A Semantic Web Framework To Computerize And Execute Clinical Guidelines: Towards The Handling Of Co-Morbidities In Clinical Decision Support Systems
Notice bibliographique
Résumé
Background: A Canadian study recommends General Practitioners (GP) to use evidence based Clinical Guidelines (CG) when dealing with co-morbid cardiovascular diseases, in particular for the diagnosis and preliminary management of co-morbid Chronic Heart Failure (CHF) and Atrial Fibrillation (AF). Although paper-based Canadian CG exist for the management of CHF and AF, the challenge for physicians is to simultaneously apply multiple independent CG when dealing with patients having cardiovascular co-morbidities. Objective: The objective of this inter-disciplinary research program is to assist physicians in handling co-morbidities through a computerized clinical decision support framework that recommends evidence-based interventions based on the patient's health profile. We target decision support for the diagnosis and treatment of CHF, AF and co-morbid CHF-AF. Approach: We take a healthcare knowledge management approach to develop a Clinical Decision Support System (CDSS) for handling comorbid diseases. Our solution involves the development of institution-specific CP from a combination of CG, and then generate a CP knowledge model using a semantically-rich formalism—i.e. a CG ontology. The CG ontology semantically defines the clinical concepts in order to establish semantic interoperability between multiple CG. Next, we systematically align the ontologically-modeled CG of different diseases to realize a unified knowledge model that derives the evidence based recommendations for handling both single and co-morbid diseases. Our methodology entails the following steps: (i) knowledge identification to derive specialized disease-specific CG from existing evidence-based sources; (b) knowledge modeling to abstract medical and procedural knowledge from the CG; (c) knowledge representation to computerize the CG in terms of a semantically-rich CG ontology; (d) knowledge alignment to systematically synthesize multiple ontologically-modeled CG to develop a unified ontology-based CG knowledge model representing comorbid diseases; (e) knowledge execution to generate patient-specific recommendations, based on patient data, by reasoning over the aligned CG model; and (f) evaluation of the knowledge model and the recommendations produced in response to a range of clinical scenarios. Results: We present the COMET (Co-morbidity Ontological Modeling & ExecuTion) system to provide clinical decision support for three scenarios: (i) Cardiac Heart Failure (CHF); (ii) Atrial Fibrillation (AF); and (iii) co-morbidity of either AF or CHF. COMET is designed for GP in Nova Scotia and is accessible over the web. Evaluation: A pilot study was conducted to assess how well COMET meets the physician's needs to manage co-morbid CHF-AF. Conclusion: In conclusion, this project provides a solution for the complex problem of handling co-morbidities in a CDSS. Our solution is based on semantic modeling of disease-specific knowledge which extends the possibility of scaling up to include additional diseases and aligning their knowledge models to handle even further co-morbid situations. Our CG alignment approach helps (a) avoiding duplication of clinical tasks; (b) re-usability of diagnostic results; (c) determine compatibility of different clinical activities; and (d) standardization of care across multiple institutions. We believe that this project achieves knowledge translation whereby we have successfully computerized and translated paper-based CG so that they can now be operationalized at the point-of-care by GP to handle comorbid diseases.
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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 | 0,006 | 0,007 |
| 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 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| 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 ».