Data-driven Modeling for Diabetes PDF Download
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Author: Vasilis Marmarelis
Publisher: Springer Science & Business
ISBN: 3642544649
Category : Technology & Engineering
Languages : en
Pages : 241
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Book Description
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.
Author: Vasilis Marmarelis
Publisher: Springer Science & Business
ISBN: 3642544649
Category : Technology & Engineering
Languages : en
Pages : 241
Get Book Here
Book Description
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.
Author: Eleni I. Georga
Publisher: Academic Press
ISBN: 0128051469
Category : Computers
Languages : en
Pages : 253
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Book Description
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
Author: Pieter Kubben
Publisher: Springer
ISBN: 3319997130
Category : Medical
Languages : en
Pages : 219
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Book Description
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.
Author: David Riaño
Publisher: Springer
ISBN: 303021642X
Category : Computers
Languages : en
Pages : 431
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Book Description
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.
Author: Rajeev Mathur
Publisher: Springer Nature
ISBN: 9811639159
Category : Technology & Engineering
Languages : en
Pages : 350
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Book Description
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.
Author: Igor Kononenko
Publisher: Horwood Publishing
ISBN: 9781904275213
Category : Computers
Languages : en
Pages : 484
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Book Description
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.
Author: Wayne Katon
Publisher: John Wiley & Sons
ISBN: 1119957478
Category : Medical
Languages : en
Pages : 197
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Book Description
In recent years, there has been a growing awareness of the multiple interrelationships between depression and various physical diseases. The WPA is providing an update of currently available evidence on these interrelationships by the publication of three books, dealing with the comorbidity of depression with diabetes, heart disease and cancer. Depression is a frequent and serious comorbid condition in diabetes, which adversely affects quality of life and the long-term prognosis. Co-occurrent depression presents peculiar clinical challenges, making both conditions harder to manage. Depression and Diabetes is the first book devoted to the interaction between these common disorders. World leaders in diabetes, depression and public health synthesize current evidence, including some previously unpublished data, in a concise, easy-to-read format. They provide an overview of the epidemiology, pathogenesis, medical costs, management, and public health and cultural implications of the comorbidity between depression and diabetes. The book describes how the negative consequences of depression in diabetes could be avoided, given that effective depression treatments for diabetic patients are available. Its practical approach makes the book ideal for all those involved in the management of these patients: psychiatrists, psychologists, diabetologists, general practitioners, diabetes specialist nurses and mental health nurses.
Author: Jay Skyler
Publisher: Springer Science & Business Media
ISBN: 1461410282
Category : Medical
Languages : en
Pages : 424
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Book Description
This handbook is an invaluable resource for improving the management of diabetes. Chapters cover the fundamentals, including epidemiology, history and physical examination, and functional evaluations. Diabetes in children, adolescents, adults, and geriatrics are addressed. Differential diagnosis is emphasized, and evidence-based guidelines and patient-specific considerations aid the reader with injury evaluation and care. Notably, the book highlights the importance of understanding diabetic symptoms when determining the source of illnesses. In addition, the text presents the spectrum of treatment options for diabetes. The book is complete with appendices that explain the evidence-based approach used throughout and the science behind therapeutic modalities.
Author: Robert Thomson
Publisher: Springer
ISBN: 3030217418
Category : Computers
Languages : en
Pages : 304
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Book Description
This book constitutes the proceedings of the 12th International Conference on Social, Cultural, and Behavioral Modeling, SBP-BRiMS 2019, held in Washington, DC, USA, in July 2019. The total of 28 papers presented in this volume was carefully reviewed and selected from 72 submissions. The papers in this volume show, people, theories, methods and data from a wide number of disciplines including computer science, psychology, sociology, communication science, public health, bioinformatics, political science, and organizational science. Numerous types of computational methods are used include, but not limited to, machine learning, language technology, social network analysis and visualization, agent-based simulation, and statistics.
Author: Edward Curry
Publisher: Springer Nature
ISBN: 3030681769
Category : Computers
Languages : en
Pages : 399
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Book Description
This open access book presents the foundations of the Big Data research and innovation ecosystem and the associated enablers that facilitate delivering value from data for business and society. It provides insights into the key elements for research and innovation, technical architectures, business models, skills, and best practices to support the creation of data-driven solutions and organizations. The book is a compilation of selected high-quality chapters covering best practices, technologies, experiences, and practical recommendations on research and innovation for big data. The contributions are grouped into four parts: · Part I: Ecosystem Elements of Big Data Value focuses on establishing the big data value ecosystem using a holistic approach to make it attractive and valuable to all stakeholders. · Part II: Research and Innovation Elements of Big Data Value details the key technical and capability challenges to be addressed for delivering big data value. · Part III: Business, Policy, and Societal Elements of Big Data Value investigates the need to make more efficient use of big data and understanding that data is an asset that has significant potential for the economy and society. · Part IV: Emerging Elements of Big Data Value explores the critical elements to maximizing the future potential of big data value. Overall, readers are provided with insights which can support them in creating data-driven solutions, organizations, and productive data ecosystems. The material represents the results of a collective effort undertaken by the European data community as part of the Big Data Value Public-Private Partnership (PPP) between the European Commission and the Big Data Value Association (BDVA) to boost data-driven digital transformation.
