This compendium brings together leading researchers in the fields of Intelligent Systems and healthcare aiming at medical engineers, healthcare managers and computer scientists worldwide. This book is an overview of intelligent paradigms and strategic investments that might payoff for the healthcare enterprise. Specifically, the reader will get ideas for efficiency enhancements for improving effectiveness and quality of care and for increasing patient safety. "Advanced Intelligent Paradigms in Healthcare" straddles technologic topics from DNA processing and automating medical second opinions in the lab, to telemedicine and chat spaces for rural patient outreach, among many others. In terms of management concerns, this book also explores systems approaches such as automated clinical guidelines, institutional workflow management, and best practices and lessons learned with actual applications.
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
This book consists of various contributions in conjunction with the keywords “reasoning” and “intelligent systems”, which widely covers theoretical to practical aspects of intelligent systems. Therefore, it is suitable for researchers or graduate students who want to study intelligent systems generally.
Hybrid Computational Intelligence: Challenges and Utilities is a comprehensive resource that begins with the basics and main components of computational intelligence. It brings together many different aspects of the current research on HCI technologies, such as neural networks, support vector machines, fuzzy logic and evolutionary computation, while also covering a wide range of applications and implementation issues, from pattern recognition and system modeling, to intelligent control problems and biomedical applications. The book also explores the most widely used applications of hybrid computation as well as the history of their development. Each individual methodology provides hybrid systems with complementary reasoning and searching methods which allow the use of domain knowledge and empirical data to solve complex problems.
The revolution in healthcare as well as demand for efficient real-time healthcare services are driving the progression of edge computing, AI-mediated techniques, deep learning, and IoT applications for healthcare industries and cloud computing. Edge computing helps to meet the demand for newer and more sophisticated healthcare systems that are more personalized and that match the speed of modern life. With applications of edge computing, automated intelligence and intuitions are incorporated into existing healthcare analysis tools for identifying, forecasting, and preventing high-risk diseases. Reconnoitering the Landscape of Edge Intelligence in Healthcare provides comprehensive research on edge intelligence technology with the emphasis on application in the healthcare industry. It covers all the various areas of edge intelligence for data analysis in healthcare, looking at the emerging technologies such as AI-based techniques, machine learning, IoT, cloud computing, and deep learning with illustrations of the design, implementation, and management of smart and intelligent healthcare systems. Chapters showcase the advantages and highlights of the adoption of the intelligent edge models toward smart healthcare infrastructure. The book also addresses the increased need for a high level of medical data security while transferring real-time data to cloud-based architecture, a matter of prime concern for both patient and doctor. Topics include edge intelligence for wearable sensor technologies and their applications for health monitoring, the various edge computing techniques for disease prediction, e-health services and e-security solutions through IoT devices that aim to improve the quality of care for transgender patients, smart technology in ambient assisted living, the role of edge intelligence in limiting virus spread during pandemics, neuroscience in decoding and analysis of visual perception from the neural patterns and visual image reconstruction, and more. The technology addressed include energy aware cross-layer routing protocol (ECRP), OMKELM-IDS technique, graphical user interface (GUI), IOST (an ultra-fast, decentralized blockchain platform), etc. This volume will be helpful to engineering students, research scholars, and manufacturing industry professionals in the fields of engineering applications initiatives on AI, machine learning, and deep learning techniques for edge computing.
Intelligent paradigms are increasingly finding their ways in the design and development of decision support systems. This book presents a sample of recent research results from key researchers. The contributions include: Introduction to intelligent systems in decision making - A new method of ranking intuitionistic fuzzy alternatives - Fuzzy rule base model identification by bacterial memetic algorithms - Discovering associations with uncertainty from large databases - Dempster-Shafer structures, monotonic set measures and decision making - Interpretable decision-making models - A general methodology for managerial decision making - Supporting decision making via verbalization of data analysis results using linguistic data summaries - Computational intelligence in medical decisions making. This book is directed to the researchers, graduate students, professors, decision makers and to those who are interested to investigate intelligent paradigms in decision making.
There has been a growing concern for the improvement of pharmaceutical services provided by healthcare institutions. This concern is also shared by other stakeholders including patients, regulatory organizations, pharmaceutical companies, insurance companies, and research institutions. Advancing Pharmaceutical Processes and Tools for Improved Health Outcomes presents research-based perspectives on the pharmaceutical industry in today’s digitally-fueled world. Focusing on technological innovations for pharmaceutical applications as well as current trends in the industry, this publication is ideally designed for use by pharmacists, medical professionals, administrators in the medical field, health insurance professionals, researchers, and graduate-level students.
This book constitutes the refereed proceedings of the 11th Conference on Artificial Intelligence in Medicine in Europe, AIME 2007, held in Amsterdam, The Netherlands in July 2007. The 28 revised full papers and 38 revised short papers presented were carefully reviewed and selected from 137 submissions. The papers are organized in topical sections on agent-based systems, temporal data mining, machine learning and knowledge discovery, text mining, natural language processing and generation, ontologies, decision support systems, applications of AI-based image processing techniques, protocols and guidelines, as well as workflow systems.
Through the use of ICT tools, such as the internet, portals, and telecommunication devices, the quality of healthcare has improved in local and global health; aiding in the development of a sustainable economy. Handbook of Research on ICTs and Management Systems for Improving Efficiency in Healthcare and Social Care brings together a valuable research collection on ICT elements needed to improve communication and collaboration between global health institutes, public and private organizations, and foundations. Highlighting the adoption and success factors in the development of technologies for healthcare, this book is essential for IT professionals, technology solution providers, researchers, and students interested in technology and its relationship with healthcare and social services.
Contains papers which were presented at the 10th anniversary of the International Conference of the ACIA (CCIA'07) in Sant Julia de Loria (Andorra), October 25-26th, 2007. This book is intended for those computer scientists or engineers who are interested in artificial intelligence."
