Deep Learning Approaches for Healthcare Data Analysis and Decision Making

Ashish Bagwari, Shivendra Dubey, Jorge Luis Victória Barbosa, Ciro Rodriguez, Albena Mihovska (Herausgeber)

Buch | Softcover

400 Seiten

2026
Academic Press Inc (Verlag)
978-0-443-43798-4 (ISBN)

Infos

Deep Learning Approaches for Healthcare Data Analysis and Decision Making demystifies complex data-driven technologies, providing a clear framework for integrating advanced analytics into healthcare practices. With a focus on practical applications, the authors present a comprehensive digital transformation methodology that empowers readers to tackle the multifaceted challenges of healthcare data management. By leveraging deep learning techniques, readers will learn to analyze vast datasets, identify critical patterns, and develop predictive models that enhance diagnosis and treatment strategies while ensuring compliance with stringent data regulations. The book also addresses the pressing need for ethical AI practices, emphasizing patient privacy and data security.

Real-world case studies illustrate how to implement personalized healthcare solutions and foster interdisciplinary collaboration, breaking down silos in knowledge and practice. Moreover, it explores innovative business models for sustainable AI integration, offering actionable insights for healthcare providers. This resource equips professionals with the tools to drive innovation, improve patient outcomes, and navigate the complexities of digital transformation in healthcare, making it a must-read for anyone at the intersection of technology and healthcare.

Autor

Ashish Bagwari is currently working as Head for the Department of Electronics and Communication Engineering at WIT Dehradun, Uttarakhand Technical University (State Government Technical University), Dehradun, India. He has more than 14.0 years of experience in industry, academics, and research. He received the Best WIT Faculty award in 2013 and 2015 and Best Project Guide Award in 2015. Dr. Bagwari has been awarded by the Corps of Electrical and Mechanical Engineers Prize from the Institution of Engineers, India (IEI) in December 2015. Also, received Outstanding Scientist Award 2021 from VDGOOD Technology, Chennai, India in November 2021, Dr. A.P.J. Abdul Kalam Life Time Achievement National Award 2022 from NISED, Bangalore, India in June 2022, “Best Teacher Award-2023” and “UTU Best Researcher Award-2024” from Veer Madho Singh Bhandari Uttarakhand Technical University, Dehradun on September 05, 2023, and 2024 respectively. Also, listed among the top 2% of scientists globally for 2024 according to Stanford University, USA and Elsevier, Netherland lists. Shivendra Dubey received the Bachalor of Engineering degree in Computer Science and Engineering from the RKDF Institute of Science & Technology, Bhopal, India in 2010, Master of Technology degree in Computer Science & Engineering from the Radharaman Institute of Technology & Science, Bhopal, India, in 2014 and the PhD degree in Computer Science and Engineering is about to completed from Jaypee University of Engineering and Technology, Guna, MP, India. He has 3 SCI, 1 SCOPUS, 8 conference publications with proceeding in SCOPUS, and more than 15 articles published in international journals. He has 2 book chapter publications. He has 3 Indian patent publications and 2 Australian patent publications with grant. Jorge Barbosa received his BS degrees in Data Processing Technology (1990) and Electrical Engineering (1991) from the Catholic University of Pelotas, Brazil. He obtained his MS and Ph.D. degrees in Computer Science from the Federal University of Rio Grande do Sul (UFRGS), Brazil, in 1996 and 2002, respectively. He conducted post-doctoral studies at Sungkyunkwan University (SKKU, Suwon, South Korea, 2016) and University of California Irvine (UCI, Irvine, USA, 2020). Nowadays, he is a full professor of Applied Computing Graduate Program (PPGCA) at the University of Vale do Rio dos Sinos (UNISINOS), São Leopoldo, Brazil. Additionally, he is a researcher of productivity at CNPq (the Brazilian Council for Scientific and Technological Development) and head of the Mobile Computing Laboratory (MobiLab/UNISINOS). His research interests are Ubiquitous Computing, Ambient Intelligence, Big Data, Internet of Things (IoT), Machine Learning,, Applied Computing in Health, Accessibility, Learning, Industry and Agriculture. Role and amount of work done for this book- Editor role, and he will prepare some chapters for the editor book, also preparing the Artificial Intelligence (AI) related works i.e. will work on section IV to VIII. Ciro Rodriguez is a Professor at the National University Mayor de San Marcos, Lima, Peru. Ciro Rodriguez is associated with the Department of Software Engineering at National University Mayor de San Marcos and associated with the Department of Informatics Engineering and Electronics at National University Federico Villarreal. He has completed his Doctoral studies in System Engineering and has advanced studies at the Institute of Theoretical Physics ICTP of Italy, in the United States Particle Accelerator School USPAS, and Information Technology Development Policy Studies Korea Telecom KT in South Korea. His research interests include Artificial Intelligence, Health-Social Welfare, Environment, Cybersecurity, and photonics. He has published over 100 research articles in reputed journals indexed in Scopus, WoS, and IEEE, and filed two patents in engineering fields. Recently published the book "Variables in the research methodology". Role and amount of work done for this book- Editor role, and will prepare some chapters for the editor book, also preparing the section II and III. Albena Mihovska holds a PhD degree in EEng (2008) from Aalborg University (AAU), Aalborg, Denmark. She has a strong academic research track record of more than 20 years, with positions as an Associate Professor at the Dept. of Electronic Systems at AAU (until 2017), Denmark and later as an Associate Professor in Digital Technologies at the Dept. of Business Development and Technology at Aarhus University (AU), Denmark. Currently, she holds positions as a CTO, SmartAvatar BV, Netherlands and as a Research Director at CTIF Global Capsule (CGC) Foundation, Skagen, Denmark, of which she is a Founding Member. Further, she is a Senior Research Expert at the Laboratory for Intelligent Communication and Infrastructure at Research & Development & Innovation Consortium (RDIC), Sofia, Bulgaria. Hugo Herrero Antón de Vez is an active surgeon and Director of Innovation and Strategy at Alma IT Systems. Specializing in model-guided medicine within computer-assisted surgery and personalized medicine, he combines his medical expertise with system architecture to advance digital transformation in healthcare. His research on precision medicine and workflow optimization, along with his contributions to international conferences and journals, highlight his role in integrating technology with medical practice. Dr. Herrero Antón de Vez is committed to a human-centered healthcare system, promoting value-based, patient-centered models, and digital democratization to drive innovative solutions in the field. He has also been actively involved in European and national projects aimed at integrating AI, Virtual Human Models with Infrastructure Virtual Twins for enabling a sustainable transformation of the healthcare ecosystem. Dr. Herrero is a member of the editorial committee of ISCAS and a scientific reviewer for the "International Journal of Computer Assisted Radiology and Surgery".

Inhalt

PART I: Understanding the Landscape
1. Problem Description: Challenges in Modern Healthcare
2. Current Healthcare Infrastructures and Standards

PART II: A multidimensional approach to address healthcare ecosystem’s challenges
3. Model-Guided Medicine: An Overview
4. Harnessing Big Data Insights in Healthcare
5. Challenges of AI in Healthcare
6. Infrastructure perspective

PART III: Enhancing Diagnostics and Treatment
7. Machine Learning and Predictive Analytics in Medical Diagnostics
8. Optimizing Treatment with Machine Learning

PART IV: Enhancing Healthcare Delivery
9. Clinical Decision Support Systems Powered by AI
10. Overcoming Ethical and Regulatory Challenges

PART V: Practical Implementation and Case Studies
11. From Theory to Practice: Applying Machine Learning Models in Healthcare
12. AI-Powered Diagnostics

PART VI: Advanced Techniques and Emerging Trends
13. Deep Neural Networks for Predictive and Early Disease Identification
14. Reinforcement Learning in Medical Decision Support Systems
15. Explainable AI: Clarity and Confidence in Medical Decision-Making
16. Few-Shot Learning and Transfer Learning for Medical Imaging
17. Temporal Modeling with Long and Short-Term Memory Networks
18. Unsupervised Learning for Anomaly Detection and Patient Stratification
19. Scalable Architectures for Large-Scale Healthcare Data

PART VII: Future Directions and Innovations
20. Future Trends and Technologies in Healthcare
21. Building Sustainable Business Models for AI in Healthcare

PART VIII: Appendices and Additional Resources
22. Glossary of Key Terms and Concepts
23. Further Reading and Resources

Produkt-Details

• Erscheint lt. Verlag: 1.9.2026
• Verlagsort: San Diego
• Sprache: englisch
• Maße: 191 x 235 mm
• Gewicht: 450 g
• Themenwelt: Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
• Themenwelt: Medizin / Pharmazie ► Physiotherapie / Ergotherapie ► Orthopädie
• Themenwelt: Technik ► Medizintechnik
• Themenwelt: Technik ► Umwelttechnik / Biotechnologie
• ISBN-10: 0-443-43798-X / 044343798X
• ISBN-13: 978-0-443-43798-4 / 9780443437984
• Zustand: Neuware