A Software Framework for Remote Patient Monitoring by Using Multi-Agent Systems Support
Notice bibliographique
Résumé
BACKGROUND: Although there have been significant advances in network, hardware, and software technologies, the health care environment has not taken advantage of these developments to solve many of its inherent problems. Research activities in these 3 areas make it possible to apply advanced technologies to address many of these issues such as real-time monitoring of a large number of patients, particularly where a timely response is critical. OBJECTIVE: The objective of this research was to design and develop innovative technological solutions to offer a more proactive and reliable medical care environment. The short-term and primary goal was to construct IoT4Health, a flexible software framework to generate a range of Internet of things (IoT) applications, containing components such as multi-agent systems that are designed to perform Remote Patient Monitoring (RPM) activities autonomously. An investigation into its full potential to conduct such patient monitoring activities in a more proactive way is an expected future step. METHODS: A framework methodology was selected to evaluate whether the RPM domain had the potential to generate customized applications that could achieve the stated goal of being responsive and flexible within the RPM domain. As a proof of concept of the software framework's flexibility, 3 applications were developed with different implementations for each framework hot spot to demonstrate potential. Agents4Health was selected to illustrate the instantiation process and IoT4Health's operation. To develop more concrete indicators of the responsiveness of the simulated care environment, an experiment was conducted while Agents4Health was operating, to measure the number of delays incurred in monitoring the tasks performed by agents. RESULTS: IoT4Health's construction can be highlighted as our contribution to the development of eHealth solutions. As a software framework, IoT4Health offers extensibility points for the generation of applications. Applications can extend the framework in the following ways: identification, collection, storage, recovery, visualization, monitoring, anomalies detection, resource notification, and dynamic reconfiguration. Based on other outcomes involving observation of the resulting applications, it was noted that its design contributed toward more proactive patient monitoring. Through these experimental systems, anomalies were detected in real time, with agents sending notifications instantly to the health providers. CONCLUSIONS: We conclude that the cost-benefit of the construction of a more generic and complex system instead of a custom-made software system demonstrated the worth of the approach, making it possible to generate applications in this domain in a more timely fashion.
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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 | 0,004 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».