Novel sensor technology integration for outcome-based risk analysis in diabetes

Subramanian, Mahesh, Conley, Edward Clarke, Rana, Omer Farooq ORCID: https://orcid.org/0000-0003-3597-2646, Hardisty, Alex ORCID: https://orcid.org/0000-0002-0767-4310, Shaikh Ali, Ali, Luzio, Stephen, Owens, David Raymond, Wright, Steve, Donovan, Tim, Bedi, Bharat, Conway-Jones, Dave, Vyvyan, David, Arnold, Gillian, Creasey, Chris, Horgan, Adrian, Cox, Tristram and Waite, Rhys 2008. Novel sensor technology integration for outcome-based risk analysis in diabetes. Presented at: HealthINF 2008 : International Conference on Health Informatics, Funchal-Madeira, Portugal, 28-31 January, 2008.

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Abstract

Novel sensor-based continuous biomedical monitoring technologies have a major role in chronic disease management for early detection and prevention of known adverse trends. In the future, a diversity of physiological, biochemical and mechanical sensing principles will be available through sensor device 'ecosystems'. In anticipation of these sensor-based ecosystems, we have developed Healthcare@Home (HH) - a research-phase generic intervention-outcome monitoring framework. HH incorporates a closed-loop intervention effect analysis engine to evaluate the relevance of measured (sensor) input variables to system-defined outcomes. HH offers real-world sensor type validation by evaluating the degree to which sensor-derived variables are relevant to the predicted outcome. This 'index of relevance' is essential where clinical decision support applications depend on sensor inputs. HH can help determine system-integrated cost-utility ratios of bespoke sensor families within defined applications - taking into account critical factors like device robustness / reliability / reproducibility, mobility / interoperability, authentication / security and scalability / usability. Through examples of hardware / software technologies incorporated in the HH end-to-end monitoring system, this paper discusses aspects of novel sensor technology integration for outcome-based risk analysis in diabetes.

Item Type:	Conference or Workshop Item (Paper)
Status:	Published
Schools:	Schools > Computer Science & Informatics Schools > Medicine
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > R Medicine (General)
Uncontrolled Keywords:	Health informatics, home healthcare, biomedical sensor devices, mobility, wearable sensors, decision support system, individualised risk analysis.
Date of First Compliant Deposit:	30 March 2016
Last Modified:	10 Sep 2025 21:48
URI:	https://orca.cardiff.ac.uk/id/eprint/5245

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