Edited Book: Data Driven Science for Clinically Actionable Knowledge in Diseases

Publisher: CRC Press, Taylor & Francis Group, CHAPMAN & HALL

Data driven science has become a major decision-making aid for diagnosis and treatment of diseases. Computational and visual analytics enables effective exploration and sense making of the large and complex data through the deployment of appropriate data science methods, meaningful visualization and human-information interaction.

It is vital to utilize inter- and transdisciplinary expertise and knowledge in computational, visual analytics and health to enable better and more effective ways to engineer and discover actionable knowledge and knowledge representation schemata for utilization of such knowledge.

The book aims to tackle important issues in personalized medicine that applies data driven science to enrich and lead clinically actionable knowledge. The book emphasizes how and what the related data driven science will be or has been successfully implemented into the biomedical domain.This book covers the current theories, methods, models, designs, evaluations, and applications in computational and visual analytics in desktop, mobile and immersive environments for analyzing biomedical and health data. We seek contributions on data-driven integral analysis, including computational methods and visual analytics practices, and solutions for discovering actionable knowledge that is required to support clinical actions in real environments.

We solicit contributed chapters including but not limited to the following topics in biomedical data analytics and health domain:

• Computational data analytics methods, technologies, methodologies or surveys, including machine learning, deep learning analysis, statistical analysis, artificial intelligence (AI), and hybrid methods.
• Data driven analytics models, process frameworks, and case studies with deployment of actionable knowledge.
• Visual analytics and visualization frameworks, models, and methods.
• Visualization design, platform-agnostic algorithms and interaction design for human-computer interaction and human-information interaction.
• Interpretability, explainability, and evaluation of machine learning, AI and visualization.
• Frameworks, models, and analytics methods for rare diseases, including causality from observations.