Personalized Predictive Modeling in Diabetes features state-of-the-art methodologies and algorithmic approaches which have been applied to predictive modeling of glucose concentration, ranging from simple autoregressive models of the CGM time series to multivariate nonlinear regression techniques of machine learning. Developments in the field have been analyzed with respect to: (i) feature set (univariate or multivariate), (ii) regression technique (linear or non-linear), (iii) learning mechanism (batch or sequential), (iv) development and testing procedure and (v) scaling properties. In addition, simulation models of meal-derived glucose absorption and insulin dynamics and kinetics are covered, as an integral part of glucose predictive models. This book will help engineers and clinicians to: select a regression technique which can capture both linear and non-linear dynamics in glucose metabolism in diabetes, and which exhibits good generalization performance under stationary and non-stationary conditions; ensure the scalability of the optimization algorithm (learning mechanism) with respect to the size of the dataset, provided that multiple days of patient monitoring are needed to obtain a reliable predictive model; select a features set which efficiently represents both spatial and temporal dependencies between the input variables and the glucose concentration; select simulation models of subcutaneous insulin absorption and meal absorption; identify an appropriate validation procedure, and identify realistic performance measures. Describes fundamentals of modeling techniques as applied to glucose control Covers model selection process and model validation Offers computer code on a companion website to show implementation of models and algorithms Features the latest developments in the field of diabetes predictive modeling

This contributed volume presents computational models of diabetes that quantify the dynamic interrelationships among key physiological variables implicated in the underlying physiology under a variety of metabolic and behavioral conditions. These variables comprise for example blood glucose concentration and various hormones such as insulin, glucagon, epinephrine, norepinephrine as well as cortisol. The presented models provide a powerful diagnostic tool but may also enable treatment via long-term glucose regulation in diabetics through closed-look model-reference control using frequent insulin infusions, which are administered by implanted programmable micro-pumps. This research volume aims at presenting state-of-the-art research on this subject and demonstrating the potential applications of modeling to the diagnosis and treatment of diabetes. The target audience primarily comprises research and experts in the field but the book may also be beneficial for graduate students.

BackgroundPatients with type 1 diabetes (T1D) make their decisions for insulin delivery from available past and present blood glucose (BG) data and the expected effects on BG of forthcoming meals and activities according to education rules and their own experience. Enriched information on predicted BG glucose evolution could help them in better tuning insulin therapy. CDDIAB studyu2019s objective was to evaluate a new machine learning approach to predicting BG levels of each individual from a collection of personal BG measurements with contextual data.MethodsFourteen patients with T1D (8F/6M, age: 51+/-15, T1D duration: 26+/-17 years, HbA1c: 7.09+/-0.82%), treated by insulin pump (n=11) or multiple daily insulin injections (n=3) volunteered to track BG using FreeStyle Libre (n=12), Enlite (n=1) or Dexcom G4 (n=1) CGM devices and log manually meal intakes and insulin doses for 30 days. Collected data were used to design patient-specific prediction models with 30- to 90-min horizons. The algorithms were initially fitted on a training dataset corresponding to an average of 9 days, using a 5-fold cross-validation method. The remaining days of available data were used to provide an unbiased evaluation of final models.ResultsThe MARD (Mean Absolute Relative Deviation) and the consensus Error Grid Analysis were used to evaluate accuracy of BG predictions for 30- to 90-min horizons, Our results, detailed below, show the MARD and percentage of points in zones A+B on a Parkes EGA:- At 30 minutes: MARD of 6.98%u00b12.0, and 99.93%u00b10.13,- At 60 minutes: MARD of 14.78%u00b13.25, and 98.56%u00b11.00,- At 90 minutes: MARD of 20.78%u00b14.08, and 96.29%u00b12.15.ConclusionPrediction algorithms showed promising results since 99.9, 98.6 and 96.3% of computed BG values were in EGA A+B zones at 30-, 60- and 90-min horizons, respectively. The integration into the training process of collected data by an activity tracker could further improve accuracy in future developments of the algorithm.Integrated inside a mobile application to support decision-making process, this technology could help patients anticipate and avoid upcoming occurrence of hypoglycaemia and hyperglycaemia, in particular during night time. It could also be used on top of an Artificial Pancreas MPC model, allowing for more personalization and better regulation of the system, particularly during unstable phases with rapid glucose changes.

This book constitutes the refereed proceedings of the 17th Conference on Artificial Intelligence in Medicine, AIME 2019, held in Poznan, Poland, in June 2019. The 22 revised full and 31 short papers presented were carefully reviewed and selected from 134 submissions. The papers are organized in the following topical sections: deep learning; simulation; knowledge representation; probabilistic models; behavior monitoring; clustering, natural language processing, and decision support; feature selection; image processing; general machine learning; and unsupervised learning.

"Exercise has numerous well-established benefits, such as decreased risk of cardiovascular disease, improved lipid profile, and overall improved well being. These benefits are especially important to patients with type 1 diabetes, given the increased risk of cardiovascular disease in this population. Despite the established benefits of exercise, moderate intensity aerobic exercise increases the risk of hypoglycemia in individuals with type 1 diabetes, making exercise more difficult in this population. For exercise management in type 1 diabetes, carbohydrate ingestion and insulin reduction are recommended to prevent hypoglycemia. However, due to the large inter-individual variability in glucose responses to exercise, these general recommendations are not always efficient in preventing hypoglycemia. In the present thesis, a personalized closed-loop algorithm based on each patient's glucose response to exercise was developed to reduce the risk of exercise-induced hypoglycemia. The designed algorithm is based on a prediction mathematical model and uses an optimization-based method. After each exercise session, the prediction model is updated by estimating the exercise effect using a least squares algorithm. Given the updated model, an optimization problem is formulated to obtain recommendations of basal rate reduction and carbohydrate intake for the upcoming exercise session. The developed algorithm was evaluated on 100 virtual patients in a computer simulation environment. The results showed that there was a significant reduction in hypoglycemia with the developed algorithm in comparison to the conventional exercise management strategy, without significant increase in time in hyperglycemia. Furthermore, it was shown that when exercise is announced earlier, the algorithm performs better and leads to lower risk of hypoglycemia. The developed algorithm has the potential to facilitate physical activity in type 1 diabetes and thus improve quality of life. Clinical studies to assess the algorithm are warranted"--

Shows the newest developments in the field of multi-parametric model predictive control and optimization and their application for drug delivery systems This book is based on the Modelling, Control and Optimization of Biomedical Systems (MOBILE) project, which was created to derive intelligent computer model-based systems for optimization of biomedical drug delivery systems in the cases of diabetes, anaesthesia, and blood cancer. These systems can ensure reliable and fast calculation of the optimal drug dosage without the need for an online computer—while taking into account the specifics and constraints of the patient model, flexibility to adapt to changing patient characteristics and incorporation of the physician’s performance criteria, and maintaining the safety of the patients. Modelling Optimization and Control of Biomedical Systems covers: mathematical modelling of drug delivery systems; model analysis, parameter estimation, and approximation; optimization and control; sensitivity analysis & model reduction; multi-parametric programming and model predictive control; estimation techniques; physiologically-based patient model; control design for volatile anaesthesia; multiparametric model based approach to intravenous anaesthesia; hybrid model predictive control strategies; Type I Diabetes Mellitus; in vitro and in silico block of the integrated platform for the study of leukaemia; chemotherapy treatment as a process systems application; and more. Introduces readers to the Modelling, Control and Optimization of Biomedical Systems (MOBILE) project Presents in detail the theoretical background, computational tools, and methods that are used in all the different biomedical systems Teaches the theory for multi-parametric mixed-integer programming and explicit optimal control of volatile anaesthesia Provides an overview of the framework for modelling, optimization, and control of biomedical systems This book will appeal to students, researchers, and scientists working on the modelling, control, and optimization of biomedical systems and to those involved in cancer treatment, anaesthsia, and drug delivery systems.

This carefully edited book combines symbolic and sub-symbolic techniques to construct more robust and reliable problem solving models. This volume focused on "Hybrid Artificial Intelligence Systems" contains a collection of papers that were presented at the 2nd International Workshop on Hybrid Artificial Intelligence Systems, held in 12 - 13 November, 2007, Salamanca, Spain.

This open access book comprehensively covers the fundamentals of clinical data science, focusing on data collection, modelling and clinical applications. Topics covered in the first section on data collection include: data sources, data at scale (big data), data stewardship (FAIR data) and related privacy concerns. Aspects of predictive modelling using techniques such as classification, regression or clustering, and prediction model validation will be covered in the second section. The third section covers aspects of (mobile) clinical decision support systems, operational excellence and value-based healthcare. Fundamentals of Clinical Data Science is an essential resource for healthcare professionals and IT consultants intending to develop and refine their skills in personalized medicine, using solutions based on large datasets from electronic health records or telemonitoring programmes. The book’s promise is “no math, no code”and will explain the topics in a style that is optimized for a healthcare audience.

This book constitutes the proceedings of the Second International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2020, which took place in Zhongshan, China, in October 2020. The 49 full and 14 short papers presented were carefully reviewed and selected for inclusion in the book. The papers were organized in topical sections as follows: handwriting and text processing; features and classifiers; deep learning; computer vision and image processing; medical imaging and applications; and forensic studies and medical diagnosis.