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HYDRA: A HYbrid Diagnosis and monitoRing Architecture for diabetes

Kafalı, Özgur and Schaechtle, Ulrich and Stathis, Kostas (2015) HYDRA: A HYbrid Diagnosis and monitoRing Architecture for diabetes. In: 2014 IEEE 16th International Conference on e-Health Networking, Applications and Services (Healthcom). IEEE, pp. 531-536. E-ISBN 978-1-4799-6644-8. (doi:10.1109/HealthCom.2014.7001898) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1109/HealthCom.2014.7001898

Abstract

We present Hydra: a multi-agent hybrid diagnosis and monitoring architecture that is aimed at helping diabetic patients manage their illness. It makes use of model-based diagnosis techniques, where the model can be developed by two different approaches combined in a novel way. In the first approach, we build the model based on the medical guidelines provided for diabetes. A computational logic agent monitors the patient and provides feedback based on the model whenever the current observations regarding the patient are sufficient to draw a conclusion. In the second approach, we assume a function for the model, and learn its parameters through data. The model is consistently updated via incoming observations about the patients, and allows prediction of possible future values. We describe the components of such an architecture, and how it can integrated into the existing COMMODITY12 personal health system. We implement a prototype of Hydra, and present its workings on a case study on hypoglycemia monitoring. We report our prediction results for this scenario.

Item Type: Book section
DOI/Identification number: 10.1109/HealthCom.2014.7001898
Uncontrolled keywords: sugar; monitoring; predictive models; diabetes; autoregressive processes; Kalman filters; data models
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.76.E95 Expert Systems (Intelligent Knowledge Based Systems)
Divisions: Faculties > Sciences > School of Computing
Depositing User: Ozgur Kafali
Date Deposited: 04 Feb 2018 13:28 UTC
Last Modified: 25 Sep 2019 11:44 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/65880 (The current URI for this page, for reference purposes)
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