Adaptive Algorithms for Personalized Diabetes Treatment

Dynamic systems, especially in real-life applications, are often determined by inter-/intra-variability, uncertainties and time-varying components. Physiological systems are probably the most representative example in which population variability, vital signal measurement noise and uncertain dynamics render their explicit representation and optimization a rather difficult task. Systems characterized by such challenges often require the use of adaptive algorithmic solutions able to perform an iterative structural and/or parametrical update process towards optimized behavior. Adaptive optimization presents the advantages of (i) individualization through learning of basic system characteristics, (ii) ability to follow time-varying dynamics and (iii) low computational cost. In this chapter, the use of online adaptive algorithms is investigated in two basic research areas related to diabetes management: (i) real-time glucose regulation and (ii) real-time prediction of hypo-/hyperglycemia. The applicability of these methods is illustrated through the design and development of an adaptive glucose control algorithm based on reinforcement learning and optimal control and an adaptive, personalized early-warning system for the recognition and alarm generation against hypo- and hyperglycemic events.

Modeling corrective experiences as motivational mountain pass

Aim: The landscape metaphor allows viewing corrective experiences (CE) as pathway to a state with relatively lower 'tension' (local minimum). However, such local minima are not easily accessible but obstructed by states with relatively high tension (local maxima) according to the landscape metaphor (Caspar & Berger, 2012). For example, an individual with spider phobia has to transiently tolerate high levels of tension during an exposure therapy to access the local minimum of habituation. To allow for more specific therapeutic guidelines and empirically testable hypotheses, we advance the landscape metaphor to a scientific model which bases on motivational processes. Specifically, we conceptualize CEs as available but unusual trajectories (=pathways) through a motivational space. The dimensions of the motivational state are set up by basic motives such as need for agency or attachment. Methods: Dynamic system theory is used to model motivational states and trajectories using mathematical equations. Fortunately, these equations have easy-to-comprehend and intuitive visual representations similar to the landscape metaphor. Thus, trajectories that represent CEs are informative and action guiding for both therapists and patients without knowledge on dynamic systems. However, the mathematical underpinnings of the model allow researchers to deduct hypotheses for empirical testing. Results: First, the results of simulations of CEs during exposure therapy in anxiety disorders are presented and compared to empirical findings. Second, hypothetical CEs in an autonomy-attachment conflict are reported from a simulation study. Discussion: Preliminary clinical implications for the evocation of CEs are drawn after a critical discussion of the proposed model.

Influence of screw augmentation in posterior dynamic and rigid stabilization systems in osteoporotic lumbar vertebrae: a biomechanical cadaveric study.

STUDY DESIGN Biomechanical cadaveric study. OBJECTIVE To determine whether augmentation positively influence screw stability or not. SUMMARY OF BACKGROUND DATA Implantation of pedicle screws is a common procedure in spine surgery to provide an anchorage of posterior internal fixation into vertebrae. Screw performance is highly correlated to bone quality. Therefore, polymeric cement is often injected through specifically designed perforated pedicle screws into osteoporotic bone to potentially enhance screw stability. METHODS Caudocephalic dynamic loading was applied as quasi-physiological alternative to classical pull-out tests on 16 screws implanted in osteoporotic lumbar vertebrae and 20 screws in nonosteoporotic specimen. Load was applied using 2 different configurations simulating standard and dynamic posterior stabilization devices. Screw performance was quantified by measurement of screwhead displacement during the loading cycles. To reduce the impact of bone quality and morphology, screw performance was compared for each vertebra and averaged afterward. RESULTS All screws (with or without cement) implanted in osteoporotic vertebrae showed lower performances than the ones implanted into nonosteoporotic specimen. Augmentation was negligible for screws implanted into nonosteoporotic specimen, whereas in osteoporotic vertebrae pedicle screw stability was significantly increased. For dynamic posterior stabilization system an increase of screwhead displacement was observed in comparison with standard fixation devices in both setups. CONCLUSION Augmentation enhances screw performance in patients with poor bone stock, whereas no difference is observed for patients without osteoporosis. Furthermore, dynamic stabilization systems have the possibility to fail when implanted in osteoporotic bone.