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| blood glucose data analysis: Exploratory Data Analysis: An Introduction to Data Analysis Using SAS Patricia Cerrito, 2007-12-01 This is an introductory text on how to investigate datasets. It is intended to be a practical text for those who need to research large datasets. Therefore, it does not follow the standard contents for more typical introductory statistics textbooks. When you complete the material, you will be able to work with your data using data visualization and regression in order to make sense of it, and to use your findings to make decisions. The book makes use of the statistical software, SAS, and its menu system SAS Enterprise Guide. This can be used as a stand alone text, or as a supplementary text to a more standard course. There are some datasets to accompany this text. ID# 1640751, Data for Exploratory Data Analysis. |
| blood glucose data analysis: Smart Grids and Big Data Analytics for Smart Cities Chun Sing Lai, Loi Lei Lai, Qi Hong Lai, 2020-10-31 This book provides a comprehensive introduction to different elements of smart city infrastructure - smart energy, smart water, smart health, and smart transportation - and how they work independently and together. Theoretical development and practical applications are presented, along with related standards, recommended practices, and professional guidelines. Throughout the book, diagrams and case studies are provided that demonstrate the systems presented, and extensive use of scenarios helps readers better grasp how smart grids, the Internet of Things, big data analytics, and trading models can improve road safety, healthcare, smart water management, and a low-carbon economy. A must-read for practicing engineers, consultants, regulators, utility operators, and environmentalists involved in smart city development, the book will also appeal to city planners and designers, as well as upper-level undergraduate and graduate students studying energy, environmental science, technology, economics, signal processing, information science, and power engineering. |
| blood glucose data analysis: An Introduction to Statistical Genetic Data Analysis Melinda C. Mills, Nicola Barban, Felix C. Tropf, 2020-02-18 A comprehensive introduction to modern applied statistical genetic data analysis, accessible to those without a background in molecular biology or genetics. Human genetic research is now relevant beyond biology, epidemiology, and the medical sciences, with applications in such fields as psychology, psychiatry, statistics, demography, sociology, and economics. With advances in computing power, the availability of data, and new techniques, it is now possible to integrate large-scale molecular genetic information into research across a broad range of topics. This book offers the first comprehensive introduction to modern applied statistical genetic data analysis that covers theory, data preparation, and analysis of molecular genetic data, with hands-on computer exercises. It is accessible to students and researchers in any empirically oriented medical, biological, or social science discipline; a background in molecular biology or genetics is not required. The book first provides foundations for statistical genetic data analysis, including a survey of fundamental concepts, primers on statistics and human evolution, and an introduction to polygenic scores. It then covers the practicalities of working with genetic data, discussing such topics as analytical challenges and data management. Finally, the book presents applications and advanced topics, including polygenic score and gene-environment interaction applications, Mendelian Randomization and instrumental variables, and ethical issues. The software and data used in the book are freely available and can be found on the book's website. |
| blood glucose data analysis: Computer Methods Part A , 2009-03-10 The combination of faster, more advanced computers and more quantitatively oriented biomedical researchers has recently yielded new and more precise methods for the analysis of biomedical data. These better analyses have enhanced the conclusions that can be drawn from biomedical data, and they have changed the way that experiments are designed and performed. This volume, along with previous and forthcoming 'Computer Methods' volumes for the Methods in Enzymology serial, aims to inform biomedical researchers about recent applications of modern data analysis and simulation methods as applied to biomedical research. |
| blood glucose data analysis: Data Analysis and Related Applications, Volume 1 Konstantinos N. Zafeiris, Christos H. Skiadas, Yiannis Dimotikalis, Alex Karagrigoriou, Christiana Karagrigoriou-Vonta, 2022-08-17 The scientific field of data analysis is constantly expanding due to the rapid growth of the computer industry and the wide applicability of computational and algorithmic techniques, in conjunction with new advances in statistical, stochastic and analytic tools. There is a constant need for new, high-quality publications to cover the recent advances in all fields of science and engineering. This book is a collective work by a number of leading scientists, computer experts, analysts, engineers, mathematicians, probabilists and statisticians who have been working at the forefront of data analysis and related applications. The chapters of this collaborative work represent a cross-section of current concerns, developments and research interests in the above scientific areas. The collected material has been divided into appropriate sections to provide the reader with both theoretical and applied information on data analysis methods, models and techniques, along with related applications. |
| blood glucose data analysis: Data Analysis for Chemistry D. Brynn Hibbert, J. Justin Gooding, 2006 Annotation. Definitions, Questions, and Useful Functions: Where to Find Things and What To Do1. Introduction2. Describing Data3. Hypothesis Testing4. Analysis of Variance5. Calibration. |
| blood glucose data analysis: Handbook of Diabetes Technology Yves Reznik, 2019-01-31 This book covers the main fields of diabetes management through applied technologies. The different chapters include insulin therapy through basic insulin injection therapy, external and implantable insulin pumps and the more recent approaches such as sensor augmented pumps and close-loop systems. Islet transplantation is also described through its technical aspects and clinical evaluation. Glucose measurement through blood glucose meters and continuous glucose monitoring systems are comprehensively explained. Educational tools including videogames and software dedicated to diabetes management are depicted. Lastly, Telemedicine systems devoted to data transmission, telemonitoring and decision support systems are described and their use for supporting health systems are summarized. This book will help professionals involved in diabetes management understanding the contribution of diabetes technologies for promoting the optimization of glucose control and monitoring. This volume will be helpful in current clinical practice for diabetes management and also beneficial to students. |
| blood glucose data analysis: Intelligent Data Analytics for Bioinformatics and Biomedical Systems Neha Sharma, Korhan Cengiz, Prasenjit Chatterjee, 2024-10-11 The book analyzes the combination of intelligent data analytics with the intricacies of biological data that has become a crucial factor for innovation and growth in the fast-changing field of bioinformatics and biomedical systems. Intelligent Data Analytics for Bioinformatics and Biomedical Systems delves into the transformative nature of data analytics for bioinformatics and biomedical research. It offers a thorough examination of advanced techniques, methodologies, and applications that utilize intelligence to improve results in the healthcare sector. With the exponential growth of data in these domains, the book explores how computational intelligence and advanced analytic techniques can be harnessed to extract insights, drive informed decisions, and unlock hidden patterns from vast datasets. From genomic analysis to disease diagnostics and personalized medicine, the book aims to showcase intelligent approaches that enable researchers, clinicians, and data scientists to unravel complex biological processes and make significant strides in understanding human health and diseases. This book is divided into three sections, each focusing on computational intelligence and data sets in biomedical systems. The first section discusses the fundamental concepts of computational intelligence and big data in the context of bioinformatics. This section emphasizes data mining, pattern recognition, and knowledge discovery for bioinformatics applications. The second part talks about computational intelligence and big data in biomedical systems. Based on how these advanced techniques are utilized in the system, this section discusses how personalized medicine and precision healthcare enable treatment based on individual data and genetic profiles. The last section investigates the challenges and future directions of computational intelligence and big data in bioinformatics and biomedical systems. This section concludes with discussions on the potential impact of computational intelligence on addressing global healthcare challenges. Audience Intelligent Data Analytics for Bioinformatics and Biomedical Systems is primarily targeted to professionals and researchers in bioinformatics, genetics, molecular biology, biomedical engineering, and healthcare. The book will also suit academicians, students, and professionals working in pharmaceuticals and interpreting biomedical data. |
| blood glucose data analysis: Big Data Analytics for Cyber-Physical System in Smart City Mohammed Atiquzzaman, Neil Yen, Zheng Xu, 2020-12-17 This book gathers a selection of peer-reviewed papers presented at the second Big Data Analytics for Cyber-Physical System in Smart City (BDCPS 2020) conference, held in Shanghai, China, on 28–29 December 2020. The contributions, prepared by an international team of scientists and engineers, cover the latest advances made in the field of machine learning, and big data analytics methods and approaches for the data-driven co-design of communication, computing, and control for smart cities. Given its scope, it offers a valuable resource for all researchers and professionals interested in big data, smart cities, and cyber-physical systems. |
| blood glucose data analysis: Sensor Data Analysis and Management A. Suresh, R. Udendhran, M. S. Irfan Ahmed, 2021-11-11 Discover detailed insights into the methods, algorithms, and techniques for deep learning in sensor data analysis Sensor Data Analysis and Management: The Role of Deep Learning delivers an insightful and practical overview of the applications of deep learning techniques to the analysis of sensor data. The book collects cutting-edge resources into a single collection designed to enlighten the reader on topics as varied as recent techniques for fault detection and classification in sensor data, the application of deep learning to Internet of Things sensors, and a case study on high-performance computer gathering and processing of sensor data. The editors have curated a distinguished group of perceptive and concise papers that show the potential of deep learning as a powerful tool for solving complex modelling problems across a broad range of industries, including predictive maintenance, health monitoring, financial portfolio forecasting, and driver assistance. The book contains real-time examples of analyzing sensor data using deep learning algorithms and a step-by-step approach for installing and training deep learning using the Python keras library. Readers will also benefit from the inclusion of: A thorough introduction to the Internet of Things for human activity recognition, based on wearable sensor data An exploration of the benefits of neural networks in real-time environmental sensor data analysis Practical discussions of supervised learning data representation, neural networks for predicting physical activity based on smartphone sensor data, and deep-learning analysis of location sensor data for human activity recognition An analysis of boosting with XGBoost for sensor data analysis Perfect for industry practitioners and academics involved in deep learning and the analysis of sensor data, Sensor Data Analysis and Management: The Role of Deep Learning will also earn a place in the libraries of undergraduate and graduate students in data science and computer science programs. |
| blood glucose data analysis: Intelligent Data Analysis in Medicine and Pharmacology Nada Lavrač, Elpida Keravnou-Papailiou, Blaz Zupan, 2012-12-06 Intelligent data analysis, data mining and knowledge discovery in databases have recently gained the attention of a large number of researchers and practitioners. This is witnessed by the rapidly increasing number of submissions and participants at related conferences and workshops, by the emergence of new journals in this area (e.g., Data Mining and Knowledge Discovery, Intelligent Data Analysis, etc.), and by the increasing number of new applications in this field. In our view, the awareness of these challenging research fields and emerging technologies has been much larger in industry than in medicine and pharmacology. The main purpose of this book is to present the various techniques and methods that are available for intelligent data analysis in medicine and pharmacology, and to present case studies of their application. Intelligent Data Analysis in Medicine and Pharmacology consists of selected (and thoroughly revised) papers presented at the First International Workshop on Intelligent Data Analysis in Medicine and Pharmacology (IDAMAP-96) held in Budapest in August 1996 as part of the 12th European Conference on Artificial Intelligence (ECAI-96), IDAMAP-96 was organized with the motivation to gather scientists and practitioners interested in computational data analysis methods applied to medicine and pharmacology, aimed at narrowing the increasing gap between excessive amounts of data stored in medical and pharmacological databases on the one hand, and the interpretation, understanding and effective use of stored data on the other hand. Besides the revised Workshop papers, the book contains a selection of contributions by invited authors. The expected readership of the book is researchers and practitioners interested in intelligent data analysis, data mining, and knowledge discovery in databases, particularly those who are interested in using these technologies in medicine and pharmacology. Researchers and students in artificial intelligence and statistics should find this book of interest as well. Finally, much of the presented material will be interesting to physicians and pharmacologists challenged by new computational technologies, or simply in need of effectively utilizing the overwhelming volumes of data collected as a result of improved computer support in their daily professional practice. |
| blood glucose data analysis: Exploratory Data Analytics for Healthcare R. Lakshmana Kumar, R. Indrakumari, B. Balamurugan, Achyut Shankar, 2021-12-23 Exploratory data analysis helps to recognize natural patterns hidden in the data. This book describes the tools for hypothesis generation by visualizing data through graphical representation and provides insight into advanced analytics concepts in an easy way. The book addresses the complete data visualization technologies workflow, explores basic and high-level concepts of computer science and engineering in medical science, and provides an overview of the clinical scientific research areas that enables smart diagnosis equipment. It will discuss techniques and tools used to explore large volumes of medical data and offers case studies that focus on the innovative technological upgradation and challenges faced today. The primary audience for the book includes specialists, researchers, graduates, designers, experts, physicians, and engineers who are doing research in this domain. |
| blood glucose data analysis: Evidence-based Management of Diabetes Giten Vora,, John Buse,, 2012-10-01 The clinical management of patients with diabetes is rapidly evolving. Evidence-based Management of Diabetes provides a succinct summary of a range of topics, including areas where there is already well developed evidence for a particular treatment, but also those areas where the evidence is perhaps doubtful or there is some associated controversy or ambiguity. Where possible throughout the book treatment recommendations are given based on the available evidence and practice guidelines. The book also highlights the gaps in evidence where further research is needed. In the practice of diabetes care, there are many issues influencing practitoners currently. This book addresses many of the most pertinent issues concerning delivery of diabetes care. The authors are internationally renowned experts in the field of diabetes care who successfully and succinctly present state-of-the-art reviews based on the medical evidence designed to help the clinician be as best informed as possible in the care of patients with diabetes. |
| blood glucose data analysis: New Perspectives in Statistical Modeling and Data Analysis Salvatore Ingrassia, Roberto Rocci, Maurizio Vichi, 2011-06-29 This volume provides recent research results in data analysis, classification and multivariate statistics and highlights perspectives for new scientific developments within these areas. Particular attention is devoted to methodological issues in clustering, statistical modeling and data mining. The volume also contains significant contributions to a wide range of applications such as finance, marketing, and social sciences. The papers in this volume were first presented at the 7th Conference of the Classification and Data Analysis Group (ClaDAG) of the Italian Statistical Society, held at the University of Catania, Italy. |
| blood glucose data analysis: Big Data Analytics for Healthcare Pantea Keikhosrokiani, 2022-05-19 Big Data Analytics and Medical Information Systems presents the valuable use of artificial intelligence and big data analytics in healthcare and medical sciences. It focuses on theories, methods and approaches in which data analytic techniques can be used to examine medical data to provide a meaningful pattern for classification, diagnosis, treatment, and prediction of diseases. The book discusses topics such as theories and concepts of the field, and how big medical data mining techniques and applications can be applied to classification, diagnosis, treatment, and prediction of diseases. In addition, it covers social, behavioral, and medical fake news analytics to prevent medical misinformation and myths. It is a valuable resource for graduate students, researchers and members of biomedical field who are interested in learning more about analytic tools to support their work. - Presents theories, methods and approaches in which data analytic techniques are used for medical data - Brings practical information on how to use big data for classification, diagnosis, treatment, and prediction of diseases - Discusses social, behavioral, and medical fake news analytics for medical information systems |
| blood glucose data analysis: Fundamentals of Clinical Data Science Pieter Kubben, Michel Dumontier, Andre Dekker, 2018-12-21 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. |
| blood glucose data analysis: Glucose Monitoring Devices Chiara Fabris, Boris Kovatchev, 2020-06-02 Glucose Monitoring Devices: Measuring Blood Glucose to Manage and Control Diabetes presents the state-of-the-art regarding glucose monitoring devices and the clinical use of monitoring data for the improvement of diabetes management and control. Chapters cover the two most common approaches to glucose monitoring–self-monitoring blood glucose and continuous glucose monitoring–discussing their components, accuracy, the impact of use on quality of glycemic control as documented by landmark clinical trials, and mathematical approaches. Other sections cover how data obtained from these monitoring devices is deployed within diabetes management systems and new approaches to glucose monitoring. This book provides a comprehensive treatment on glucose monitoring devices not otherwise found in a single manuscript. Its comprehensive variety of topics makes it an excellent reference book for doctoral and postdoctoral students working in the field of diabetes technology, both in academia and industry. - Presents a comprehensive approach that spans self-monitoring blood glucose devices, the use of continuous monitoring in the artificial pancreas, and intraperitoneal glucose sensing - Provides a high-level descriptions of devices, as well as detailed mathematical descriptions of methods and techniques - Written by experts in the field with vast experience in the field of diabetes and diabetes technology |
| blood glucose data analysis: Connecting Medical Informatics and Bio-informatics Rolf Engelbrecht, 2005 A variety of topics of bio-informatics, including both medical and bio-medical informatics are addressed by MIE. The main theme in this publication is the development of connections between bio-informatics and medical informatics. Tools and concepts from both disciplines can complement each other. |
| blood glucose data analysis: Introductory Time Series with R Paul S.P. Cowpertwait, Andrew V. Metcalfe, 2009-05-28 This book gives you a step-by-step introduction to analysing time series using the open source software R. Each time series model is motivated with practical applications, and is defined in mathematical notation. Once the model has been introduced it is used to generate synthetic data, using R code, and these generated data are then used to estimate its parameters. This sequence enhances understanding of both the time series model and the R function used to fit the model to data. Finally, the model is used to analyse observed data taken from a practical application. By using R, the whole procedure can be reproduced by the reader. All the data sets used in the book are available on the website http://staff.elena.aut.ac.nz/Paul-Cowpertwait/ts/. The book is written for undergraduate students of mathematics, economics, business and finance, geography, engineering and related disciplines, and postgraduate students who may need to analyse time series as part of their taught programme or their research. |
| blood glucose data analysis: Machine Learning Advanced Dynamic Omics Data Analysis for Precision Medicine Tao Zeng, Tao Huang, Chuan Lu, 2020-03-30 |
| blood glucose data analysis: Proceedings of Data Analytics and Management Abhishek Swaroop, Zdzislaw Polkowski, Sérgio Duarte Correia, Bal Virdee, 2024-01-13 This book includes original unpublished contributions presented at the International Conference on Data Analytics and Management (ICDAM 2023), held at London Metropolitan University, London, UK, during June 2023. The book covers the topics in data analytics, data management, big data, computational intelligence, and communication networks. The book presents innovative work by leading academics, researchers, and experts from industry which is useful for young researchers and students. The book is divided into four volumes. |
| blood glucose data analysis: Advances in Intelligent Data Analysis. Reasoning about Data Xiaohui Liu, Michael R. Berthold, 1997-07-23 This book constitutes the refereed proceedings of the Second International Symposium on Intelligent Data Analysis, IDA-97, held in London, UK, in August 1997. The volume presents 50 revised full papers selected from a total of 107 submissions. Also included is a keynote, Intelligent Data Analysis: Issues and Opportunities, by David J. Hand. The papers are organized in sections on exploratory data analysis, preprocessing and tools; classification and feature selection; medical applications; soft computing; knowledge discovery and data mining; estimation and clustering; data quality; qualitative models. |
| blood glucose data analysis: Real World Health Care Data Analysis Douglas Faries, Xiang Zhang, Zbigniew Kadziola, Uwe Siebert, Felicitas Kuehne, Robert L Obenchain, Josep Maria Haro, 2020-01-15 Discover best practices for real world data research with SAS code and examples Real world health care data is common and growing in use with sources such as observational studies, patient registries, electronic medical record databases, insurance healthcare claims databases, as well as data from pragmatic trials. This data serves as the basis for the growing use of real world evidence in medical decision-making. However, the data itself is not evidence. Analytical methods must be used to turn real world data into valid and meaningful evidence. Real World Health Care Data Analysis: Causal Methods and Implementation Using SAS brings together best practices for causal comparative effectiveness analyses based on real world data in a single location and provides SAS code and examples to make the analyses relatively easy and efficient. The book focuses on analytic methods adjusted for time-independent confounding, which are useful when comparing the effect of different potential interventions on some outcome of interest when there is no randomization. These methods include: propensity score matching, stratification methods, weighting methods, regression methods, and approaches that combine and average across these methods methods for comparing two interventions as well as comparisons between three or more interventions algorithms for personalized medicine sensitivity analyses for unmeasured confounding |
| blood glucose data analysis: Applied Categorical and Count Data Analysis Wan Tang, Hua He, Xin Tu, 2012-06-04 Developed from the authors' graduate-level biostatistics course, Applied Categorical and Count Data Analysis explains how to perform the statistical analysis of discrete data, including categorical and count outcomes. The authors describe the basic ideas underlying each concept, model, and approach to give readers a good grasp of the fundamentals o |
| blood glucose data analysis: Official Gazette of the United States Patent and Trademark Office , 2005 |
| blood glucose data analysis: IoT Technologies and Wearables for HealthCare Venere Ferraro, |
| blood glucose data analysis: Advanced Deep Learning Applications in Big Data Analytics Bouarara, Hadj Ahmed, 2020-10-16 Interest in big data has swelled within the scholarly community as has increased attention to the internet of things (IoT). Algorithms are constructed in order to parse and analyze all this data to facilitate the exchange of information. However, big data has suffered from problems in connectivity, scalability, and privacy since its birth. The application of deep learning algorithms has helped process those challenges and remains a major issue in today’s digital world. Advanced Deep Learning Applications in Big Data Analytics is a pivotal reference source that aims to develop new architecture and applications of deep learning algorithms in big data and the IoT. Highlighting a wide range of topics such as artificial intelligence, cloud computing, and neural networks, this book is ideally designed for engineers, data analysts, data scientists, IT specialists, programmers, marketers, entrepreneurs, researchers, academicians, and students. |
| blood glucose data analysis: Advances in Artificial Intelligence Kunal Pal, Bala Chakravarthy Neelapu, J. Sivaraman, 2024-05-21 Artificial Intelligence in health care has become one of the best assisting techniques for clinicians in proper diagnosis and surgery. In biomedical applications, artificial intelligence algorithms are explored for bio-signals such as electrocardiogram (ECG/ EKG), electrooculogram (EOG), electromyogram (EMG), electroencephalogram (EEG), blood pressure, heart rate, nerve conduction, etc., and for bio-imaging modalities, such as Computed Tomography (CT), Cone-Beam Computed Tomography (CBCT), MRI (Magnetic Resonance Imaging), etc. Advancements in Artificial intelligence and big data has increased the development of innovative medical devices in health care applications. Recent Advances in Artificial Intelligence: Medical Applications provides an overview of artificial intelligence in biomedical applications including both bio-signals and bio-imaging modalities. The chapters contain a mathematical formulation of algorithms and their applications in biomedical field including case studies. Biomedical engineers, advanced students, and researchers can use this book to apply their knowledge in artificial intelligence-based processes to biological signals, implement mathematical models and advanced algorithms, as well as develop AI-based medical devices. - Covers the recent advancements of artificial intelligence in healthcare, including case studies on how this technology can be used - Provides an understanding of the design of experiments to validate the developed algorithms - Presents an understanding of the versatile application of artificial intelligence in bio-signal and bio-image processing techniques |
| blood glucose data analysis: Driving Smart Medical Diagnosis Through AI-Powered Technologies and Applications Khang, Alex, 2024-02-26 Academic scholars face the daunting challenge of keeping pace with the rapid evolution of innovative technologies. The emergence of AI-driven solutions, deep learning frameworks, and medical robotics introduces a complex terrain, demanding in-depth understanding and analysis. As scholars navigate the intricacies of patient hate speech detection, cardiovascular diseases AI-CDSS, and the revolution in medical diagnostics, a pressing need arises for comprehensive insights that bridge the gap between theoretical knowledge and practical applications. Driving Smart Medical Diagnosis Through AI-Powered Technologies and Applications serves as a solution in this era of transformative healthcare and addresses these challenges head-on. It unravels the complexities surrounding the implementation of AI in healthcare, offering in-depth discussions on the latest breakthroughs. From unraveling the mysteries of AI-driven cataract detection to exploring the implications of decentralized mammography classification, the book is a valuable resource that equips scholars with the knowledge to navigate this innovative landscape. |
| blood glucose data analysis: The Research Process in Sport, Exercise and Health Rich Neil, Sheldon Hanton, Scott Fleming, Kylie Wilson, 2013-12-04 What are the challenges and potential pitfalls of real research? What decision-making process is followed by successful researchers? The Research Process in Sport, Exercise and Health fills an important gap in the research methods literature. Conventional research methods textbooks focus on theory and descriptions of hypothetical techniques, while the peer-reviewed research literature is mainly concerned with discussion of data and the significance of results. In this book, a team of successful researchers from across the full range of sub-disciplines in sport, exercise and health discuss real pieces of research, describing the processes they went through, the decisions that they made, the problems they encountered and the things they would have done differently. As a result, the book goes further than any other in bringing the research process to life, helping students identify potential issues and problems with their own research right at the beginning of the process. The book covers the whole span of the research process, including: identifying the research problem justifying the research question choosing an appropriate method data collection and analysis identifying a study’s contribution to knowledge and/or applied practice disseminating results. Featuring real-world studies from sport psychology, biomechanics, sports coaching, ethics in sport, sports marketing, health studies, sport sociology, performance analysis, and strength and conditioning, the book is an essential companion for research methods courses or dissertations on any sport or exercise degree programme. |
| blood glucose data analysis: Handbook of Research on Biomimicry in Information Retrieval and Knowledge Management Hamou, Reda Mohamed, 2017-12-15 In the digital age, modern society is exposed to high volumes of multimedia information. In efforts to optimize this information, there are new and emerging methods of information retrieval and knowledge management leading to higher efficiency and a deeper understanding of this data. The Handbook of Research on Biomimicry in Information Retrieval and Knowledge Management is a critical scholarly resource that examines bio-inspired classes that solve computer problems. Featuring coverage on a broad range of topics such as big data analytics, bioinformatics, and black hole optimization, this book is geared towards academicians, practitioners, and researchers seeking current research on the use of biomimicry in information and knowledge management. |
| blood glucose data analysis: Understanding Clinical Data Analysis Ton J. Cleophas, Aeilko H. Zwinderman, 2016-08-23 This textbook consists of ten chapters, and is a must-read to all medical and health professionals, who already have basic knowledge of how to analyze their clinical data, but still, wonder, after having done so, why procedures were performed the way they were. The book is also a must-read to those who tend to submerge in the flood of novel statistical methodologies, as communicated in current clinical reports, and scientific meetings. In the past few years, the HOW-SO of current statistical tests has been made much more simple than it was in the past, thanks to the abundance of statistical software programs of an excellent quality. However, the WHY-SO may have been somewhat under-emphasized. For example, why do statistical tests constantly use unfamiliar terms, like probability distributions, hypothesis testing, randomness, normality, scientific rigor, and why are Gaussian curves so hard, and do they make non-mathematicians getting lost all the time? The book will cover the WHY-SOs. |
| blood glucose data analysis: Advances in Digital Health and Medical Bioengineering Hariton-Nicolae Costin, |
| blood glucose data analysis: Handbook of Optical Sensing of Glucose in Biological Fluids and Tissues Valery V. Tuchin, 2008-12-22 Although noninvasive, continuous monitoring of glucose concentration in blood and tissues is one of the most challenging areas in medicine, a wide range of optical techniques has recently been designed to help develop robust noninvasive methods for glucose sensing. For the first time in book form, the Handbook of Optical Sensing of Glucose in Biological Fluids and Tissues analyzes trends in noninvasive optical glucose sensing and discusses its impact on tissue optical properties. This handbook presents methods that improve the accuracy in glucose prediction based on infrared absorption spectroscopy, recent studies on the influence of acute hyperglycemia on cerebral blood flow, and the correlation between diabetes and the thermo-optical response of human skin. It examines skin glucose monitoring by near-infrared spectroscopy (NIR), fluorescence-based glucose biosensors, and a photonic crystal contact lens sensor. The contributors also explore problems of polarimetric glucose sensing in transparent and turbid tissues as well as offer a high-resolution optical technique for noninvasive, continuous, and accurate blood glucose monitoring and glucose diffusion measurement. Written by world-renowned experts in biomedical optics and biophotonics, this book gives a complete, state-of-the-art treatise on the design and applications of noninvasive optical methods and instruments for glucose sensing. |
| blood glucose data analysis: Proceedings of ASEAN-Australian Engineering Congress (AAEC2022) Chung Siung Choo, Basil T. Wong, Khairul Hafiz Bin Sharkawi, Daniel Kong, 2023-12-19 This book presents the proceedings of the ASEAN-Australian Engineering Congress (AAEC2022), held as a virtual event, 13–15 July 2022 with the theme “Engineering Solutions in the Age of Digital Disruption”. The book presents selected papers covering scientific research in the field of Engineering Computing, Network, Communication and Cybersecurity, Artificial Intelligence & Machine Learning, Materials Science & Manufacturing, Automation and Sensors, Smart Energy & Cities, Simulation & Optimisation and other Industry 4.0 related Technologies. The book appeals to researchers, academics, scientists, students, engineers and practitioners who are interested in the latest developments and applications related to addressing the Fourth Industrial Revolution (IR4.0). |
| blood glucose data analysis: Clinical Analytics and Data Management for the DNP Martha L. Sylvia, PhD, MBA, RN, Mary F. Terhaar, PhD, RN, ANEF, FAAN, 2018-03-28 Praise for the First Edition: “DNP students may struggle with data management, since their projects are not research, but quality improvement, and this book covers the subject well. I recommend it for DNP students for use during their capstone projects. Score: 98, 5 Stars --Doody's Medical Reviews This is the only text to deliver the strong data management knowledge and skills that are required competencies for all DNP students. It enables readers to design data tracking and clinical analytics in order to rigorously evaluate clinical innovations/programs for improving clinical outcomes, and to document and analyze change. The second edition is greatly expanded and updated to address major changes in our health care environment. Incorporating faculty and student input, it now includes modalities such as SPSS, Excel, and Tableau to address diverse data management tasks. Eleven new chapters cover the use of big data analytics, ongoing progress towards value-based payment, the ACA and its future, shifting of risk and accountability to hospitals and clinicians, advancement of nursing quality indicators, and new requirements for Magnet certification. The text takes the DNP student step by step through the complete process of data management from planning to presentation, and encompasses the scope of skills required for students to apply relevant analytics to systematically and confidently tackle the clinical interventions data obtained as part of the DNP student project. Of particular value is a progressive case study illustrating multiple techniques and methods throughout the chapters. Sample data sets and exercises, along with objectives, references, and examples in each chapter, reinforce information. Key Features: Provides extensive content for rigorously evaluating DNP innovations/projects Takes DNP students through the complete process of data management from planning through presentation Includes a progressive case study illustrating multiple techniques and methods Offers very specific examples of application and utility of techniques Delivers sample data sets, exercises, PowerPoint slides and more, compiled in Supplemental Materials and an Instructor Manual |
| blood glucose data analysis: Fractal and Multifractal Facets in the Structure and Dynamics of Physiological Systems and Applications to Homeostatic Control, Disease Diagnosis and Integrated Cyber-Physical Platforms Paul Bogdan, Plamen Ch. Ivanov, Andras Eke, 2020-06-25 Widespread chronic diseases (e.g., heart diseases, diabetes and its complications, stroke, cancer, brain diseases) constitute a significant cause of rising healthcare costs and pose a significant burden on quality-of-life for many individuals. Despite the increased need for smart healthcare sensing systems that monitor / measure patients’ body balance, there is no coherent theory that facilitates the modeling of human physiological processes and the design and optimization of future healthcare cyber-physical systems (HCPS). The HCPS are expected to mine the patient’s physiological state based on available continuous sensing, quantify risk indices corresponding to the onset of abnormality, signal the need for critical medical intervention in real-time by communicating patient’s medical information via a network from individual to hospital, and most importantly control (actuate) vital health signals (e.g., cardiac pacing, insulin level, blood pressure) within personalized homeostasis. To prevent health complications, maintain good health and/or avoid fatal conditions calls for a cross-disciplinary approach to HCPS design where recent statistical-physics inspired discoveries done by collaborations between physicists and physicians are shared and enriched by applied mathematicians, control theorists and bioengineers. This critical and urgent multi-disciplinary approach has to unify the current state of knowledge and address the following fundamental challenges: One fundamental challenge is represented by the need to mine and understand the complexity of the structure and dynamics of the physiological systems in healthy homeostasis and associated with a disease (such as diabetes). Along the same lines, we need rigorous mathematical techniques for identifying the interactions between integrated physiologic systems and understanding their role within the overall networking architecture of healthy dynamics. Another fundamental challenge calls for a deeper understanding of stochastic feedback and variability in biological systems and physiological processes, in particular, and for deciphering their implications not only on how to mathematically characterize homeostasis, but also on defining new control strategies that are accounting for intra- and inter-patient specificity – a truly mathematical approach to personalized medicine. Numerous recent studies have demonstrated that heart rate variability, blood glucose, neural signals and other interdependent physiological processes demonstrate fractal and non-stationary characteristics. Exploiting statistical physics concepts, numerous recent research studies demonstrated that healthy human physiological processes exhibit complex critical phenomena with deep implications for how homeostasis should be defined and how control strategies should be developed when prolonged abnormal deviations are observed. In addition, several efforts have tried to connect these fractal characteristics with new optimal control strategies that implemented in medical devices such as pacemakers and artificial pancreas could improve the efficiency of medical therapies and the quality-of-life of patients but neglecting the overall networking architecture of human physiology. Consequently, rigorously analyzing the complexity and dynamics of physiological processes (e.g., blood glucose and its associated implications and interdependencies with other physiological processes) represents a fundamental step towards providing a quantifiable (mathematical) definition of homeostasis in the context of critical phenomena, understanding the onset of chronic diseases, predicting deviations from healthy homeostasis and developing new more efficient medical therapies that carefully account for the physiological complexity, intra- and inter-patient variability, rather than ignoring it. This Research Topic aims to open a synergetic and timely effort between physicians, physicists, applied mathematicians, signal processing, bioengineering and biomedical experts to organize the state of knowledge in mining the complexity of physiological systems and their implications for constructing more accurate mathematical models and designing QoL-aware control strategies implemented in the new generation of HCPS devices. By bringing together multi-disciplinary researchers seeking to understand the many aspects of human physiology and its complexity, we aim at enabling a paradigm shift in designing future medical devices that translates mathematical characteristics in predictable mathematical models quantifying not only the degree of homeostasis, but also providing fundamentally new control strategies within the personalized medicine era. |
| blood glucose data analysis: Unity in Diversity and the Standardisation of Clinical Pharmacy Services Elida Zairina, Junaidi Khotib, Chrismawan Ardianto, Syed Azhar Syed Sulaiman, Charles D. Sands III, Timothy E. Welty, 2017-12-22 Unity in Diversity and the Standardisation of Clinical Pharmacy Services represents the proceedings of the 17th Asian Conference on Clinical Pharmacy (ACCP 2017), held 28—30 July 2017 in Yogyakarta, Indonesia. The primary aim of ACCP 2017 was to bring together experts from all fields of clinical pharmacy to facilitate the discussion and exchange of research ideas and results. The conference provided a forum for the dissemination of knowledge and exchange of experiences. As such, it brought together clinical pharmacy scholars, pharmacy practitioners, policy makers and stakeholders from all areas of pharmacy society and all regions of the world to share their research, knowledge, experiences, concepts, examples of good practice, and critical analysis with their international peers. This year also marks the celebration of 20 years of ACCP. Central themes of the conference and contributed papers were Clinical Pharmacy, Social and Administrative Pharmacy, Pharmacy Education, Pharmacoeconomics, Pharmacoepidemiology, Complementary and Alternative Medicine (CAM) and a number of related topics in the field of Pharmacy. |
| blood glucose data analysis: Complete Nurse's Guide to Diabetes Care Belinda B Childs, Marjorie Cypress, Geralyn Spollett, 2017-08-10 The third edition of the Complete Nurse's Guide to Diabetes Care is a comprehensive resource for all nurses who work with diabetes patients. Inside, readers will find expert advice on: The evolution of the nurse's roles in diabetes care and education Recent research on complications and associated diseases Practical issues, such as the effects of anxiety, depression, and polypharmacy Updated guidelines for nutrition therapy and physical activity How diabetes affects women, children, and the elderly An extensive resources section featuring contact information for useful organizations and essential patient care The Complete Nurses Guide to Diabetes Care, 3rd Edition, gives nurses the tools they need to give quality care to the person with diabetes. |
| blood glucose data analysis: Published Scientific Papers of the National Institutes of Health National Institutes of Health (U.S.), 1990 |
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regulation to control blood glucose levels, oral medication, and insulin injection, and all of these treatments should rely on blood glucose measurement. Diabetic patients are encouraged to …
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and statistical algorithms for retrospective analysis of CGM data exists. The objective of the present review is to summa-rize existing data on (1) statistical packages to retrospectively …
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Abstract. The management of blood glucose levels is critical in the care of Type 1 diabetes subjects. In extremes, high or low lev-els of blood glucose are fatal. To avoid such adverse …
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standard deviation (SD)), High Blood Glucose Index (HBGI) and Low Blood Glucose Index (LBGI) at baseline (t 0), week 2-4 (t 1) and month 3-6 (t 2) were analyzed. Baseline data (t 0) was …
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breaks down lactose into glucose and galactose. These two simpler sugars, or monosaccharides, are easily … Blood Glucose Data Analysis Educator Materials Students infer whether someone …
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