Deep Generative Models for Integrative Analysis of Alzheimer's Biomarkers

The integration of generative AI and deep learning techniques for Alzheimer's disease detection significantly impacts the research community by advancing diagnostic accuracy and providing a comprehensive understanding of the disease. By combining multiple data modalities, including imaging, genetics, and clinical data, researchers can improve diagnostic precision and develop personalized treatment strategies. Generative AI facilitates efficient data utilization through dataset augmentation, fostering innovation and collaboration across interdisciplinary fields. These methodologies forward the exploration of new diagnostic tools while expediting their application in clinical practice, benefiting patients through early detection and intervention. The incorporation of generative AI may enhance research capabilities, promote collaboration, and improve Alzheimer's disease management and patient outcomes.

Deep Generative Models for Integrative Analysis of Alzheimer's Biomarkers explores the integration of deep generative models in disease diagnosis, biomarking, and prediction. It examines the use of tools like data analysis, natural language processing, and machine learning for effective Alzheimer’s research. This book covers topics such as data analysis, biomedicine, and machine learning, and is a useful resource for computer engineers, biologists, scientists, medical professionals, healthcare workers, academicians, and researchers.

The many academic areas covered in this publication include, but are not limited to:

• Alzheimer's Disease
• Biological Markers
• Biomedicine
• Biotechnology
• Data Analysis
• Data Models
• Deep Learning
• Diagnosis and Monitoring
• Disease Detection and Prediction
• Electronic Health Records
• Generative Artificial Intelligence
• Machine Learning
• Natural Language Processing