AI and Data Engineering for Healthcare: Real-World Applications and Case Studies [Hardcover]

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

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.

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