Implantable sensors for glucose monitoring are the first step towards the development of an implantable closed-loop diabetes control system. Although significant advances in the designs and chemistries employed to prepare intravascular and subcutaneous devices have been achieved, the biological responses can have a dramatic impact on the analytical accuracy of such probes. With a view to assisting the effective design of such devices for assuring clinical performance, the causes of implantable glucose sensor failure have been investigated by means of Fault Tree Analysis (FTA) relying on fuzzy reasoning to account for uncertainty. The approach suggested may contribute significantly to the self-optimisation of the measuring equipment from one generation to the next as it supports the flexible, ad hoc, and tailor made sensor development, thus potentiating the progress of epidemics from statistics to individualisation.
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