Clinical Terminology in Patient Health Record System - SNOMED CT Overview

Background of study: Patient Health Record System (PHRS) is used byphysicians for capturing patient medical records in electronic media.Standardization in PHRS arises a major challenge due to its complexities. Theused of clinical terminology is needed in order to facilitate more expressiveclinical data input, provide unambiguous encoding and support the exchange ofclinical information. One of highly specialized clinical terminology is SNOMEDCT(Systematized Nomenclature of Medicine Clinical Terms) that able to encodeclinical data, and contains concepts that linked to clinical knowledge to enableaccurate recording of data without ambiguity. The aims of this paper is to discussthe use of clinical terminology in PHRS and identifying importance factors forapplying clinical terminology in healthcare services.Method: This study used review of literature in order to find the use of clinicalterminology in patient health record system by reviewing current used of clinicalterminology.Result: The result of the study found that clinical terminology supportsinformation exchange between healthcare providers

Clinical Terminology in Patient Health Record System - SNOMED CT Overview

Background of study: Patient Health Record System (PHRS) is used by physicians for capturing patient medical records in electronic media. Standardization in PHRS arises a major challenge due to its complexities. The used of clinical terminology is needed in order to facilitate more expressive clinical data input, provide unambiguous encoding and support the exchange of clinical information. One of highly specialized clinical terminology is SNOMED CT(Systematized Nomenclature of Medicine Clinical Terms) that able to encode clinical data, and contains concepts that linked to clinical knowledge to enable accurate recording of data without ambiguity. The aims of this paper is to discuss the use of clinical terminology in PHRS and identifying importance factors for applying clinical terminology in healthcare services. Method: This study used review of literature in order to find the use of clinical terminology in patient health record system by reviewing current used of clinical terminology. Result: The result of the study found that clinical terminology supports information exchange between healthcare provider

Contribution à la construction d’ontologies et à la recherche d’information : application au domaine médical

This work aims at providing efficient access to relevant information among the increasing volume of digital data. Towards this end, we studied the benefit from using ontology to support an information retrieval (IR) system.We first described a methodology for constructing ontologies. Thus, we proposed a mixed method which combines natural language processing techniques for extracting knowledge from text and the reuse of existing semantic resources for the conceptualization step. We have also developed a method for aligning terms in English and French in order to enrich terminologically the resulting ontology. The application of our methodology resulted in a bilingual ontology dedicated to Alzheimer’s disease.We then proposed algorithms for supporting ontology-based semantic IR. Thus, we used concepts from ontology for describing documents automatically and for query reformulation. We were particularly interested in: 1) the extraction of concepts from texts, 2) the disambiguation of terms, 3) the vectorial weighting schema adapted to concepts and 4) query expansion. These algorithms have been used to implement a semantic portal about Alzheimer’s disease. Further, because the content of documents are not always fully available, we exploited incomplete information for identifying the concepts, which are relevant for indexing the whole content of documents. Toward this end, we have proposed two classification methods: the first is based on the k nearest neighbors’ algorithm and the second on the explicit semantic analysis. The two methods have been evaluated on large standard collections of biomedical documents within an international challenge.Ce travail vise à permettre un accès efficace à des informations pertinentes malgré le volume croissant des données disponibles au format électronique. Pour cela, nous avons étudié l’apport d’une ontologie au sein d’un système de recherche d'information (RI).Nous avons tout d’abord décrit une méthodologie de construction d’ontologies. Ainsi, nous avons proposé une méthode mixte combinant des techniques de traitement automatique des langues pour extraire des connaissances à partir de textes et la réutilisation de ressources sémantiques existantes pour l’étape de conceptualisation. Nous avons par ailleurs développé une méthode d’alignement de termes français-anglais pour l’enrichissement terminologique de l’ontologie. L’application de notre méthodologie a permis de créer une ontologie bilingue de la maladie d’Alzheimer.Ensuite, nous avons élaboré des algorithmes pour supporter la RI sémantique guidée par une ontologie. Les concepts issus d’une ontologie ont été utilisés pour décrire automatiquement les documents mais aussi pour reformuler les requêtes. Nous nous sommes intéressés à : 1) l’identification de concepts représentatifs dans des corpus, 2) leur désambiguïsation, 3), leur pondération selon le modèle vectoriel, adapté aux concepts et 4) l’expansion de requêtes. Ces propositions ont permis de mettre en œuvre un portail de RI sémantique dédié à la maladie d’Alzheimer. Par ailleurs, le contenu des documents à indexer n’étant pas toujours accessible dans leur ensemble, nous avons exploité des informations incomplètes pour déterminer les concepts pertinents permettant malgré tout de décrire les documents. Pour cela, nous avons proposé deux méthodes de classification de documents issus d’un large corpus, l’une basée sur l’algorithme des k plus proches voisins et l’autre sur l’analyse sémantique explicite. Ces méthodes ont été évaluées sur de larges collections de documents biomédicaux fournies lors d’un challenge international