Integrating semantic and fuzzy dimensions into electronic medical records: Case of cerebral palsy information system
Notice bibliographique
Résumé
The meta-modeling of medical records helps standardize and capitalize the expert’s knowledge domain. It promotes the interoperability knowledge and the reuse of clinical concepts, i.e., archetypes. It also promotes high quality electronic medical record system (EMRS) design, which helps provide better care service delivery. As a result, different standards of medical informatics use the dual model to support interoperability between Medical Information Systems. We particularly quote ISO/EN 13606 and OpenEHR. However, the use of these standards still presents challenges. Apart from political reasons, the main obstacles to the adoption of these standards include: (1) a lack of guides and methodological tools to facilitate the construction of EMRS using two conceptual levels. Designers must have languages, approaches and tools to assist them in the modeling of archetypal EMRS; (2) a lack of methodologies for semantic activities on the content of electronic health records in the semantic web environment; (3) and a lack of management of uncertainties and inaccuracies that may exist in the medical field. Theconstruction of an approach to modeling EMRS according to the dual model approach, considering the uncertainties, inaccuracies and semantics of these systems, is a difficult task, given the challenges to emancipate. In literature, we don’t find such an approach. We, therefore, defined one in this paper. Our goal is to guide the designer in all stages of developing a new generation of EMRS, from analysis and specification of requirements to implementation. To achieve this goal, we have created an approach to support the following activities: (1) clinical concepts and information management and meta-modeling in accordance with the openEHR standard, (2) integration of the semantic dimension into EMRS considered to enable the execution of semantic activities in the semantic web environment; and (3) integration of the fuzzy dimension into electronic medical record data structures. As a contribution, we defined an approach called Fuzzy SemanticOpenEHR allowing the integration of semantic and fuzzy dimensions into EMRS modeled using the openEHR standard. Fuzzy SemanticOpenEHR intends to help and equip the designer during the different phases of creating a fuzzy ontology. Thanks to the mechanisms offered by this approach, we have been able to obtain a fuzzy ontological basis that can serve as a knowledge base that can support the semantic interoperability between EMRS, the deduction of new knowledge and the taking of knowledge’s clinical decision. To test our contribution, we proceeded to the realization of a prototype of tools realized for the pediatric neurology service of the university hospital “Hédi Chaker Sfax - Tunisia” and the association of the handicapped persons safeguard of Sfax. This prototype is a framework called “XML 2 FuzzyOWL”. Then, we tested this framework using a case of a disease which is “Cerebral Palsy”.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».