CAS: corpus of clinical cases in French

Grabar, Natalia; Dalloux, Clément; Claveau, Vincent

doi:10.1186/s13326-020-00225-x

Cited by 10 publications

(12 citation statements)

References 30 publications

(26 reference statements)

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“…The evaluation is done on 30 clinical cases (11,864 word occurrences and 755 sentences) from different specialties, such as provided by the CAS corpus [15]. The evaluation is done manually, since the automatic metrics of the simplification are not representative of the obtained quality and are often criticized [16].…”

Section: Discussionmentioning

confidence: 99%

Fine-Grained Simplification of Medical Documents

Koptient

Londres

Grabar

2021

Studies in Health Technology and Informatics

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Easy access to medical and health information for children, foreigners and patients is an important issue for the modern society and research. Indeed, misunderstanding of medical and health information by patients may have a negative impact on their healthcare process and health. Even if several simplification guidelines exist, they are difficult to use by medical experts (i.e. lack of time, difficulty to respect the criteria). Existing simplification systems mainly address some lexical or syntactic transformations. We propose to combine lexical and syntactic simplifications within one rule-based system and to make the process fine-grained thanks to a better control of the grammaticality of sentences.

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Section: Discussionmentioning

confidence: 99%

Fine-Grained Simplification of Medical Documents

Koptient

Londres

Grabar

2021

Studies in Health Technology and Informatics

Self Cite

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“…Before 2020, there were nine LoE resources out of a total of 19 publicly available negation-annotated corpora [73,74]. However, only two languages were annotated for the medical domain: the IULA Spanish Clinical Record Corpus [75] and the two corpora of clinical texts in French [27,76]. Four additional corpora have been released since then: a cancer dataset [77] and a clinical corpus of negation and uncertainty [78] in Spanish, a multipurpose clinical corpus in Brazilian Portuguese [79], and a German corpus of discharge summaries [26] (Table 1).…”

Section: Recent Advances In Negation Resolution For Loementioning

confidence: 99%

“…Despite the advancement of transfer learning for negation detection [76,79,80], rule-based [27] and supervised machine learning approaches [76,77,[81][82][83] for LoE continue to be researched and employed. One paper presented a corpus-free approach, which is an attractive prospect in a scenario where there is no annotated data [84].…”

Section: Recent Advances In Negation Resolution For Loementioning

confidence: 99%

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

Shaitarova,

Zaghir,

Lavelli

et al. 2023

Yearb Med Inform

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Objectives: This survey aims to provide an overview of the current state of biomedical and clinical Natural Language Processing (NLP) research and practice in Languages other than English (LoE). We pay special attention to data resources, language models, and popular NLP downstream tasks. Methods: We explore the literature on clinical and biomedical NLP from the years 2020-2022, focusing on the challenges of multilinguality and LoE. We query online databases and manually select relevant publications. We also use recent NLP review papers to identify the possible information lacunae. Results: Our work confirms the recent trend towards the use of transformer-based language models for a variety of NLP tasks in medical domains. In addition, there has been an increase in the availability of annotated datasets for clinical NLP in LoE, particularly in European languages such as Spanish, German and French. Common NLP tasks addressed in medical NLP research in LoE include information extraction, named entity recognition, normalization, linking, and negation detection. However, there is still a need for the development of annotated datasets and models specifically tailored to the unique characteristics and challenges of medical text in some of these languages, especially low-resources ones. Lastly, this survey highlights the progress of medical NLP in LoE, and helps at identifying opportunities for future research and development in this field.

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“…In the clinical domain, BNER has gained even more attention since some annotated datasets have become available in challenges such as ShARe/CLEF eHealth Evaluation Lab 2013 (Mowery et al 2013), BC5CDR (Li et al 2016), and n2c2 (Henry et al 2019). In relation to annotated datasets for languages other than English, it is worth mentioning the CAS corpus of clinical cases annotated in French (Grabar, Dalloux, and Claveau 2020) and the multilingual E3C corpus (Magnini et al 2020(Magnini et al , 2021, which is the subject of this study.…”

Section: Related Workmentioning

confidence: 99%

Assessment of the E3C corpus for the recognition of disorders in clinical texts

Zanoli,

Lavelli,

Verdi do Amarante

et al. 2023

Nat. Lang. Eng.

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Disorder named entity recognition (DNER) is a fundamental task of biomedical natural language processing, which has attracted plenty of attention. This task consists in extracting named entities of disorders such as diseases, symptoms, and pathological functions from unstructured text. The European Clinical Case Corpus (E3C) is a freely available multilingual corpus (English, French, Italian, Spanish, and Basque) of semantically annotated clinical case texts. The entities of type disorder in the clinical cases are annotated at both mention and concept level. At mention -level, the annotation identifies the entity text spans, for example, abdominal pain. At concept level, the entity text spans are associated with their concept identifiers in Unified Medical Language System, for example, C0000737. This corpus can be exploited as a benchmark for training and assessing information extraction systems. Within the context of the present work, multiple experiments have been conducted in order to test the appropriateness of the mention-level annotation of the E3C corpus for training DNER models. In these experiments, traditional machine learning models like conditional random fields and more recent multilingual pre-trained models based on deep learning were compared with standard baselines. With regard to the multilingual pre-trained models, they were fine-tuned (i) on each language of the corpus to test per-language performance, (ii) on all languages to test multilingual learning, and (iii) on all languages except the target language to test cross-lingual transfer learning. Results show the appropriateness of the E3C corpus for training a system capable of mining disorder entities from clinical case texts. Researchers can use these results as the baselines for this corpus to compare their own models. The implemented models have been made available through the European Language Grid platform for quick and easy access.

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CAS: corpus of clinical cases in French

Cited by 10 publications

References 30 publications

Fine-Grained Simplification of Medical Documents

Fine-Grained Simplification of Medical Documents

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

Assessment of the E3C corpus for the recognition of disorders in clinical texts

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