Neural modeling of the blood glucose level for type 1 diabetes mellitus patients

This paper presents the application of a recurrent multilayer perceptron neural network for modeling blood glucose dynamics in Type 1 Diabetes Mellitus (T1DM). Training is performed based on an extended Kalman filtering (EKF) learning algorithm. Then, the EKF performance is compared with the well-known Levenberg-Marquardt (LM) learning algorithm. The goal is to derive a dynamical mathematical model for T1DM considering the response of a patient to meal and subcutaneous insulin infusion. Thus, the main contribution of this work is to propose a modeling methodology for blood glucose dynamics based in Artificial Neural Networks (ANN). Experimental data, given by a continuous glucose monitoring system, are utilized for identification purposes and for applicability trials of the proposed scheme in T1DM therapy. � 2011 IEEE

Neural modeling of the blood glucose level for type 1 diabetes mellitus patients

This paper discusses a novel training algorithm for a neural network architecture applied to time series prediction with smart grids applications. The proposed training algorithm is based on an extended Kalman filter (EKF) improved using particle swarm optimization (PSO) to compute the design parameters. The EKF-PSO-based algorithm is employed to update the synaptic weights of the neural network. The size of the regression vector is determined by means of the Cao methodology. The proposed structure captures more efficiently the complex nature of the wind speed, energy generation, and electrical load demand time series that are constantly monitorated in a smart grid benchmark. The proposed model is trained and tested using real data values in order to show the applicability of the proposed scheme. " 2013 Alma Y. Alanis et al.",,,,,,"10.1155/2013/197690",,,"http://hdl.handle.net/20.500.12104/43068","http://www.scopus.com/inward/record.url?eid=2-s2.0-84879290075&partnerID=40&md5=cc3ccfb7130389dad494823eb1611e09",,,,,,,,"Mathematical Problems in Engineering",,,,"2013",,"Scopu

EDMON - Electronic Disease Surveillance and Monitoring Network: A Personalized Health Model-based Digital Infectious Disease Detection Mechanism using Self-Recorded Data from People with Type 1 Diabetes

Through time, we as a society have been tested with infectious disease outbreaks of different magnitude, which often pose major public health challenges. To mitigate the challenges, research endeavors have been focused on early detection mechanisms through identifying potential data sources, mode of data collection and transmission, case and outbreak detection methods. Driven by the ubiquitous nature of smartphones and wearables, the current endeavor is targeted towards individualizing the surveillance effort through a personalized health model, where the case detection is realized by exploiting self-collected physiological data from wearables and smartphones. This dissertation aims to demonstrate the concept of a personalized health model as a case detector for outbreak detection by utilizing self-recorded data from people with type 1 diabetes. The results have shown that infection onset triggers substantial deviations, i.e. prolonged hyperglycemia regardless of higher insulin injections and fewer carbohydrate consumptions. Per the findings, key parameters such as blood glucose level, insulin, carbohydrate, and insulin-to-carbohydrate ratio are found to carry high discriminative power. A personalized health model devised based on a one-class classifier and unsupervised method using selected parameters achieved promising detection performance. Experimental results show the superior performance of the one-class classifier and, models such as one-class support vector machine, k-nearest neighbor and, k-means achieved better performance. Further, the result also revealed the effect of input parameters, data granularity, and sample sizes on model performances. The presented results have practical significance for understanding the effect of infection episodes amongst people with type 1 diabetes, and the potential of a personalized health model in outbreak detection settings. The added benefit of the personalized health model concept introduced in this dissertation lies in its usefulness beyond the surveillance purpose, i.e. to devise decision support tools and learning platforms for the patient to manage infection-induced crises