An open platform for personal health record apps with platform-level privacy protection.

One of the main barriers to the adoption of Personal Health Records (PHR) systems is their closed nature. It has been argued in the literature that this barrier can be overcome by introducing an open market of substitutable PHR apps. The requirements introduced by such an open market on the underlying platform have also been derived. In this paper, we argue that MyPHRMachines, a cloud-based PHR platform recently developed by the authors, satisfies these requirements better than its alternatives. The MyPHRMachines platform leverages Virtual Machines as flexible and secure execution sandboxes for health apps. MyPHRMachines does not prevent pushing hospital- or patient-generated data to one of its instances, nor does it prevent patients from sharing data with their trusted caregivers. External software developers have minimal barriers to contribute innovative apps to the platform, since apps are only required to avoid pushing patient data outside a MyPHRMachines cloud. We demonstrate the potential of MyPHRMachines by presenting two externally contributed apps. Both apps provide functionality going beyond the state-of-the-art in their application domain, while they did not require any specific MyPHRMachines platform extension.

Model predictive control using fuzzy decision functions.

Fuzzy predictive control integrates conventional model predictive control with techniques from fuzzy multicriteria decision making, translating the goals and the constraints to predictive control in a transparent way. The information regarding the (fuzzy) goals and the (fuzzy) constraints of the control problem is combined by using a decision function from the theory of fuzzy sets. This paper investigates the use of fuzzy decision making (FDM) in model predictive control (MPG), and compares the results to those obtained from conventional MPG. Attention is also paid to the choice of aggregation operators for fuzzy decision making in control. Experiments on a nonminimum phase, unstable linear system, and on an air-conditioning system with nonlinear dynamics are studied. It is shown that the performance of the model predictive controller can be improved by the use of fuzzy criteria in a fuzzy decision making framework.

Similarity measures in fuzzy rule base simplification.

In fuzzy rule-based models acquired from numerical data, redundancy may be present in the form of similar fuzzy sets that represent compatible concepts. This results in an unnecessarily complex and less transparent linguistic description of the system. By using a measure of similarity, a rule base simplification method is proposed that reduces the number of fuzzy sets in the model. Similar fuzzy sets are merged to create a common fuzzy set to replace them in the rule base. If the redundancy in the model is high, merging similar fuzzy sets might result in equal rules that also can be merged, thereby reducing the number of rules as well. The simplified rule base is computationally more efficient and linguistically more tractable. The approach has been successfully applied to fuzzy models of real world systems.