AI and Data Engineering for Healthcare
Real-World Applications and Case Studies
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Product details:
- Edition number 1
- Publisher Chapman and Hall
- Date of Publication 17 December 2025
- ISBN 9781032988467
- Binding Hardback
- No. of pages456 pages
- Size 234x156 mm
- Language English
- Illustrations 116 Illustrations, black & white; 12 Halftones, black & white; 104 Line drawings, black & white; 22 Tables, black & white 700
Categories
Short description:
This book examines the transformative role of artificial intelligence (AI) and data engineering in revolutionizing the healthcare landscape. It presents cutting-edge developments ranging from predictive algorithms for disease diagnosis to large-scale data systems that enhance patient outcomes.
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Long description:
This book examines the transformative role of artificial intelligence (AI) and data engineering in revolutionizing the healthcare landscape. It presents cutting-edge developments ranging from predictive algorithms for disease diagnosis to large-scale data systems that enhance patient outcomes. By emphasizing the synergy between AI and data engineering, the book showcases practical applications in medical imaging, clinical diagnostics, and personalized treatment strategies.
It also thoughtfully examines ethical considerations, data privacy, and healthcare equity, particularly in underserved and rural populations.
Key Features:
Explores state-of-the-art technologies in healthcare, including image segmentation, feature extraction, feature selection, and classification.
Provides real-world case studies, practical examples, and hands-on exercises for effective implementation of AI-driven solutions.
Bridges disciplines across computer science, data engineering, and biomedical sciences to foster cross-domain collaboration.
Highlights innovative research methodologies and their applications in AI-powered healthcare systems.
Discusses the role of AI in improving healthcare access, delivery, and outcomes across diverse populations.
This book is ideal for professionals, researchers, and policymakers seeking to understand and shape the future of healthcare through the lens of AI and data-driven innovation.
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Table of Contents:
1. Artificial Intelligence in Mental Health: A Comprehensive Review 2. ABCNN: Attention-Based Convolutional Neural Networks for Arrhythmia Detection from ECG Data 3. Beyond the Black Box: Hybrid Deep Learning and Multi-Domain Fusion for Explainable EEG-Based Emotion Recognition 4. Economic Implications of Artificial Intelligence in Diabetes Management: Opportunities, Challenges, and Regional Prospects for Odisha 5. AI in Early Disease Detection and Prevention 6. AI in Medical Imaging: Revolutionizing Diagnostics through AI-Powered Services and Case Studies 7. Artificial Intelligence for Disease Prevention: From Diagnosis to Personalized Treatment 8. Deep Learning for Cardiovascular Risk Prediction: Unveiling Insights with RNNs and LSTMs 9. Predicting Protein-Protein Interactions: Machine Learning Models, Obstacles, and Advancements 10. The Role of Natural Language Processing in Analyzing Patient Records for Improved Clinical Decision-Making 11. AI-Enabled IADF Framework for MHD Diagnosis 12. AI for Remote Healthcare and Telemedicine 13. Diabetic Retinopathy Classification using Convolutional Neural Networks 14. Epidemiology and Transmission Dynamics of SARS-CoV-2 15. Harnessing Generative Adversarial Networks for Heart Disease Prediction: A Comprehensive Review
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