Recherche d'information médicale pour le patient Impact de ressources terminologiques

National audienceABSTRACT. The right of patients to access their clinical health record is granted by the code of Santé Publique. Yet, this content remain difficult to understand. We propose an experience, in which we use queries defined by patients in order to find relevant documents. We utilise the Indri search engine, based on statistical language modeling and semantic resources. We stress the point related to the terminological variation (e.g. synonyms, abbreviations) to make the link between expert and patient languages. Various combinations of resources and Indri settings are explored, mostly based on query expansion. Our system shows up to 0.7660 P@10 and up to 0.6793 [email protected]ÉSUMÉ. Le droit d'accès au dossier clinique par les patients est inscrit dans le code de Santé Publique. Cependant, ce contenu reste difficile à comprendre. Nous proposons une expérience, où les requêtes des patients sont utilisées pour retrouver les documents pertinents. Nous util-isons le moteur de recherche Indri, basé sur le modèle statistique de la langue, et des ressources sémantiques. L'accent est mis sur la variation terminologique (e.g. synonymes, abréviations) pour faire le lien entre la langue des experts et des patients. Différentes combinaisons de ressources et du paramétrage de Indri sont testées, essentiellement à travers l'expansion des requêtes. Notre système montre jusqu'à 0,7660 de P@10 et 0,6793 de NDCG@10

ShARe/CLEF eHealth evaluation lab 2014, task 3: user-centred health information retrieval

This paper presents the results of task 3 of the ShARe/CLEF eHealth Evaluation Lab 2014. This evaluation lab focuses on improving access to medical information on the web. The task objective was to investigate the effect of using additional information such as a related discharge summary and external resources such as medical ontologies on the IR effectiveness, in a monolingual and in a multilingual context. The participants were allowed to submit up to seven runs for each language, one mandatory run using no additional information or external resources, and three each using or not using discharge summaries

An enhanced concept based approach medical information retrieval to address readability, vocabulary and presentation issues

Querying of health information retrieval for health advice has now become a general and notable task performed by individuals on the Internet. However, the failure of the existing approaches to integrate program modules that would address the information needs of all categories of end-users remains. This study focused on proposing an improved framework and designing an enhanced concept based approach (ECBA) for medical information retrieval that would better address readability, vocabulary mismatched and presentation issues by generating medical discharge documents and medical search queries results in both medical expert and layman’s forms. Three special program modules were designed and integrated in the enhanced concept based approach namely: medical terms control module, vocabulary controlled module and readability module to specifically address the information needs of both medical experts and laymen end-users. Eight benched marked datasets namely: Medline, UMLS, MeSH, Metamap, Metathesaurus, Diagnosia 7, Khresmoi Project 6 and Genetic Home Reference were used in validating the systems performance. Additionally, the ECBA was compared using three existing approaches such as concept based approach (CBA), query likelihood model (QLM) and latent semantic indexing (LSI). The evaluation was conducted using the performance and statistical metrics: P@40, NDCG@40, MAP, Analysis of Variance (ANOVA) and Turkey HSD Tests. The outcome of the final experimental results obtained shows that, the ECBA consistently obtained above 93% accuracy rate results on Medline, UMLS and MeSH Datasets, 92% on Metamap, Metathesaurus and Diagnosia 7 datasets and 91% on Khresmoi Project 6 and Genetic Home Reference datasets. Also, the statistical analysis performance results obtained by each of the four approaches: ECBA, CBA, QLM and LSI shows that, there is a significant difference among their Mean Scores, hence, the null hypothesis of no significant difference was rejected