Healthcare Systems and Health Informatics
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This book covers the fundamentals of IoT and healthcare systems for carrying out system architectures, protocols, wearable devices, and interoperability. It explores major challenges in artificial intelligence (AI) and smart computing in resource-constrained IoT-based applications along with cost, energy efficiency, and the availability of quality service. Healthcare Systems and Health Informatics: Using Internet of Things explores the role of AI and smart computing in health informatics and healthcare with an emphasis on clinical data management and analysis for precise prediction and prompt action. It presents cutting-edge tracking, monitoring, real-time assistance, and security for IoT in healthcare and broadly discusses wearable sensors and IoT devices and their role in smart living assistance. The book goes on to describe a system model and architecture for a clear picture of energy conservation-based IoT in healthcare and explains the challenges and opportunities with IoT-based healthcare industries. A study of the threats and impacts, along with the need for information security, is also included. The chapters are written by experts in the field, and this book provides a comprehensive description of the important aspects of IoT and health from a beginner- to advanced-level perspective and is ideal for researchers, academicians, students, persons in industry, technologists, and entrepreneurs.
Artificial Intelligence
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Artificial intelligence (AI) has the potential to drastically change healthcare, including the optimisation of time and materials for clinical tasks (efficiency), and more accurate diagnose detection (effectiveness). However, these clinical-based AI applications are still in very early development stages. Investigating the wide range of applications of AI in both community and hospital care settings, and along the patient care pathway, the chapters here focus on applications capable of contributing to the workflow and management of the activities to be provided. The impact of AI in solving the main issues of modern medicine is discussed, such as reorganization of services towards patient-centricity, management of task shifting and skills mix, greater competitiveness between public and private sector, and financing mechanisms. Difficulties that may arise during the implementation of AI in an organization are addressed, including practical solutions, providing a practical guide for managers, clinical leaders and policy makers. Artificial Intelligence: Why and How it is Revolutionizing Healthcare Management identifies a roadmap for the appropriate introduction of artificial intelligence in healthcare organizations that responds to the need of decision-makers and managers to have a clear picture of how to move in the developing field of AI.
Computer Systems for Healthcare and Medicine
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The development of modern civilization leads to us having to solve new problems which did not exist before. The contemporary world faces a great challenge of aging societies, where the increasing number of citizens requires constant medical attention. To ensure safety and wellbeing of elderly people, patients in hospitals and disabled persons, advanced technologies can be implemented. These include both sophisticated data acquisition systems and data processing algorithms, aiming at the constant and discreet monitoring of persons whilst raising alarm if immediate attention is required.Computer Systems for Healthcare and Medicine presents a novel look at the introduced problems, including proposed solutions in the form of automated data acquisition and processing systems, which were tested in various environments. Characteristic features include a wide range of sensors used to monitor the situation of the person, and accurate decision making algorithms, often based on the computational intelligence domain. Technical topics discussed in the book include application for the healthcare of the following:Infrared sensorsMEMSUltra wideband radarsDeep learningDecision treesArtificial neural networksGabor filtersDecision support systems
Artificial Intelligence in Healthcare Industry
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This book presents a systematic evolution of artificial intelligence (AI), its applications, challenges and solutions in the field of healthcare. The book mainly covers the foundations and various methods of learning in artificial intelligence with its application in healthcare industry. This book provides a comprehensive introduction to data analysis using AI as a tool in the generation, normalization and analysis of healthcare data in association with several evaluation techniques and accuracy measurements. The book is divided into three major sections describing the basic foundations of AI and its associated algorithms, history of artificial intelligence in healthcare, recent developments and several modeling techniques for the same. The last section of the book provides insights into several implementations and methods of evaluation and accuracy prediction for healthcare analysis in AI. Extensive use of data for analysis and prediction using several technologies has transformed thelives of normal people indirectly effecting our process to communicate, learn, work and socialize within the society. Thus, the book also provides an insight into the ethics of AI that is very vital in the process of implementation and evaluation of healthcare data. The book provides an organized analysis to a considerable part of data in a digitized society. In view of this, it covers the theory, methodology, perfection and verification of empirical work for health-related data processing. Particular attention is devoted to in-depth experiments and applications.
Blockchain and EHR
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This book is divided into seven chapters that provide detailed explanations of blockchain technology and its applications in the healthcare industries. The first chapter covers the security of transactions using blockchain technology. Every transaction is recorded in a block, which acts as a record book and helps in maintaining transparency and security. Chapter 2 delves into the Smart Contract, Hash Function, and Coinbase Transaction Structure in depth. Chapter 3 investigates the current state of blockchain advancement and crypto applications across various regions. Chapter 4 looks at the electronic voting system that takes advantage of blockchain technology's high level of security. Chapter 5 talks about WEB3.0, which uses blockchain as a medium to work, reducing the factor of data loss or leaks, as blockchain is fundamentally unalterable making them more secure for data storage. Chapter 6 presents the views on healthcare data management based on blockchain technology as well as prospective study areas for exchanging medical data via blockchain. Chapter 7 discusses how to generate tokens through a step-by-step procedure.
Artificial Intelligence for Healthcare Applications and Management
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Artificial Intelligence for Healthcare Applications and Management introduces application domains of various AI algorithms across healthcare management. Instead of discussing AI first and then exploring its applications in healthcare afterward, the authors attack the problems in context directly, in order to accelerate the path of an interested reader toward building industrial-strength healthcare applications. Readers will be introduced to a wide spectrum of AI applications supporting all stages of patient flow in a healthcare facility. The authors explain how AI supports patients throughout a healthcare facility, including diagnosis and treatment recommendations needed to get patients from the point of admission to the point of discharge while maintaining quality, patient safety, and patient/provider satisfaction. AI methods are expected to decrease the burden on physicians, improve the quality of patient care, and decrease overall treatment costs. Current conditions affected by COVID-19 pose new challenges for healthcare management and learning how to apply AI will be important for a broad spectrum of students and mature professionals working in medical informatics. This book focuses on predictive analytics, health text processing, data aggregation, management of patients, and other fields which have all turned out to be bottlenecks for the efficient management of coronavirus patients.
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Authored and curated by Prof. Givens of the College of Health Sciences
Edited by JRossi of DeVry Univeristy Library