A Guideline Compliant Clinical Decision Support System In Mobile And Smart Environments For Diagnosing Medical Conditions
Notice bibliographique
Résumé
Background & Objectives: Integration of Clinical Decision Support Systems (CDSS) in mobile and smart environments helps to improve the quality of life of people with health problems. CDSS are used to derive clinical conclusions from patient data, in order to automate and help the process of diagnosing and treating the patient. One way that CDSS can be implemented is to formalize a clinical guideline (document detailing best practices for diagnosing and treating patients) and use it on a knowledge base containing the patient's data. An interesting perspective is to use CDSS in a smart home (SH) setting, where data for the remote CDSS can be obtained from SH services and mobile devices using ambient and wearable sensors. However, in order to maintain minimum quality of service for CDSS decision support, the CDSS decision process must be deployed locally as a SH service an on mobile devices. An ideal example domain for this integration scenario is the diagnosis of sleep apnea. Sleep Apnea has several symptoms that include recurrent awakening, loud snoring, choking episodes, non-restorative sleep and daytime sleepiness. Usually, an individual with sleep apnea is not aware of having difficulty breathing, and is often recognized by others witnessing the individual during sleep apnea episode or is suspected because of the observed symptoms. Sensors could detect such episodes automatically without the need for a human intervention, or recognitions of said symptoms. The objectives are to illustrate the feasibility of CDSS as SH service and on mobile devices by using the Sleep Apnea CDSS. Methods: The sleep apnea CDSS decision process uses Semantic Web tools and rule-based reasoning, in order to formalize the current Canadian guideline for the recognition of sleep apnea. A total of 9 rules were derived. A patient dataset comprises health factors related to sleep apnea, including clinically relevant personal information, clinical measures and observations, and symptoms specific to sleep apnea. To validate the decision process of a CDSS integrated with smart homes, we implemented the Sleep Apnea CDSS decision process on an Android smartphone (Samsung Galaxy SIII). For this validation, we assume that we have received data from the patient diary application, the SH services or local smartphone monitoring services. We generated datasets containing clinical data (measurements), whereby fact values were created based on ranges encompassing both clinically normal situations as well as abnormal situations. We have 7 dataset configurations (1 to 7 days of data), with 10 generated datasets by configuration (70 datasets). Results: The validation shows promising results for using CDSS on mobile phone: loading data and rules (140-425 ms), executing rules (45-82 ms) and memory usage for the reasoning process (174-350 KB). Conclusions: The results show that a guideline compliant CDSS system can be implemented on currently available mobile devices. While this validation was limited in scope, as we did not tackle the precise derivation of sensor data into the used clinical facts, it shows nonetheless that with such set of inferred clinical facts, interesting and clinically relevant problems can be tackled.
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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 | 0,013 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 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 ».