Carbohydrate Detection Failure Analysis via Biosensoring

The causes of implantable carbohydrate biosensor sensitivity reduction have been investigated by means of fault tree analysis (FTA) relying on fuzzy reasoning to account for uncertainty. Using the fuzzy FTA methodology proposed herein all ultimate causes or combination of causes, attributed to the device components, the surrounding tissue and their intra/inter-relations at both, the surface- and the deep-knowledge level, that are responsible for or contribute to the top event have been recognized and quantified based on: (a) measurements for the deterministic contributors; and (b) experience for the stochastic contributors. The tree structure has been designed by combining deduction and induction, top-down and bottom-up techniques, thus establishing a dialectic trade-off which brings this method closer to scientific logic, permitting the introduction of deeper knowledge into the surface or experiential knowledge level characterizing usual FTA applications. The partitioning of the space of the variables and the determination of the fuzzy rules serving as the inference engine for diagnosis has been achieved by using experts' opinion through a modified 4-stage Delphi method. 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 tailor made development, thus potentiating the progress of epidemics from statistics to individualisation