Computational Health Informatics for Biomedical Applications  book cover
1st Edition

Computational Health Informatics for Biomedical Applications



  • Available for pre-order on February 20, 2023. Item will ship after March 13, 2023
ISBN 9781774912539
March 13, 2023 Forthcoming by Apple Academic Press
276 Pages 9 Color & 105 B/W Illustrations

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Book Description

The explosion of technology in healthcare in recent years has rapidly changed the healthcare sector. Technologies such as artificial intelligence and machine learning along with the integration of the Internet of Medical Things (IoMT) have evolved to tackle the need for remote healthcare systems, augmenting them in a self-sustainable way. This new volume explores computational tactics as applied to the development of biomedical applications, using artificial intelligence, machine learning, signal analysis, computer-aided design, robotics and automation, biomedical imaging, telemedicine, and other technologies. The book aims to provide a solid framework to provide the modern class of medical gearheads with information on the innovative applications of computational mechanisms for improving and expediting patient-friendly automation in healthcare.

The volume provides an overview of the advancements in modern technology for diagnosing major life-threatening diseases, including using photonic MEMS sensors, biomedical signal processing, 1D photonic crystal-based distributed Bragg reflectors (DBRs), and biosensor chips used to detect foreign bodies, such as cancer cells, or infected stages of blood cells for quick medical diagnosis.

The book discusses employing predictive analysis using AI, ML, and DL for tracking diseases, predicting their progress, and designing tactics as applied to heart disease, coronavirus, and many other ailments. It looks at various machine learning methods, grouping and association rules, vector machine assistance, and evolutionary algorithms. Also discussed is the evolution and implementation of information and communication technologies in healthcare delivery, which hold enormous promise for patients, providers, and payers in future healthcare systems. Other topics include using drones in health centers, such as for drug distribution and other purposes; using powerful artificial intelligence algorithms that can reveal clinically significant information hidden in vast amounts of data; and more.

Computational Health Informatics for Biomedical Applications explores the many important smart technologies that can make healthcare delivery and monitoring faster, more efficient, and less invasive. It will be a valuable resource for those at the forefront of designing and employing advanced smart technologies for improved healthcare services.

Table of Contents

1. Computational Health Informatics: Biomedical Applications 2. Distributed Bragg Reflector Biosensor for Medical Applications 3. Photonic MEMS Sensor for Biomedical Applications 4. Chaotic and Nonlinear Features as EEG Biomarkers for the Diagnosis of Neuropathologies 5. Application of Artificial Intelligence and Deep Learning in Healthcare 6. Heart Disease Prediction Desktop Application Using Supervised Learning 7. Coronavirus Outbreak Prediction Analysis and Corona Virus Detection Through X-Ray Using Machine Learning 8. Numerical Analysis of Bioheat Transfer in Thermal Medicine 9. Evolution of Artificial Intelligence and Deep Learning in Health Care 10. Medication Extender Drone Using CoppeliaSim 11. Big Data and Visualization-Oriented Latency-Aware Smart Health Architecture 12. Signal Processing in Biomedical Applications in the Present and Future Development 13. Emerging Trends in Healthcare and Drug Development 14. Future Directions in Healthcare Research

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Editor(s)

Biography

Aryan Chaudhary is the Research Head and Lead Member of the research project launched by Nijji Healthcare Pvt Ltd. He focuses on implementing technologies such as artificial intelligence, deep learning, IoT (Internet of Things), cognitive technology, and the blockchain to better the healthcare sector. He has published academic papers on public health and digital health in international journals and has participated as a keynote speaker at many international and national conferences. His research includes the integration of IoT and sensor technology for gathering vital signs through one-time/ambulatory monitoring and then the functionality of artificial intelligence and machine learning leading to big data analytics for faster intervention, tracking of prognosis, and research on these vast data for effective clinical research for future development of treatment, drug, pathological tests, and supply systems. He is editor of many books on biomedical science and the Chief Editor of a CRC book series. He also serves as a guest editor of many special issues in reputed journals. He has been awarded with being named Most Inspiring Young Leader in Healthtech Space 2022 by Business Connect and the best project leader at Global Education and Corporate Leadership. He is the senior member of many international associations in science.

Sardar M. N. Islam (Naz), PhD, is currently a Professor at the Institute for Sustainable Industries & Liveable Cities and Lead of the Decision Sciences and Modelling Program at Victoria University, Australia. He is also a Distinguished Visiting Professor of Artificial Intelligence at Sriwijaya University (UnSri), Indonesia, and a Distinguished Visiting Professor (2019–2021) at American University of Ras Al Khaimah (AURAK), United Arab of Emirates. His academic work has gained international acclaim resulting in many honors and awards, visiting and adjunct professorial appointments in different countries, appointments in editorial roles of journals, as a keynote speaker at international conferences in several countries. He has published over 31 scholarly authored academic books in different disciplines; each of these books makes significant scientific contributions to the literature. He has also published about 250 articles, including some in the top leading international journal articles in his specialized research areas.