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To select, present, and summarize the best papers in the field of Knowledge Representation and Management (KRM) published in 2019. A comprehensive and standardized review of the biomedical informatics literature was performed to select the most interesting papers of KRM published in 2019, based on PubMed and ISI Web Of Knowledge queries. Four best papers were selected among 1,189 publications retrieved, following the usual International Medical Informatics Association Yearbook reviewing process. In 2019, research areas covered by pre-selected papers were represented by the design of semantic resources (methods, visualization, curation) and the application of semantic representations for the integration/enrichment of biomedical data. Besides new ontologies and sound methodological guidance to rethink knowledge bases design, we observed large scale applications, promising results for phenotypes characterization, semantic-aware machine learning solutions for biomedical data analysis, and semantic provenance information representations for scientific reproducibility evaluation. In the KRM selection for 2019, research on knowledge representation demonstrated significant contributions both in the design and in the application of semantic resources. Semantic representations serve a great variety of applications across many medical domains, with actionable results.

To select, present, and summarize the best papers published in 2017 in the field of Knowledge Representation and Management (KRM). A comprehensive and standardized review of the medical informatics literature was performed to select the most interesting papers of KRM published in 2017, based on a PubMed query. In direct line with the research on data integration presented in the KRM section of the 2017 edition of the International Medical Informatics Association (IMIA) Yearbook, the five best papers for 2018 demonstrate even further the added-value of ontology-based integration approaches for phenotype-genotype association mining. Additionally, among the 15 preselected papers, two aspects of KRM are in the spotlight: the design of knowledge bases and new challenges in using ontologies. Ontologies are demonstrating their maturity to integrate medical data and begin to support clinical practices. New challenges have emerged: the query on distributed semantically annotated datasets, the efficiency of semantic annotation processes, the semantic representation of large textual datasets, the control of biases associated with semantic annotations, and the computation of Bayesian indicators on data annotated with ontologies.

Psychiatry aims at detecting symptoms, providing diagnoses and treating mental disorders. We developed ONTOPSYCHIA, an ontology for psychiatry in three modules: social and environmental factors of mental disorders, mental disorders, and treatments. The use of ONTOPSYCHIA, associated with dedicated tools, will facilitate semantic research in Patient Discharge Summaries (PDS). To develop the first module of the ontology we propose a PDS text analysis in order to explicit psychiatry concepts. We decided to set aside classifications during the construction of the modu le, to focus only on the information contained in PDS (bottom-up approach) and to return to domain classifications solely for the enrichment phase (top-down approach). Then, we focused our work on the development of the LOVMI methodology (Les Ontologies Validées par Méthode Interactive - Ontologies Validated by Interactive Method), which aims to provide a methodological framework to validate the structure and the semantic of an ontology.

Clinical data captured in hospital information systems may be unusable in their original format due to missing information or knowledge. The use of external resources (e.g. domain ontology) could be a way of dealing with this lack of knowledge. Our study thus aimed to develop a framework allowing a user to perform medical queries in the context of infectious diseases. By creating an interaction between a knowledge source and clinical data, using semantic and semantic web tools and methods, the users are able to perform queries on a database to obtain results about antibiotic resistance. This work has been performed in the context of the DebugIT European project that aims to control and monitor the antibioresistance growth via a semantic interoperability platform. The results obtained by the use of different semantic web tools were quantitatively evaluated by comparison of the number of results and the query execution time. We have compared our approach with classic business intelligence approaches in terms of usability and functionality.

L’homme Ce que nous avons de Corippe ne nous est connu que par son œuvre et par les incipit ou explicit de ses rares manuscrits. Son nom Flavius Cresconius Corippus peut être reconstitué à partir de la tradition manuscrite et des témoignages médiévaux. Son gentilice a été transmis par un manuscrit aujourd’hui perdu de la bibliothèque fondée à Buda par Matthias Corvin. Son premier surnom, Cresconius, se rencontre fréquemment dans l’onomastique de l’Afrique chrétienne. Le second, attesté sous l...

L’homme Fronton est né en Numidie, à Cirta (Constantine), au début du iie siècle après Jésus-Christ. Il commença ses études en Afrique (à Carthage ?), puis les compléta à Rome avec Athénodote et Dionysius. A la fin du règne d’Hadrien, il était considéré comme le plus grand orateur et avocat de son temps, ce qui lui valut une brillante carrière (questeur en Sicile. Puis édile et préteur à Rome) et surtout d’être choisi par Antonin le Pieux comme précepteur des futurs empereurs Marc-Aurèle et L...

Structured entry forms for clinical records should be updated to take into account the physicians' needs during consultation and advances in medical knowledge and practice. We updated the computerized medical record form of a hypertension clinic, based on its previous use and clinical guidelines. A statistical analysis of previously completed forms identified several unnecessary items rarely used by clinicians. A terminological analysis of guidelines and of free-text answers on completed forms identified several new topics relevant to current clinical practice. We therefore added new items to the form and some topics previously recorded as free text were itemized. We collaborated with clinicians in interpretation of the results of the statistical and terminological analyses used as the starting point and guide for this updating process.

In the medical field, it is now established that the maintenance of unambiguous thesauri goes through ontologies. Our research task is to help pneumologists code acts and diagnoses with a software that represents medical knowledge through a domain ontology. In this paper, we describe our general methodology aimed at knowledge engineers in order to build various types of medical ontologies based on terminology extraction from texts. The hypothesis is to apply natural language processing tools to textual patient discharge summaries to develop the resources needed to build an ontology in pneumology. Results indicate that the joint use of distributional analysis and lexico-syntactic patterns performed satisfactorily for building such ontologies.