Deep Learning Approaches for Healthcare Data Analysis and Decision Making

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Deep Learning Approaches for Healthcare Data Analysis and Decision Making

Bagwari, Ashish (EDT)/ Dubey, Shivendra (EDT)/ Barbosa, Jorge Luis Victória (EDT)

ウェブストア価格 ¥43,929（本体¥39,936）
Academic Press Inc（2026/09発売）
外貨定価 US$ 200.00
ポイント 399pt

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製本 Paperback:紙装版/ペーパーバック版／ページ数 400 p.
言語 ENG
商品コード 9780443437984

Full 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.

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