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.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,012 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,004 |
| Bibliométrie | 0,006 | 0,005 |
| Études des sciences et des technologies | 0,002 | 0,004 |
| Communication savante | 0,008 | 0,008 |
| Science ouverte | 0,004 | 0,005 |
| Intégrité de la recherche | 0,004 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».