Translating the foundational model of anatomy into french using knowledge-based and lexical methods

Merabti, Tayeb; Soualmia, Lina Fatima; Grosjean, Julien; Palombi, Olivier; Müller, Jean-Michel; Darmoni, Stéfan Jacques

doi:10.1186/1472-6947-11-65

Cited by 13 publications

(7 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Section: Methodsmentioning

confidence: 99%

“…Lexicons for each concept type were compiled to help with the annotation task, with the following steps: Identification of core concepts: an initial set of terms related to thromboembolic disease and its related anatomy location was obtained by pooling French MeSH terms from the Anatomy category, available synonyms [ 28 ] and the French translation of the Foundational Model of Anatomy [ 29 ]. Lexicon enrichment: As the terms in literature-oriented terminologies often did not reflect the way the same concepts were referred to in clinical narratives, we needed to add clinically-oriented term to the lexicon.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2014

View full text Add to dashboard Cite

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.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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

et al. 2014

View full text Add to dashboard Cite

show abstract

“…One of the authors (SJD) has created exact match mappings between MeSH, OMIM, HPO and HRDO based on a natural language processing/conceptual based algorithm [ 17 , 18 ] suggestions. Exact match mapping means that the two concepts are real synonyms (e.g.…”

Section: Methodsmentioning

confidence: 99%

Searching for rare diseases in PubMed: a blind comparison of Orphanet expert query and query based on terminological knowledge

Griffon

Schuers

Dhombres

et al. 2016

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

BackgroundDespite international initiatives like Orphanet, it remains difficult to find up-to-date information about rare diseases. The aim of this study is to propose an exhaustive set of queries for PubMed based on terminological knowledge and to evaluate it versus the queries based on expertise provided by the most frequently used resource in Europe: Orphanet.MethodsFour rare disease terminologies (MeSH, OMIM, HPO and HRDO) were manually mapped to each other permitting the automatic creation of expended terminological queries for rare diseases. For 30 rare diseases, 30 citations retrieved by Orphanet expert query and/or query based on terminological knowledge were assessed for relevance by two independent reviewers unaware of the query’s origin. An adjudication procedure was used to resolve any discrepancy. Precision, relative recall and F-measure were all computed.ResultsFor each Orphanet rare disease (n = 8982), there was a corresponding terminological query, in contrast with only 2284 queries provided by Orphanet. Only 553 citations were evaluated due to queries with 0 or only a few hits. There were no significant differences between the Orpha query and terminological query in terms of precision, respectively 0.61 vs 0.52 (p = 0.13). Nevertheless, terminological queries retrieved more citations more often than Orpha queries (0.57 vs. 0.33; p = 0.01). Interestingly, Orpha queries seemed to retrieve older citations than terminological queries (p < 0.0001).ConclusionThe terminological queries proposed in this study are now currently available for all rare diseases. They may be a useful tool for both precision or recall oriented literature search.Electronic supplementary materialThe online version of this article (doi:10.1186/s12911-016-0333-0) contains supplementary material, which is available to authorized users.

show abstract

“…The 'Foundational Model of Anatomy' (FMA), a noteworthy example, is a coherently structured electronic database that integrates several anatomy 'ontologies' (i.e., anatomical nomenclatures that individually pertain to a specialised domain), such as the TA and NeuroNames (Rickard et al, 2004;Rosse and Mejino, 2008;Merabti et al, 2011). Fundamentally, the FMA ontology (nomenclature) is designed to comprehensively link its constituent ontologies with each other, along with other on-line resources (e.g., anatomical literature, anatomical atlases, medical dictionaries, multilingual translation systems), and other large-scale application domains (e.g., MeSH, READ codes, ICD codes, SNOMED CT) 3 that use machineunderstandable anatomical terminology to expedite information retrieval and health care management.…”

Section: Eponymmentioning

confidence: 99%

Fascial eponyms may help elucidate terminological and nomenclatural development

Adstrum

2015

Journal of Bodywork and Movement Therapies

View full text Add to dashboard Cite

Translating the foundational model of anatomy into french using knowledge-based and lexical methods

Cited by 13 publications

References 23 publications

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

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

Searching for rare diseases in PubMed: a blind comparison of Orphanet expert query and query based on terminological knowledge

Fascial eponyms may help elucidate terminological and nomenclatural development

Contact Info

Product

Resources

About