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OntolUrgences is a termino-ontological resource developed to index and retrieve information in electronic Emergency Medical Record. In this project, we improved the ontology coverage to accommodate both anatomical and pathophysiological concepts in emergency medicine. This work lead to the automatic addition of 3,470 concepts and their underlying semantic formalization. In our method, we reuse and select the anatomical concepts relevant to emergency from FMA: To capture the anatomical specific concepts, (i) we involved Emergency practitioners and identified the key concepts from this domain; (ii) we applied an automatic algorithm to define the semantic relationships and integrated the result in the existing ontology.

Evaluation and validation have become a crucial problem for the development of semantic resources. We developed Ci4SeR, a Graphical User Interface to optimize the curation work (not taking into account structural aspects), suitable for any type of resource with lightweight description logic. We tested it on OntoADR, an ontology of adverse drug reactions. A single curator has reviewed 326 terms (1020 axioms) in an estimated time of 120 hours (2.71 concepts and 8.5 axioms reviewed per hour) and added 1874 new axioms (15.6 axioms per hour). Compared with previous manual endeavours, the interface allows increasing the speed-rate of reviewed concepts by 68% and axiom addition by 486%. A wider use of Ci4SeR would help semantic resources curation and improve completeness of knowledge modelling.

Robust alignments between ICD and MedDRA are essential to enable the secondary use of clinical data for pharmacovigilance research. UMLS makes available ICD-to-MedDRA mappings, but they are only poorly specified, which introduces difficulties when exploited in an automatic way. SKOS vocabulary can help achieve quality and machine-processable mappings. We have developed an algorithm based on several simple rules which annotates automatically ICD-to-MedDRA mappings with SKOS predicates. The method was tested and evaluated on a sample of ICD-10-to MedDRA mappings extracted from UMLS. The algorithm demonstrated satisfying performances, especially for skos:exactMatch properties, which suggests that automatic methods can be used to improve the quality of terminology mappings.

The difficult task of detecting adverse drug events (ADEs) and the tedious process of building manual reports of ADE occurrences out of patient profiles result in a majority of adverse reactions not being reported to health regulatory authorities. The SALUS individual case safety report (ICSR) reporting tool, a component currently developed within the SALUS project, aims to support semi-automatic reporting of ADEs to regulatory authorities. In this paper, we present an initial design and current state of of our ICSR reporting tool that features: (i) automatic pre-population of reporting forms through extraction of the patient data contained in an Electronic Health Record (EHR); (ii) generation and electronic submission of the completed ICSRs by the physician to regulatory authorities; and (iii) integration of the reporting process into the physician's work-flow to limit the disturbance. The objective is to increase the rates of ADE reporting and the quality of the reported data. The SALUS interoperability platform supports patient data extraction independently of the EHR data model in use and allows generation of reports using the format expected by regulatory authorities.

In the context of PROTECT European project, we have developed an ontology of adverse drug reactions (OntoADR) based on the original MedDRA hierarchy and a query-based method to achieve automatic MedDRA terms groupings for improving pharmacovigilance signal detection. Those groupings were evaluated against standard handmade MedDRA groupings corresponding to first priority pharmacovigilance safety topics. Our results demonstrate that this automatic method allows catching most of the terms present in the reference groupings, and suggest that it could offer an important saving of time for the achievement of pharmacovigilance groupings. This paper describes the theoretical context of this work, the evaluation methodology, and presents the principal results.