Extending and encoding existing biological terminologies and datasets for use in the reasoned semantic web
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Notice bibliographique
Résumé
BACKGROUND: Clinical phenotypes and disease-risk stratification are most often determined through the direct observations of clinicians in conjunction with published standards and guidelines, where the clinical expert is the final arbiter of the patient's classification. While this "human" approach is highly desirable in the context of personalized and optimal patient care, it is problematic in a healthcare research setting because the basis for the patient's classification is not transparent, and likely not reproducible from one clinical expert to another. This sits in opposition to the rigor required to execute, for example, Genome-wide association analyses and other high-throughput studies where a large number of variables are being compared to a complex disease phenotype. Most clinical classification systems and are not structured for automated classification, and similarly, clinical data is generally not represented in a form that lends itself to automated integration and interpretation. Here we apply Semantic Web technologies to the problem of automated, transparent interpretation of clinical data for use in high-throughput research environments, and explore migration-paths for existing data and legacy semantic standards. RESULTS: Using a dataset from a cardiovascular cohort collected two decades ago, we present a migration path - both for the terminologies/classification systems and the data - that enables rich automated clinical classification using well-established standards. This is achieved by establishing a simple and flexible core data model, which is combined with a layered ontological framework utilizing both logical reasoning and analytical algorithms to iteratively "lift" clinical data through increasingly complex layers of interpretation and classification. We compare our automated analysis to that of the clinical expert, and discrepancies are used to refine the ontological models, finally arriving at ontologies that mirror the expert opinion of the individual clinical researcher. Other discrepancies, however, could not be as easily modeled, and we evaluate what information we are lacking that would allow these discrepancies to be resolved in an automated manner. CONCLUSIONS: We demonstrate that the combination of semantically-explicit data, logically rigorous models of clinical guidelines, and publicly-accessible Semantic Web Services, can be used to execute automated, rigorous and reproducible clinical classifications with an accuracy approaching that of an expert. Discrepancies between the manual and automatic approaches reveal, as expected, that clinicians do not always rigorously follow established guidelines for classification; however, we demonstrate that "personalized" ontologies may represent a re-usable and transparent approach to modeling individual clinical expertise, leading to more reproducible science.
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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 | 0,002 | 0,003 |
| 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 découle