Deep Learning Approaches for Healthcare Data Analysis and Decision Making
- 1st Edition - February 2, 2027
- Latest edition
- Editors: Ashish Bagwari, Shivendra Dubey, Jorge Luis Victória Barbosa, Ciro Rodriguez, Albena Mihovska, Hugo Herrero Antón de Vez
- Language: English
Deep Learning Approaches for Healthcare Data Analysis and Decision Making demystifies complex data-driven technologies, providing a clear framework for integrating advanced analyt… Read more
Description
Description
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.
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.
Key features
Key features
- Integrates deep learning and AI into healthcare practices, addressing data management and workflow optimization
- Illustrates practical examples in the successful application of deep learning techniques in various healthcare settings
- Provides insights into developing and implementing predictive models to enhance diagnosis and treatment strategies
- Identifies and addresses biases in predictive models to enhance trust and accountability in AI-driven decisions
- Presents tools and methodologies for managing and analyzing large healthcare datasets to derive meaningful insights and improve decision-making
Readership
Readership
Data Scientists and Machine Learning Engineers looking to apply their skills in the healthcare sector, biomedical Engineers seeking to integrate advanced data analytics and machine learning into medical device development, healthcare solutions Researchers in Biomedical Informatics and computer science seeking to implement digital transformation strategies
Table of contents
Table of contents
PART I: Understanding the Landscape
1. Problem Description: Challenges in Modern Healthcare
1.1. The Complexity of Healthcare Data
1.2. Current Limitations in Diagnosis and Treatment
1.3. Operational Inefficiencies and Workflow Challenges
1.4. Innovation transfer and monetization challenges
2. Current Healthcare Infrastructures and Standards
2.1. Overview of Healthcare Information Systems (PACS, HIS, RIS)
2.2. Laboratory Systems and Standards (DICOM, LOINC, RxNorm, HL7, OpenEHR)
2.3. Interoperability and Integration Challenges
2.4. Disconnection Between Intra and Extrahospitalary Data
2.5. Issues of Auditability, Standardization, and Security
2.6. Scalability and Modularity Concerns
2.7. Knowledge Silos and Disparate Tools
PART II: A multidimensional approach to address healthcare ecosystem’s challenges
3. Model-Guided Medicine: An Overview
3.1. Definition and Importance of Model-Guided Medicine
3.2. Key Components: Data Integration, Analytics, and Decision Support
3.3. Human-machine interaction and introspective interfaces
3.4. Benefits for Patients and other Healthcare agents
4. Harnessing Big Data Insights in Healthcare
4.1. Medical Diagnostics and Imaging
4.2. Predictive Analytics in Patient Care
4.3. Natural Language Processing (NLP) in Healthcare
4.4. Personalized Treatment and Medical Recommendation
5. Challenges of AI in Healthcare
5.1. Explainability and Transparency
5.2. Bias in Machine Learning Models
5.3. Auditability and Accountability
5.4. Privacy and Security Concerns
5.5. Adversarial Attacks and Tampering
5.6. Integration and model selection
5.7. Environmental footprint and computational needs
5.8. Regulation challenges
5.9. Ethics
6. Infrastructure perspective
6.1. Orchestration and explainability
6.2. Auditability and measurement of quality
6.3. Systems of Systems to address the challenges
6.4. Advanced Architectures for Healthcare AI
6.5. Importance of the Data Plane
6.6. Environmental Optimization
6.7. Advanced Computation Methods
PART III: Enhancing Diagnostics and Treatment
7. Machine Learning and Predictive Analytics in Medical Diagnostics
7.1. Disease Diagnosis and Detection using Supervised Learning
7.2. Medical Imaging using Deep Learning
7.3. Predictive Analytics for Patient Risk Stratification
7.4. Legal and Ethical Considerations in Machine Learning
8. Optimizing Treatment with Machine Learning
8.1. Predictive Modeling for Personalized Medicine
8.2. Optimizing Medication and Dosage
8.3. Treatment Outcome Prediction
8.4. Adaptive Treatment Strategies
PART IV: Enhancing Healthcare Delivery
9. Clinical Decision Support Systems Powered by AI
9.1. Clinical Workflow Optimization
9.2. NLP for Clinical Documentation
9.3. Real-Time Alerts and Monitoring
9.4. Personalized Patient Engagement and Education
10. Overcoming Ethical and Regulatory Challenges
10.1. Addressing Data Security and Privacy
10.2. Scalability and Integration with Clinical Support Systems
10.3. Enhancing Collaboration Between AI and Healthcare Professionals
PART V: Practical Implementation and Case Studies
11. From Theory to Practice: Applying Machine Learning Models in Healthcare
11.1. Integration and Training with Clinical Workflows
11.2. Monitoring and Continuous Improvement
11.3. Real-World Case Studies and Success Stories
12. AI-Powered Diagnostics
12.1. Personalized Medicine and Treatment Optimization
12.2. AI-Enhanced Remote Monitoring and Telemedicine
PART VI: Advanced Techniques and Emerging Trends
13. Deep Neural Networks for Predictive and Early Disease Identification
13.1. Implementing Deep Neural Networks
13.2. Clinical Workflows Integration
14. Reinforcement Learning in Medical Decision Support Systems
14.1. Importance and Overview of Medical Decision Support Systems
14.2. Success Stories and Impactful Implementations
14.3. Algorithm Design and AI Technologies
15. Explainable AI: Clarity and Confidence in Medical Decision-Making
15.1. Electronic Health Records Integration
15.2. Predictive Analytics for Risk Assessment
15.3. Symptom Checkers and Virtual Assistants
16. Few-Shot Learning and Transfer Learning for Medical Imaging
16.1. Overview and Importance of Transfer Learning and Few-Shot Learning
16.2. Cross-Domain and Domain Adaptation Learning
16.3. Emerging Technologies Integration
17. Temporal Modeling with Long and Short-Term Memory Networks
17.1. Healthcare Time-Series Data Analysis
17.2. Attention Mechanisms with LSTMs
17.3. Computational Requirements and Model Complexity
18. Unsupervised Learning for Anomaly Detection and Patient Stratification
18.1. Overview and Importance of Unsupervised Learning in Healthcare
18.2. Disease Phenotyping and Subtyping
18.3. Representation and Feature Extraction Learning
19. Scalable Architectures for Large-Scale Healthcare Data
19.1. Overview and Importance of Federated Learning in Healthcare
19.2. Performance and Scalability Optimization
19.3. Infrastructure and Cost Considerations
19.4. Integration with AI and IoT in Healthcare
PART VII: Future Directions and Innovations
20. Future Trends and Technologies in Healthcare
20.1. The Rise of Big Data and AI in Healthcare
20.2. Key Trends: Telemedicine, Prevention, Wellness, Wearables, and patient journey continuum
20.3. One health concept
20.4. Emerging Technologies: Blockchain, 6G, distributed and Edge Computing
20.5. Predictive and Preventive Healthcare
20.6. AI-Driven Precision Medicine
20.7. Ethical and Societal Impacts of AI in Healthcare
21. Building Sustainable Business Models for AI in Healthcare
21.1. Value-Based and Oblicual Approaches
21.2. Enhancing Collaboration Between AI and Healthcare Professionals
21.3 Real-World Use Cases
21.4. Aligning AI Integration with Economic Strategies
PART VIII: Appendices and Additional Resources
22. Glossary of Key Terms and Concepts
22.1. Important Definitions
22.2. Acronyms and Abbreviations
23. Further Reading and Resources
23.1. Recommended Books and Articles
23.2. Online Courses and Tutorials
1. Problem Description: Challenges in Modern Healthcare
1.1. The Complexity of Healthcare Data
1.2. Current Limitations in Diagnosis and Treatment
1.3. Operational Inefficiencies and Workflow Challenges
1.4. Innovation transfer and monetization challenges
2. Current Healthcare Infrastructures and Standards
2.1. Overview of Healthcare Information Systems (PACS, HIS, RIS)
2.2. Laboratory Systems and Standards (DICOM, LOINC, RxNorm, HL7, OpenEHR)
2.3. Interoperability and Integration Challenges
2.4. Disconnection Between Intra and Extrahospitalary Data
2.5. Issues of Auditability, Standardization, and Security
2.6. Scalability and Modularity Concerns
2.7. Knowledge Silos and Disparate Tools
PART II: A multidimensional approach to address healthcare ecosystem’s challenges
3. Model-Guided Medicine: An Overview
3.1. Definition and Importance of Model-Guided Medicine
3.2. Key Components: Data Integration, Analytics, and Decision Support
3.3. Human-machine interaction and introspective interfaces
3.4. Benefits for Patients and other Healthcare agents
4. Harnessing Big Data Insights in Healthcare
4.1. Medical Diagnostics and Imaging
4.2. Predictive Analytics in Patient Care
4.3. Natural Language Processing (NLP) in Healthcare
4.4. Personalized Treatment and Medical Recommendation
5. Challenges of AI in Healthcare
5.1. Explainability and Transparency
5.2. Bias in Machine Learning Models
5.3. Auditability and Accountability
5.4. Privacy and Security Concerns
5.5. Adversarial Attacks and Tampering
5.6. Integration and model selection
5.7. Environmental footprint and computational needs
5.8. Regulation challenges
5.9. Ethics
6. Infrastructure perspective
6.1. Orchestration and explainability
6.2. Auditability and measurement of quality
6.3. Systems of Systems to address the challenges
6.4. Advanced Architectures for Healthcare AI
6.5. Importance of the Data Plane
6.6. Environmental Optimization
6.7. Advanced Computation Methods
PART III: Enhancing Diagnostics and Treatment
7. Machine Learning and Predictive Analytics in Medical Diagnostics
7.1. Disease Diagnosis and Detection using Supervised Learning
7.2. Medical Imaging using Deep Learning
7.3. Predictive Analytics for Patient Risk Stratification
7.4. Legal and Ethical Considerations in Machine Learning
8. Optimizing Treatment with Machine Learning
8.1. Predictive Modeling for Personalized Medicine
8.2. Optimizing Medication and Dosage
8.3. Treatment Outcome Prediction
8.4. Adaptive Treatment Strategies
PART IV: Enhancing Healthcare Delivery
9. Clinical Decision Support Systems Powered by AI
9.1. Clinical Workflow Optimization
9.2. NLP for Clinical Documentation
9.3. Real-Time Alerts and Monitoring
9.4. Personalized Patient Engagement and Education
10. Overcoming Ethical and Regulatory Challenges
10.1. Addressing Data Security and Privacy
10.2. Scalability and Integration with Clinical Support Systems
10.3. Enhancing Collaboration Between AI and Healthcare Professionals
PART V: Practical Implementation and Case Studies
11. From Theory to Practice: Applying Machine Learning Models in Healthcare
11.1. Integration and Training with Clinical Workflows
11.2. Monitoring and Continuous Improvement
11.3. Real-World Case Studies and Success Stories
12. AI-Powered Diagnostics
12.1. Personalized Medicine and Treatment Optimization
12.2. AI-Enhanced Remote Monitoring and Telemedicine
PART VI: Advanced Techniques and Emerging Trends
13. Deep Neural Networks for Predictive and Early Disease Identification
13.1. Implementing Deep Neural Networks
13.2. Clinical Workflows Integration
14. Reinforcement Learning in Medical Decision Support Systems
14.1. Importance and Overview of Medical Decision Support Systems
14.2. Success Stories and Impactful Implementations
14.3. Algorithm Design and AI Technologies
15. Explainable AI: Clarity and Confidence in Medical Decision-Making
15.1. Electronic Health Records Integration
15.2. Predictive Analytics for Risk Assessment
15.3. Symptom Checkers and Virtual Assistants
16. Few-Shot Learning and Transfer Learning for Medical Imaging
16.1. Overview and Importance of Transfer Learning and Few-Shot Learning
16.2. Cross-Domain and Domain Adaptation Learning
16.3. Emerging Technologies Integration
17. Temporal Modeling with Long and Short-Term Memory Networks
17.1. Healthcare Time-Series Data Analysis
17.2. Attention Mechanisms with LSTMs
17.3. Computational Requirements and Model Complexity
18. Unsupervised Learning for Anomaly Detection and Patient Stratification
18.1. Overview and Importance of Unsupervised Learning in Healthcare
18.2. Disease Phenotyping and Subtyping
18.3. Representation and Feature Extraction Learning
19. Scalable Architectures for Large-Scale Healthcare Data
19.1. Overview and Importance of Federated Learning in Healthcare
19.2. Performance and Scalability Optimization
19.3. Infrastructure and Cost Considerations
19.4. Integration with AI and IoT in Healthcare
PART VII: Future Directions and Innovations
20. Future Trends and Technologies in Healthcare
20.1. The Rise of Big Data and AI in Healthcare
20.2. Key Trends: Telemedicine, Prevention, Wellness, Wearables, and patient journey continuum
20.3. One health concept
20.4. Emerging Technologies: Blockchain, 6G, distributed and Edge Computing
20.5. Predictive and Preventive Healthcare
20.6. AI-Driven Precision Medicine
20.7. Ethical and Societal Impacts of AI in Healthcare
21. Building Sustainable Business Models for AI in Healthcare
21.1. Value-Based and Oblicual Approaches
21.2. Enhancing Collaboration Between AI and Healthcare Professionals
21.3 Real-World Use Cases
21.4. Aligning AI Integration with Economic Strategies
PART VIII: Appendices and Additional Resources
22. Glossary of Key Terms and Concepts
22.1. Important Definitions
22.2. Acronyms and Abbreviations
23. Further Reading and Resources
23.1. Recommended Books and Articles
23.2. Online Courses and Tutorials
Product details
Product details
- Edition: 1
- Latest edition
- Published: February 2, 2027
- Language: English
About the editors
About the editors
AB
Ashish Bagwari
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.
Affiliations and expertise
Professor and Head, Department of Electronics and Communication Engineering, WIT Dehradun, Uttarakhand Technical University (State Government Technical University), Dehradun, IndiaSD
Shivendra Dubey
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.
Affiliations and expertise
Assistant Professor (Senior Scale), Department of AIML, School of Computing, Manipal University, Jaipur, Rajasthan, IndiaJB
Jorge Luis Victória Barbosa
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.Affiliations and expertise
Professor, Applied Computing Graduate Program, University of Vale do Rio dos Sinos, Unisinos, BrazilCR
Ciro Rodriguez
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.Affiliations and expertise
Professor, Universidad Nacional Mayor de San Marcos UNMSM, PeruAM
Albena Mihovska
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.
Affiliations and expertise
Co-founder, SmartAvatar B.V., DenmarkHV
Hugo Herrero Antón de Vez
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".
Affiliations and expertise
Surgeon and Director of Innovation and Strategy, Alma IT Systems, Barcelona, Spain