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.

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 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 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.

This book includes best selected, high-quality research papers presented at International Conference on Data Driven Computing and IoT (DDCIoT 2021) organized jointly by Geetanjali Institute of Technical Studies (GITS), Udaipur, and Rajasthan Technical University, Kota, India, during March 20–21, 2021. This book presents influential ideas and systems in the field of data driven computing, information technology, and intelligent systems.

Fuzzy sets have long been employed to handle imprecise and uncertain information in the real world, but their limitations in dealing with incomplete and inconsistent data led to the emergence of neutrosophic sets. In this thought-provoking book, titled Data-Driven Modelling with Fuzzy Sets: A Neutrosophic Perspective, the authors delve into the theories and extensive applications of neutrosophic sets, ranging from neutrosophic graphs to single-valued trapezoidal neutrosophic sets and their practical implications in knowledge management, including student learning assessment, academic performance evaluation, and technical article screening. This comprehensive resource is intended to benefit mathematicians, physicists, computer experts, engineers, scholars, practitioners, and students seeking to deepen their understanding of neutrosophic sets and their practical applications in diverse fields. This book comprises 11 chapters that provide a thorough examination of neutrosophic set theory and its extensions. Each chapter presents valuable insights into various aspects of data-driven modeling with neutrosophic sets and explores their applications in different domains. The book covers a wide range of topics. The specific topics covered in the book include neutrosophic submodules, applications of neutrosophic sets, solutions to differential equations with neutrosophic uncertainty, cardinalities of neutrosophic sets, neutrosophic cylindrical coordinates, applications to graphs and climatic analysis, neutrosophic differential equation approaches to growth models, neutrosophic aggregation operators for decision making, and similarity measures for Fermatean neutrosophic sets. The diverse contributions from experts in the field, coupled with the constructive feedback from reviewers, ensure the book's high quality and relevance. This book presents a qualitative assessment of big data in the education sector using linguistic quadripartitioned single-valued neutrosophic soft sets showcases application of n-cylindrical fuzzy neutrosophic sets in education using neutrosophic affinity degree and neutrosophic similarity index covers scientific evaluation of student academic performance using single-valued neutrosophic Markov chain illustrates multi-granulation single-valued neutrosophic probabilistic rough sets for teamwork assessment examines estimation of distribution algorithms based on multiple-attribute group decision-making to evaluate teaching quality With its wealth of knowledge, this book aims to inspire further research and innovation in the field of neutrosophic sets and their extensions, providing a valuable resource for scholars, practitioners, and students alike.

Good data mining practice for business intelligence (the art of turning raw software into meaningful information) is demonstrated by the many new techniques and developments in the conversion of fresh scientific discovery into widely accessible software solutions. Written as an introduction to the main issues associated with the basics of machine learning and the algorithms used in data mining, this text is suitable foradvanced undergraduates, postgraduates and tutors in a wide area of computer science and technology, as well as researchers looking to adapt various algorithms for particular data mining tasks. A valuable addition to libraries and bookshelves of the many companies who are using the principles of data mining to effectively deliver solid business and industry solutions.