A Validation Tool (VaPCE) for Postcoordinated SNOMED CT Expressions: Development and Usability Study
Notice bibliographique
Résumé
Background: The digitalization of health care has increased the demand for efficient data exchange, emphasizing semantic interoperability. SNOMED Clinical Terms (SNOMED CT), a comprehensive terminology with over 360,000 medical concepts, supports this need. However, it cannot cover all medical scenarios, particularly in complex cases. To address this, SNOMED CT allows postcoordination, where users combine precoordinated concepts with new expressions. Despite SNOMED CT's potential, the creation and validation of postcoordinated expressions (PCEs) remain challenging due to complex syntactic and semantic rules. Objective: This work aims to develop a tool that validates postcoordinated SNOMED CT expressions, focusing on providing users with detailed, automated correction instructions for syntactic and semantic errors. The goal is not just validation, but also offering user-friendly, actionable suggestions for improving PCEs. Methods: A tool was created using the Fast Healthcare Interoperability Resource (FHIR) service $validate-code and the terminology server Ontoserver to check the correctness of PCEs. When errors are detected, the tool processes the SNOMED CT Concept Model in JSON format and applies predefined error categories. For each error type, specific correction suggestions are generated and displayed to users. The key added value of the tool is in generating specific correction suggestions for each identified error, which are displayed to the users. The tool was integrated into a web application, where users can validate individual PCEs or bulk-upload files. The tool was tested with real existing PCEs, which were used as input and validated. In the event of errors, appropriate error messages were generated as output. Results: In the validation of 136 PCEs from 304 FHIR Questionnaires, 18 (13.2%) PCEs were invalid, with the most common errors being invalid attribute values. Additionally, 868 OncoTree codes were evaluated, resulting in 161 (20.9%) PCEs containing inactive concepts, which were successfully replaced with valid alternatives. A user survey reflects a favorable evaluation of the tool's functionality. Participants found the error categorization and correction suggestions to be precise, offering clear guidance for addressing issues. However, there is potential for enhancement, particularly regarding the level of detail in the error messages. Conclusions: The validation tool significantly improves the accuracy of postcoordinated SNOMED CT expressions by not only identifying errors but also offering detailed correction instructions. This approach supports health care professionals in ensuring that their PCEs are syntactically and semantically valid, enhancing data quality and interoperability across systems.
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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 ».