Bellazzi and colleagues from the Dipiartimento di Informatica e Sistemistica, University of Pavia in Italy have described using simulated blood glucose data from AIDA to test out a number of computer decision-support prototypes under development in their laboratory. In their report entitled "Qualitative models and fuzzy systems: an integrated approach for learning from data" Bellazzi and colleagues [4] have described a prototype method for the identification of the dynamics of non-linear systems in diabetes care by trying to learn from data. The preliminary results obtained using AIDA simulation data have motivated the authors to do further work with their approach, moving towards an evaluation of the method with real patient data. A further report, entitled "Learning from data through the integration of qualitative models and fuzzy systems" [5] attempted to build on the earlier work from this research group and presents a methodology for the identification of the dynamics of non-linear patho-physiological systems once again by trying to learn from data. QSIM is the qualitative simulator that was developed by exploiting the physiological knowledge available in the literature, and in particular by referring to the studies presented in the main AIDA model paper [6] and the report of Berger & Rodbard [7]. Therefore, this work [5] describes a novel approach to the identification of non-linear dynamic systems, which integrates fuzzy systems and qualitative models. In further work by the same group the authors [8] have reported that the main problem in efficiently building robust fuzzy-neural models of non-linear systems lies in the difficulty to define a "meangingful" fuzzy rule-base. Once again, simulated data coming from AIDA was used for training and testing, with data sampled from the AIDA program every 15 minutes. In their report entitled “Adaptive controllers for intelligent monitoring”, Bellazzi and colleagues [9], have also described an approach based on the usual scheme of diabetes out-patient management, based on (i) a period evaluation of patients’ metabolic control performed by the physician, and (ii) patient-tailored tables for self-adjustment of insulin dosages. The low level controller for this work [9] was tested using the AIDA diabetes simulation package. The authors compared the performance of four different control strategies for the implementation of the regulator. The third strategy (c) was based on a fuzzy controller exploiting the ARX model predictions. The fourth strategy (d) used the fuzzy controller operating with ‘perfect’ predictions - i.e. The authors simulated the control system over 192 hours (8 days) with control actions and measurements at each meal (i.e. It was observed that the rule-based control (strategy b) involved insulin adjustments of positive and negative signs, whereas the Fuzzy controller with the ARX model (strategy c), and the Fuzzy controller with ‘perfect’ predictions (strategy d) have only positive arguments. In reference [10], entitled “A distributed system for diabetic patient management”, Bellazzi et al describe a telemedicine-based prototype for diabetes patient management. The authors describe how the output of the reasoning module of the system is an ordered list of alternative diabetes regimens that should be able to solve the metabolic problems detected by the system. In reference [11], entitled “Protocol-based reasoning in diabetic patient management”, Montani and colleagues propose a system for teleconsultation in the management of patients with insulin-dependent diabetes mellitus (IDDM), accessible through the use of the Internet. For this work, in order for the prototype to be able to calculate the effectiveness of a given food intake, food ‘activity’ was calculated using the AIDA model approach [6]. In all three Masters Theses [1-3], and subsequent publications from this group [4,5,8-11], AIDA was used as a simulator of blood glucose dynamics to test out the decision support prototypes. McCausland and colleagues from the University of Melbourne, Victoria, Australia have been developing an expert system to advise on insulin dosage adjustment in diabetes. Staite from University College Northampton in England has been developing a rule-based expert system to try and assist in patient insulin therapy self-management [12].
An Artificial Neural Network (ANN) is an information processing model inspired by the way the densely interconnected parallel structure of the brain is thought to process information. Most ANNs contain some form of learning rule that modifies the weights of the connections according to the input patterns that are presented.
Using this technique input vectors and the corresponding output vectors are used to train a network until it can approximate a function, associating input vectors with specific output vectors. Apparently well trained back-propagation networks tend to give reasonable answers when presented with inputs that they have never seen before. This ANN was trained to predict blood glucose levels 2 hours ahead using the data from case scenario 0001 ("Joy Wilson") in the AIDA database. This ANN prototype was trained with a representative set of one patient’s diet and insulin schedule and compared to the simulated original values.
It will be interesting to see how truly generalisable these ANN are, in practice, when trained with a large amount of data and tested against a new, different patient - separate from the training set.
This research has been taken forward in the same laboratory by Sandham and colleagues [14] who have been investigating making clinical blood glucose predictions using more sophisticated artificial neural networks [15]. For the second prototype a recurrent artificial neural network was adopted, since this has been reported to demonstrate superior performance for prediction problems with short term predictive accuracies ranging from 70-90% (in other fields) [16]. The recurrent ANN, as introduced by Elman (Figure 5) [17], has delays in the feedback loops at the outputs of the recurrent layer, which enable previous time-step values to be used in the current time step.
Basically, the Elman recurrent network is a 2-layer network with feedback from the first layer output to the first layer input.
Training for this recurrent ANN was performed using back-propagation incorporating a momentum term and an adaptive learning rate. For testing out this approach, initially six patients from the Diabetic Outpatient Department of Glasgow Royal Infirmary were selected. The aims and objectives of this further study [18] were to (i) harvest diabetic patient data using the AIDA diabetes simulation package, (ii) train the recurrent ANN with the generated data, and (iii) compare AIDA’s simulated blood glucose levels with predicted blood glucose levels from the ANN. In total 50 data sets were harvested from the PC AIDA software (all from one case scenario), with 6 samples per data set.


Nevertheless, with these test results the authors have been encouraged to expand the AIDA-based artificial neural network to include all the variable parameters included in the AIDA diabetes software simulation package. The next step would then be to train this new network and test it to see if a similar standard of prediction can be obtained with a more comprehensive ANN.
As intimated above, it will also be interesting to see how well such an ANN trained with say data from 30 separate simulated AIDA patients - manages when presented with a completely new case.
Looking ahead, the interest in being able to predict blood glucose levels accurately is based on the fact that if such reliable and accurate BG predictions were possible, then therapy planning opportunities would clearly arise.
Figure 6 summarises the schematic of an ANN blood glucose predictor and ANN therapy optimiser. The ANN predictor enables the predicted blood glucose level at time k+1 (BGLk+1) to be derived from the actual blood glucose at time k (BGLk), and the anticipated diet, exercise and insulin regimen at time k (DEIk). Haque [19] from Brunel University in London, England has also used AIDA to provide blood glucose data to train an artificial neural network (ANN) of human carbohydrate metabolism in type 1 diabetes mellitus. Input parameters for the ANN included the blood glucose level, carbohydrate intake, and insulin regimen at time, t.
The ANN was batch trained with the computer left running for 2 days (approximately 40 hours) to try all the hidden nodes and other system parameters until the predictive error was reduced to about 10%.
Following training, the ANN was tested, first using 50 data sets that the neural network had seen before, and then using a further 50 data sets that the neural network had never seen before. Chang and colleagues [20] have developed an internet-based home monitoring prototype for diabetes care. The strategy of the expert system uses a mapping method with back-propagation training of the neural network. In this network, there are five inputs: (i) present blood glucose level, (ii) carbohydrate intake, and the amounts of (iii) short-, (iv) intermediate- and (v) long-acting insulin.
The function of this approach is to find out patients’ response patterns and try & estimate their blood glucose levels in the near future.
The system was evaluated by Chang and colleagues using test case scenario data obtained from the AIDA diabetes simulator [20]. Yates and Fletcher [21] from the University of Liverpool in England have studied 3 published models of the gut to assess how well they were able to predict the appearance of glucose following the ingestion of a carbohydrate meal. Yates and Fletcher [23] have also written in a more recent report that: “The glycaemic response of an insulin-treated diabetic patient goes through many transitory phases, leading to a steady state glycaemic profile following a change in either insulin regimen or diet. The systemic appearance of glucose was compared from two view points, not only to assess the strategic principle, but also to assess the suitability of the AIDA model.
The new strategic approach was deemed a success, and the AIDA model was found to be "appropriate" [23]. Cobelli and colleagues [24] from the University of Padova in Italy are in the process of developing a new physiological model of glucose-insulin interaction in type 1 diabetes mellitus. Furthermore the new Cobelli model is currently being extended to describe a Type 1 diabetic patient in order to provide a test bed for examining various data analysis techniques and control strategies [28], in much the same way that other researchers highlighted in this section of the Website have been making use of AIDA.
Butler [29], Strachan and colleagues from the Robert Gordon University in Aberdeen, Scotland, U.K. Escreet [30] from Staffordshire University, England has also reviewed the AIDA program, as part of work to develop a more comprehensive diabetes model. Escreet attempted to enhance the AIDA simulation approach by the addition of glycaemic indices for foods and by taking exercise into account - the aim being to provide a more comprehensive model of glucose-insulin interaction in diabetes.
One of the most successful research projects to-date involving AIDA has been the development of a Web-based interactive educational diabetes simulator. This can be accessed via this link and permits AIDA's interactive diabetes simulations to be run from anywhere in the world - from any computer platform (Mac, PC, Linux, UNIX server, etc) - provided it is connected to the Internet and has a graphical display. Since logging of the number of simulations was started in August 1998 - over 155,000 interactive diabetes simulations have been run at 'AIDA on-line' [32].
Various other research projects involving AIDA are currently underway, although these have not yet reported their results.
However, it is very possible that the ventures reported in this Research Section of the Website under-represent the totality of research projects that have actually been making use of the AIDA software. The computer-science literature is full of descriptions of decision-support prototypes which attempt to provide therapeutic advice for patients with diabetes. In support of this - as for patient, relative, carer or health-care professional use - AIDA is freely available via the Web.
A small group of researchers housed at the NASA Independent Verification and Validation Facility (a Software Research Lab which is now a division of Goddard Space Flight Center) have been engaged in utilizing machine learning as an analysis technique, the goal of which is to provide importance hierarchies of parameters in determining changes in the model output. The background to this work is the fact that recent high profile satellite losses have highlighted NASA’s need for quality software.
The technique overviewed below, originally developed for software risk assessment is, however, not limited to that domain. The researchers have presented their original technique with respect to a software project risk model [39], and are now seeking to extend their research into the diabetes domain, utilizing the AIDA model. Their original approach has been using mass simulation data in a Monte-Carlo style analysis of model behaviors, which are learned by a standard Machine Learner technology. In the original software project risk example this concerned such things as the influence of programmer experience on project risk.


Clearly a large amount of data is required for any information which is actually usable in a patient's regimen.
In the diabetes domain it is hoped that [C] will permit actions to be identified that may improve glycaemic control. With closed-loop insulin delivery devices, as with any automated device, malfunction is possible. For this work, the researchers have studied the Bergman and AIDA models in the standard Internal Model Control framework, using a first order model for controller synthesis. While an Internal Model Control approach can provide adequate control of blood glucose levels using such a device, in a separate report the same authors have also reported studying the use of a Model Predictive Control approach [42]. In a separate, but related, piece of work from the same group - Parker and colleagues [43] have developed a model-based predictive control algorithm for blood glucose control in type 1 diabetic patients, using a closed-loop insulin infusion pump. Conmy [44] has described a detailed business plan for utilising telehealth-care technology in the home. This Thesis (from 1999) for its AIDA work focuses exclusively on a single AIDA paper from 1994 [45] which cites earlier validation work of a predecessor of the AIDA knowledge-based system (KBS).
Connected with this, many researchers have hoped to be able to one day develop a computer system that might be able to assist in generating insulin-dosage adjustment advice.
AIDA, being widely available and completely free, could serve as a useful ‘test bed’ for generally establishing how best to evaluate educational medical software programs.
Using the evaluation scheme shown in Figure 9, recent reports [51] would fit under Level 4 (anecdotal evidence including user comments and reviews). For RCT use, a standardised protocol for the evaluation of such diabetes simulation programs has been developed [52,53] and early pilot study (preliminary) results from a small number of patients (n=24) have shown this to be a viable method for formally evaluating programs like AIDA [54]. Nevertheless, a wide range of users in many different parts of the world do seem to have identified AIDA as an accessible source of information about glucose-insulin interaction, and a useful supporting method for diabetes education. In addition, since AIDA moved to its own dedicated Website in October 2000 various more recent research articles have been made available on the Internet.
Once again, this illustrates how placing such material on the Web allows much larger numbers of people access to such research information than would have been the case just through regular libraries and standard hard copy reprint requests. We maintain this 'Research Use' section as a way of highlighting one of the benefits of distributing work, like AIDA, via the Internet. If you are thinking of undertaking a research project - and are wondering whether AIDA might be able to help - please feel free to contact the AIDA authors via the on-line AIDA contact form. Two part special journal issue entitled Application of information technology in clinical diabetes care.
Monitoring and keeping track of your readings can be a hassle at times, but it doesn’t have to be.
Sandham’s supervision made use of a hidden layer of 10 tan-sigmoid neurons to receive inputs directly and then broadcast their outputs to a layer of linear neurons that computed the network output (Figure 3). However given that data were only available from two patients, a further study has been undertaken by Dr. Such prototypes use a wide variety of different computational techniques (see refs [34-38] for an overview). The NASA Software Research Laboratory conducts applied research into advanced software analysis technologies, through tool development, case studies and pilot projects. In this respect the researchers are currently using ‘AIDA on-line’ to generate large quantities of simulated blood glucose data, and require more than 60,000 simulations to provide sufficient entries in their dataset to perform a stable machine learner summarization with which to undertake their further analyses.
These research papers can be accessed completely free-of-charge as portable document format (PDF) files. Neuro-fuzzy system identification based on qualitative models: an application to physiological systems.
Computer simulation of plasma insulin and glucose dynamics after subcutaneous insulin injection. You can use the Excel Blood Glucose Level Chart featured on this page to monitor your glucose readings daily! Sandham using the AIDA diabetes simulator to provide a much larger amount of simulated patient data to more fully train and test the ANN. Within each day is space for three individual readings.The first step in using the chart is to input the first date of your first reading and the remaining corresponding days of the week.
You can add or remove spaces depending upon how you would like to monitor your glucose levels.Once the blood sugar levels have been added to the template, you can move on to the second page of the Excel blood glucose level chart.
It’s critical to be as accurate as possible when placing information in the data page so you will get an accurate graph.
This graph will show your blood sugar levels throughout the day and week.Download: Excel Blood Glucose Level ChartNot what you were looking for?




Blood glucose level measurements
Normal blood sugar for 50 year old male 90s


Comments

  1. 20.08.2016 at 16:22:20


    Stable congestive heart quick-fix food, wait 15 minutes, and blood sugar near normal during.

    Author: kiss_my_90
  2. 20.08.2016 at 22:56:41


    Hypoglycemia depends on how well the individual result on the GCT is abnormal (greater than 140.

    Author: Death_angel