A semi-automatic semantic method for mapping SNOMED CT concepts to VCM Icons

VCM (Visualization of Concept in Medicine) is an iconic language for representing key medical concepts by icons. However, the use of this language with reference terminologies, such as SNOMED CT, will require the mapping of its icons to the terms of these terminologies. Here, we present and evaluate a semi-automatic semantic method for the mapping of SNOMED CT concepts to VCM icons. Both SNOMED CT and VCM are compositional in nature; SNOMED CT is expressed in description logic and VCM semantics are formalized in an OWL ontology. The proposed method involves the manual mapping of a limited number of underlying concepts from the VCM ontology, followed by automatic generation of the rest of the mapping. We applied this method to the clinical findings of the SNOMED CT CORE subset, and 100 randomly-selected mappings were evaluated by three experts. The results obtained were promising, with 82 of the SNOMED CT concepts correctly linked to VCM icons according to the experts. Most of the errors were easy to fix

Towards iconic language for patient records, drug monographs, guidelines and medical search engines.

International audiencePracticing physicians have limited time for consulting medical knowledge and records. We have previously shown that using icons instead of text to present drug monographs may allow contraindications and adverse effects to be identified more rapidly and more accurately. These findings were based on the use of an iconic language designed for drug knowledge, providing icons for many medical concepts, including diseases, antecedents, drug classes and tests. In this paper, we describe a new project aimed at extending this iconic language, and exploring the possible applications of these icons in medicine. Based on evaluators' comments, focus groups of physicians and opinions of academic, industrial and associative partners, we propose iconic applications related to patient records, for example summarizing patient conditions, searching for specific clinical documents and helping to code structured data. Other applications involve the presentation of clinical practice guidelines and improving the interface of medical search engines. These new applications could use the same iconic language that was designed for drug knowledge, with a few additional items that respect the logic of the language

Towards iconic language for patient records, drug monographs, guidelines and medical search engines.

International audiencePracticing physicians have limited time for consulting medical knowledge and records. We have previously shown that using icons instead of text to present drug monographs may allow contraindications and adverse effects to be identified more rapidly and more accurately. These findings were based on the use of an iconic language designed for drug knowledge, providing icons for many medical concepts, including diseases, antecedents, drug classes and tests. In this paper, we describe a new project aimed at extending this iconic language, and exploring the possible applications of these icons in medicine. Based on evaluators' comments, focus groups of physicians and opinions of academic, industrial and associative partners, we propose iconic applications related to patient records, for example summarizing patient conditions, searching for specific clinical documents and helping to code structured data. Other applications involve the presentation of clinical practice guidelines and improving the interface of medical search engines. These new applications could use the same iconic language that was designed for drug knowledge, with a few additional items that respect the logic of the language