Exploring the Latest Highlights in Medical Natural Language Processing across Multiple Languages: A Survey

Shaitarova, Anastassia; Zaghir, Jamil; Lavelli, Alberto; Krauthammer, Michael; Rinaldi, Fabio

doi:10.1055/s-0043-1768726

Cited by 3 publications

(1 citation statement)

References 84 publications

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“…The survey paper in the Natural Language Processing (NLP) section by Shaitarova et al summarizes recent work (2020-2022) carried out in this area to process non-English biomedical texts [30]. The authors presented numerous annotated datasets and various NLP tasks, with a strong emphasis on the use of pre-trained language models for these tasks.…”

Section: One Health and Medical Informaticsmentioning

confidence: 99%

Informatics for One Health

Hollis,

Mougin,

Soualmia

2023

Yearb Med Inform

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Objectives: To introduce the 2023 International Medical Informatics Association (IMIA) Yearbook by the editors. Methods: The editorial provides an introduction and overview to the 2023 IMIA Yearbook where the special topic is “Informatics for One Health”. The special topic, survey papers and some best papers are discussed. The section changes in the Yearbook editorial committee are also described. Results: IMIA Yearbook 2023 provides many perspectives on a relatively new topic called “One Digital Health”. The subject is vast, and includes the use of digital technologies to promote the well-being of people and animals, but also of the environment in which they evolve. Many sections produced new work in the topic including One Health and all sections included the latest themes in many specialties in medical informatics. Conclusions: The theme of “Informatics for One Health” is relatively new but the editors of the IMIA Yearbook have presented excellent and thought-provoking work for biomedical informatics in 2023.

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Section: One Health and Medical Informaticsmentioning

confidence: 99%

Informatics for One Health

Hollis,

Mougin,

Soualmia

2023

Yearb Med Inform

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Large Language Models to process, analyze, and synthesize biomedical texts – a scoping review

Doneva,

Qin,

Sick

et al. 2024

Preprint

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The advent of large language models (LLMs) such as BERT and, more recently, GPT, is transforming our approach of analyzing and understanding biomedical texts. To stay informed about the latest advancements in this area, there is a need for up-to-date summaries on the impact of LLM on Natural Language Processing (NLP) in the biomedical field. Thus, this scoping review aims to provide a detailed overview of the current state of biomedical NLP research and its applications, with a special focus on the evolving role of LLMs. We conducted a systematic search of PubMed, EMBASE, and Google Scholar for studies and conference proceedings published from 2017 to December 19, 2023, that develop or utilize LLMs for NLP tasks in biomedicine. From 13,823 references, we selected 199 publications and conference proceedings for our review. LLMs are being applied to a wide array of tasks in the biomedical field, including knowledge management, text mining, drug discovery, and evidence synthesis. Prominent among these tasks are text classification, relation extraction, and named entity recognition. Although BERT-based models remain prevalent, the use of GPT-based models has substantially increased since 2023.

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Exploring the Potential of Neural Machine Translation for Cross-Language Clinical Natural Language Processing (NLP) Resource Generation through Annotation Projection

Rodríguez-Miret,

Farré-Maduell,

Lima-López

et al. 2024

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Recent advancements in neural machine translation (NMT) offer promising potential for generating cross-language clinical natural language processing (NLP) resources. There is a pressing need to be able to foster the development of clinical NLP tools that extract key clinical entities in a comparable way for a multitude of medical application scenarios that are hindered by lack of multilingual annotated data. This study explores the efficacy of using NMT and annotation projection techniques with expert-in-the-loop validation to develop named entity recognition (NER) systems for an under-resourced target language (Catalan) by leveraging Spanish clinical corpora annotated by domain experts. We employed a state-of-the-art NMT system to translate three clinical case corpora. The translated annotations were then projected onto the target language texts and subsequently validated and corrected by clinical domain experts. The efficacy of the resulting NER systems was evaluated against manually annotated test sets in the target language. Our findings indicate that this approach not only facilitates the generation of high-quality training data for the target language (Catalan) but also demonstrates the potential to extend this methodology to other languages, thereby enhancing multilingual clinical NLP resource development. The generated corpora and components are publicly accessible, potentially providing a valuable resource for further research and application in multilingual clinical settings.

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Exploring the Latest Highlights in Medical Natural Language Processing across Multiple Languages: A Survey

Cited by 3 publications

References 84 publications

Informatics for One Health

Informatics for One Health

Large Language Models to process, analyze, and synthesize biomedical texts – a scoping review

Exploring the Potential of Neural Machine Translation for Cross-Language Clinical Natural Language Processing (NLP) Resource Generation through Annotation Projection

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