UMLF: a unified medical lexicon for French

Zweigenbaum, Pierre; Baud, Robert H.; Burgun, Anita; Namer, Fiammetta; Jarrousse, Éric; Grabar, Natalia; Ruch, Patrick; Duff, F. Le; Forget, Jean-François; Douyère, Magaly; Darmoni, Stéfan Jacques

doi:10.1016/j.ijmedinf.2004.03.010

Cited by 35 publications

(26 citation statements)

References 4 publications

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“…This method applied an exact-match strategy based on a French UMLS dictionary and a lexicon derived from small samples of previously manually annotated documents. The pre-annotation process consisted of the following steps: sentence segmentation and tokenization, lemmatization with the French lemmatizer Flemm (Namer 2004), generation of spelling and derivational variants (using the Unified Medical Lexicon for French, UMLF (Zweigenbaum et al 2005)), application of regular expressions to detect measurements (e.g., 3 cm) and durations (e.g., 2 weeks), and matching with the two lexicons. This matching was first applied to the original token and then to the lemma and variants when no match was found.…”

Section: Pre-annotation Methodsmentioning

confidence: 99%

A French clinical corpus with comprehensive semantic annotations: development of the Medical Entity and Relation LIMSI annOtated Text corpus (MERLOT)

Campillos

Deléger

Grouin

et al. 2017

Lang Resources & Evaluation

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Quality annotated resources are essential for Natural Language Processing. The objective of this work is to present a corpus of clinical narratives in French annotated for linguistic, semantic and structural information, aimed at clinical information extraction. Six annotators contributed to the corpus annotation, using a comprehensive annotation scheme covering 21 entities, 11 attributes and 37 relations. All annotators trained on a small, common portion of the corpus before proceeding independently. An automatic tool was used to produce entity and attribute pre-annotations. About a tenth of the corpus was doubly annotated and annotation differences were resolved in consensus meetings. To ensure annotation consistency throughout the corpus, we devised harmonization tools to automatically identify annotation differences to be addressed to improve the overall corpus quality. The annotation project spanned over 24 months and resulted in a corpus comprising 500 documents (148,476 tokens) annotated with 44,740 entities and 26,478 relations. The average inter-annotator agreement is 0.793 F-measure for entities and 0.789 for relations. The performance of the pre-annotation tool for entities reached 0.814 F-measure when sufficient training data was available. The performance of our entity pre-annotation tool shows the value of the corpus to build and evaluate information extraction methods. In addition, we introduced harmonization methods that further improved the quality of annotations in the corpus.Electronic supplementary material The online version of this article

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Section: Pre-annotation Methodsmentioning

confidence: 99%

A French clinical corpus with comprehensive semantic annotations: development of the Medical Entity and Relation LIMSI annOtated Text corpus (MERLOT)

Campillos

Deléger

Grouin

et al. 2017

Lang Resources & Evaluation

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“…Zweigenbaum et al . [22] worked on a project to pool lexical resources scattered among several sources in a unified medical lexicon for French (UMLF). Namer [23] described a method which enables neoclassical compound nouns and adjectives of a biomedical specialized corpus to be automatically related by synonymy, hyponymy and approximation links.…”

Section: Introductionmentioning

confidence: 99%

Natural language processing of radiology reports for the detection of thromboembolic diseases and clinically relevant incidental findings

et al. 2014

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BackgroundNatural Language Processing (NLP) has been shown effective to analyze the content of radiology reports and identify diagnosis or patient characteristics. We evaluate the combination of NLP and machine learning to detect thromboembolic disease diagnosis and incidental clinically relevant findings from angiography and venography reports written in French. We model thromboembolic diagnosis and incidental findings as a set of concepts, modalities and relations between concepts that can be used as features by a supervised machine learning algorithm. A corpus of 573 radiology reports was de-identified and manually annotated with the support of NLP tools by a physician for relevant concepts, modalities and relations. A machine learning classifier was trained on the dataset interpreted by a physician for diagnosis of deep-vein thrombosis, pulmonary embolism and clinically relevant incidental findings. Decision models accounted for the imbalanced nature of the data and exploited the structure of the reports.ResultsThe best model achieved an F measure of 0.98 for pulmonary embolism identification, 1.00 for deep vein thrombosis, and 0.80 for incidental clinically relevant findings. The use of concepts, modalities and relations improved performances in all cases.ConclusionsThis study demonstrates the benefits of developing an automated method to identify medical concepts, modality and relations from radiology reports in French. An end-to-end automatic system for annotation and classification which could be applied to other radiology reports databases would be valuable for epidemiological surveillance, performance monitoring, and accreditation in French hospitals.

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“…), the OCR quality of the OCRed documents decreases substantially for the older documents. In a test set of 100 randomly selected documents from the corpus, we found that 16.4% of the words 3 did not appear in the Unified Medical Lexicon for French (Zweigenbaum et al, 2005), a word list with specific technical terms. Of these 16.4%, 3.8% pertained to words that were domain-specific terms that has been correctly identified in the OCR process but which did not feature in the UMLF, and 10.8% were words which contained at least one OCR error.…”

Section: Ocr Quality In Corpusmentioning

confidence: 97%

Proceedings of the Seventh International Workshop on Health Text Mining and Information Analysis

2016

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an interdisciplinary forum for researchers interested in automated processing of health documents. Health documents encompass electronic health records, clinical guidelines, spontaneous reports for pharmacovigilance, biomedical literature, health forums/blogs or any other type of health-related documents. The LOUHI workshop series fosters interactions between the Computational Linguistics, Medical Informatics and Artificial Intelligence communities. Following the six previous edition of the workshop which were co-located with SMBM 2008 in Turku, Finland, with NAACL 2010 in Los Angeles, California, with Artificial Intelligence in Medicine (AIME 2011) in Bled, Slovenia, during NICTA Techfest 2013 in Sydney, Australia, co-located with EACL 2014 in Gothenburg, Sweden, and with EMNLP 2015 in Lisbon, Portugal, this workshop is co-located this year with EMNLP 2016 in Austin, Texas.The aim of the LOUHI 2016 workshop is to bring together research work on topics related to health documents, particularly emphasizing multidisciplinary aspects of health documentation and the interplay between nursing and medical sciences, information systems, computational linguistics and computer science. The topics include, but are not limited to, the following Natural Language Processing techniques and related areas:• Techniques supporting information extraction, e.g. named entity recognition, negation and uncertainty detection• Classification and text mining applications (e.g. diagnostic classifications such as ICD-10 and nursing intensity scores) and problems (e.g. handling of unbalanced data sets)• Text representation, including dealing with data sparsity and dimensionality issues• Domain adaptation, e.g. adaptation of standard NLP tools (incl. tokenizers, PoS-taggers, etc) to the medical domain• Information fusion, i.e. integrating data from various sources, e.g. structured and narrative documentation• Unsupervised methods, including distributional semantics• Evaluation, gold/reference standard construction and annotation• Syntactic, semantic and pragmatic analysis of health documents• Anonymization/de-identification of health records and ethics• Supporting the development of medical terminologies and ontologies• Individualization of content, consumer health vocabularies, summarization and simplification of text• NLP for supporting documentation and decision making practices iii• Predictive modeling of adverse events, e.g. adverse drug events and hospital acquired infectionsThe call for papers encouraged authors to submit papers describing substantial and completed work but also focus on a contribution, a negative result, a software package or work in progress. We also encouraged to report work on low-resourced languages, addressing the challenges of data sparsity and language characteristic diversity.We received 21 submissions. Each submission went through a double-blind review process which involved three program committee members. Based on comments and rankings supplied by the reviewers, we accepted 13 papers. The overall acceptanc...

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UMLF: a unified medical lexicon for French

Cited by 35 publications

References 4 publications

A French clinical corpus with comprehensive semantic annotations: development of the Medical Entity and Relation LIMSI annOtated Text corpus (MERLOT)

A French clinical corpus with comprehensive semantic annotations: development of the Medical Entity and Relation LIMSI annOtated Text corpus (MERLOT)

Natural language processing of radiology reports for the detection of thromboembolic diseases and clinically relevant incidental findings

Proceedings of the Seventh International Workshop on Health Text Mining and Information Analysis

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