93], [101]- [104], [186]. Specifically, people can easily access medical knowledge through question answering systems [187], [188], information retrieval systems [83], [85], and machine translation systems [1], [134], [189], [190], facilitating the popularization and education of medical knowledge. In addition, text generation techniques, such as question generation and text summarization, can also be used in medical education to generate medical case-based questions [191] and construct simplified summaries [65].…”

Section: Public Healthmentioning

confidence: 99%

Natural Language Processing for Smart Healthcare

Zhou¹,

Yang²,

Shi³

et al. 2021

Preprint

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Smart healthcare has achieved significant progress in recent years. Emerging artificial intelligence (AI) technologies enable various smart applications across various healthcare scenarios. As an essential technology powered by AI, natural language processing (NLP) plays a key role in smart healthcare due to its capability of analysing and understanding human language. In this work we review existing studies that concern NLP for smart healthcare from the perspectives of technique and application. We focus on feature extraction and modelling for various NLP tasks encountered in smart healthcare from a technical point of view. In the context of smart healthcare applications employing NLP techniques, the elaboration largely attends to representative smart healthcare scenarios, including clinical practice, hospital management, personal care, public health, and drug development. We further discuss the limitations of current works and identify the directions for future works.

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Section: Public Healthmentioning

confidence: 99%

Natural Language Processing for Smart Healthcare

Zhou¹,

Yang²,

Shi³

et al. 2021

Preprint

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Methodology for the standardization of terminological resources

Vezzani

Nunzio

2020

TERM

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Terminology standardization reflects two different aspects involving the meaning of terms and the structure of terminological resources. In this paper, we focus on the structural aspect of standardization and we present the work of re-modeling TriMED, a multilingual terminological database conceived to support multi-register medical communication. In particular, we provide a general methodology to make the termbase compliant to three of the most recent ISO/TC 37 standards. We focus on the definition of (i) the structural meta-model of the resource, (ii) the provided data categories and its Data Category Repository, and (iii) the TBX format for its implementation. In particular, we provide a general methodology to make the termbase compliant to three of the most recent ISO/TC 37 standards. We focus on the definition of (i) the structural meta-model of the resource, (ii) the provided data categories and its Data Category Repository, and (iii) the TBX format for its implementation.

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