Application of Advanced Optimization Techniques for Healthcare Analytics, 1st Edition, is an excellent compilation of current and advanced optimization techniques which can readily be applied to solve different hospital management problems. The healthcare system is currently a topic of significant investigation to make life easier for those who are disabled, old, or sick, as well as for young children. The emphasis of the healthcare system has evolved throughout time due to several emerging beneficial technologies, such as personal digital assistants (PDAs), data mining, the internet of things, metaheuristics, fog computing, and cloud computing. Metaheuristics are strong technology for tackling several optimization problems in various fields, especially healthcare systems. The primary advantage of metaheuristic algorithms is their ability to find a better solution to a healthcare problem and their ability to consume as little time as possible. In addition, metaheuristics are more flexible compared to several other optimization techniques. These algorithms are not related to a specific optimization problem but could be applied to any optimization problem by making some small adaptations to become suitable to tackle it. The successful outcome of this book will enable a decision-maker or practitioner to pick a suitable optimization approach when making decisions to schedule patients under crowding environments with minimized human errors.

This book provides different approaches used to analyze, draw attention, and provide an understanding of the advancements in the optimization field across the globe. It brings all of the latest methodologies, tools, and techniques related to optimization and industrial engineering into a single volume to build insights towards the latest advancements in various domains. Applications of Advanced Optimization Techniques in Industrial Engineering includes the basic concept of optimization, techniques, and applications related to industrial engineering. Concepts are introduced in a sequential way along with explanations, illustrations, and solved examples. The book goes on to explore applications of operations research and covers empirical properties of a variety of engineering disciplines. It presents network scheduling, production planning, industrial and manufacturing system issues, and their implications in the real world. The book caters to academicians, researchers, professionals in inventory analytics, business analytics, investment managers, finance firms, storage-related managers, and engineers working in engineering industries and data management fields.

Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data

From the Preface: Collectively, the chapters in this book address application domains including inpatient and outpatient services, public health networks, supply chain management, and resource constrained settings in developing countries. Many of the chapters provide specific examples or case studies illustrating the applications of operations research methods across the globe, including Africa, Australia, Belgium, Canada, the United Kingdom, and the United States. Chapters 1-4 review operations research methods that are most commonly applied to health care operations management including: queuing, simulation, and mathematical programming. Chapters 5-7 address challenges related to inpatient services in hospitals such as surgery, intensive care units, and hospital wards. Chapters 8-10 cover outpatient services, the fastest growing part of many health systems, and describe operations research models for primary and specialty care services, and how to plan for patient no-shows. Chapters 12 – 16 cover topics related to the broader integration of health services in the context of public health, including optimizing the location of emergency vehicles, planning for mass vaccination events, and the coordination among different parts of a health system. Chapters 17-18 address supply chain management within hospitals, with a focus on pharmaceutical supply management, and the challenges of managing inventory for nursing units. Finally, Chapters 19-20 provide examples of important and emerging research in the realm of humanitarian logistics.

Optimization techniques have been widely adopted to implement various data mining algorithms. In addition to well-known Support Vector Machines (SVMs) (which are based on quadratic programming), different versions of Multiple Criteria Programming (MCP) have been extensively used in data separations. Since optimization based data mining methods differ from statistics, decision tree induction, and neural networks, their theoretical inspiration has attracted many researchers who are interested in algorithm development of data mining. Optimization based Data Mining: Theory and Applications, mainly focuses on MCP and SVM especially their recent theoretical progress and real-life applications in various fields. These include finance, web services, bio-informatics and petroleum engineering, which has triggered the interest of practitioners who look for new methods to improve the results of data mining for knowledge discovery. Most of the material in this book is directly from the research and application activities that the authors’ research group has conducted over the last ten years. Aimed at practitioners and graduates who have a fundamental knowledge in data mining, it demonstrates the basic concepts and foundations on how to use optimization techniques to deal with data mining problems.

Blockchain and artificial intelligence (AI) techniques play a crucial role in dealing with large amounts of heterogeneous, multi-scale, and multi-modal data coming from the internet of things (IoT) infrastructures. Therefore, further discussion on how the fusion of blockchain, IoT, and AI allows the design of models, mathematical models, methodologies, algorithms, evaluation benchmarks, and tools to address challenging problems related to health informatics, healthcare, and wellbeing is required. Contemporary Applications of Data Fusion for Advanced Healthcare Informatics covers the integration of IoT and AI to tackle applications in smart healthcare and discusses the efficient means to collect, monitor, control, optimize, model, and predict healthcare data using blockchain, AI, and IoT. The book also considers the advantages and improvements in the smart healthcare field, in which ubiquitous computing and traditional computational methods alone are often inadequate. Covering key topics such as disruptive technology, electronic health records, and medical data, this premier reference source is ideal for computer scientists, nurses, doctors, industry professionals, researchers, academicians, scholars, practitioners, instructors, and students.

Leverage health data into insight! Applied Health Analytics and Informatics Using SAS describes health anamatics, a result of the intersection of data analytics and health informatics. Healthcare systems generate nearly a third of the world’s data, and analytics can help to eliminate medical errors, reduce readmissions, provide evidence-based care, demonstrate quality outcomes, and add cost-efficient care. This comprehensive textbook includes data analytics and health informatics concepts, along with applied experiential learning exercises and case studies using SAS Enterprise MinerTM within the healthcare industry setting. Topics covered include: Sampling and modeling health data – both structured and unstructured Exploring health data quality Developing health administration and health data assessment procedures Identifying future health trends Analyzing high-performance health data mining models Applied Health Analytics and Informatics Using SAS is intended for professionals, lifelong learners, senior-level undergraduates, graduate-level students in professional development courses, health informatics courses, health analytics courses, and specialized industry track courses. This textbook is accessible to a wide variety of backgrounds and specialty areas, including administrators, clinicians, and executives. This book is part of the SAS Press program.

This book covers the technology of digital image processing in various fields with big data and their applications. Readers will understand various technologies and strategies used in digital image processing as well as handling big data, using machine-learning techniques. This book will help to improve the skills of students and researchers in such fields as engineering, agriculture, and medical imaging. There is a need to be able to understand and analyse the latest developments of digital image technology. As such, this book will cover: · Applications such as biomedical science and biometric image processing, content-based image retrieval, remote sensing, pattern recognition, shape and texture analysis · New concepts in color interpolation to produce the full color from the sub-pattern bare pattern color prevalent in today's digital cameras and other imaging devices · Image compression standards that are needed to serve diverse applications · Applications of remote sensing, medical science, traffic management, education, innovation, and analysis in agricultural design and image processing · Both soft and hard computing approaches at great length in relation to major image processing tasks · The direction and development of current and future research in many areas of image processing · A comprehensive bibliography for additional research (integrated within the framework of the book) This book focuses not only on theoretical and practical knowledge in the field but also on the traditional and latest tools and techniques adopted in image processing and data science. It also provides an indispensable guide to a wide range of basic and advanced techniques in the fields of image processing and data science.

This book aims to apply state-of-the-art advanced computational intelligence frameworks in healthcare. It presents recent and real-life applications of computationally intelligent healthcare. It also discusses problems and solutions to remote healthcare and emergency healthcare services. Healthcare Analytics and Advanced Computational Intelligence highlights modern ambient intelligence-enabled healthcare models along with advanced topics like quantum computing in healthcare and cryptomedical systems. Healthcare Analytics and Advanced Computational Intelligence examines designing the latest medical systems and models that will allow the societal acceptance of ambiance computing in healthcare, medical imaging, health analytics, machine intelligence, sensory computing, medical data analytics, disease detection, telemedicine, and their applications. It includes diverse case studies dealing with various clinical-based applications. These intelligent models are primarily structured to deal with complex real-world issues in clinical data analytics, by means of state-of-the-art techniques with general implementation, domain-specific solutions, or hybrid methods which integrate computational intelligence with conventional statistical methods. The book is written for researchers and academicians in diverse areas. Engineers from technical disciplines such as computer engineering are likely to purchase the book. Various sub-streams such as machine learning, big data analytics, healthcare analytics, and computational intelligence will find the book significant for their curriculum.

This book presents the proceedings of International Conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2020. The conference provides an interdisciplinary forum for researchers, professional engineers and scientists, educators and technologists to discuss, debate and promote research and technology in the upcoming areas of computing, information, communication and their applications. The book discusses these emerging research areas, providing a valuable resource for researchers and practicing engineers alike.