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, which are attributed to the device components, the surrounding tissue, and their intrarelations/interrelations at both the surface- and the deepknowledge level, that are responsible for or contribute to the top event have been recognized and quantified. The tree structure has been designed by combining deduction and induction and top-down and bottom-up techniques, thus establishing a dialectic tradeoff that brings this method closer to scientific logic, permitting the introduction of deeper knowledge into the surface or experiential knowledge level that characterizes usual FTA applications. The partitioning of the space of the variables and the determination of the fuzzy rules that serve as the inference engine for diagnosis have been achieved by using experts´ opinions through a modified four-stage Delphi method. The approach suggested may significantly contribute to the self-optimization of the measuring equipment from one generation to the next as it supports flexible tailor-made development, thus potentiating the progress of epidemics from statistics to individualization.