Handbook Of Research On Mathematical Modeling For Smart Healthcare Systems
Download Handbook Of Research On Mathematical Modeling For Smart Healthcare Systems PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Handbook Of Research On Mathematical Modeling For Smart Healthcare Systems book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Handbook of Research on Mathematical Modeling for Smart Healthcare Systems
Advances in healthcare technologies have offered real-time guidance and technical assistance for diagnosis, monitoring, operation, and interventions. The development of artificial intelligence, machine learning, internet of things technology, and smart computing techniques are crucial in today’s healthcare environment as they provide frictionless and transparent financial transactions and improve the overall healthcare experience. This, in turn, has far-reaching effects on economic, psychological, educational, and organizational improvements in the way we work, teach, learn, and provide care. These advances must be studied further in order to ensure they are adapted and utilized appropriately. The Handbook of Research on Mathematical Modeling for Smart Healthcare Systems presents the latest research findings, ideas, innovations, developments, and applications in the field of modeling for healthcare systems. Furthermore, it presents the application of innovative techniques to complex problems in the case of healthcare. Covering a range of topics such as artificial intelligence, deep learning, and personalized healthcare services, this reference work is crucial for engineers, healthcare professionals, researchers, academicians, scholars, practitioners, instructors, and students.
Federated Intelligent System for Healthcare
This practical guide gives valuable insights for integrating advanced technologies in healthcare, empowering researchers to effectively navigate and implement federated systems to enhance patient care. Federated Intelligent Systems for Healthcare: A Practical Guide explores the integration of federated learning and intelligent systems within the healthcare domain. This volume provides an in-depth understanding of how federated systems enhance healthcare practices, detailing their principles, technologies, challenges, and opportunities. Additionally, this book addresses secure and privacy-preserving sharing of medical data, applications of artificial intelligence and machine learning in healthcare, and ethical considerations surrounding the adoption of these advanced technologies. With a focus on practical implementation and real-world use cases, Federated Intelligent Systems for Healthcare: A Practical Guide equips healthcare professionals, researchers, and technology experts with the knowledge needed to navigate the complexities of federated intelligent systems in healthcare and harness their potential to transform patient care and medical advancements. Readers will find the book: Provides cutting-edge research from industry experts to unlock the future of healthcare with innovative insights that embrace federated intelligence and shape the future; Presents novel technologies and conceptual and visionary-based scenarios; Discusses real-world case studies and implementations that illustrate how federated intelligence is practically applied across various healthcare scenarios, from personalized diagnostics to population-level insights; Stands as a pioneer in the exploration of federated intelligent systems in healthcare. Audience Data scientists, IT, healthcare and business professionals working towards innovations in the healthcare sector. The book will be especially helpful to students and educators.
Green AI-Powered Intelligent Systems for Disease Prognosis
Experts in Medicine are under new pressures of advancing their studies while also reducing the impact they leave on the environment. Researchers within the fields of bio-neuro informatics, healthcare, engineering, and medical sciences require a dynamic platform that bridges the realms of academia, science, industry, and innovation. Green AI-Powered Intelligent Systems for Disease Prognosis facilitates a crossroads for a diverse audience interested in these two seldom coalesced concepts. Academicians, scientists, researchers, professionals, decision-makers, and even aspiring scholars all find a space to contribute, collaborate, and learn within the platform that this book provides. The book's thematic coverage is unequivocally compelling; by exploring the intersections of bio-neuro informatics, healthcare, engineering, and medical sciences, it captures the spirit of interdisciplinary research. It delves into well-established domains while also casting a spotlight on emerging trends that have the potential to reshape our understanding of these fields. Two prominent tracks form the backbone of the book's content. The first covers the Bioinformatics and Data Mining of Biological Data (BiDMBD), and unravels the intricacies of biomedical computation, signal analysis, clinical decision support, and health data mining. This approach holds a treasure trove of insights into the mechanisms of health data acquisition, clinical informatics, and the representation of healthcare knowledge. The second covers Biomedical Informatics and is a symposium of computational modeling, genomics, and proteomics. Here, the fusion of data science with medical sciences takes center stage.