Data Driven Science for Clinically Actionable Knowledge in Diseases

Data-driven science is a major decision-making aid for the diagnosis and treatment of disease, and this edited volume covers state-of-the-art theory, methods, models, design, evaluation, and applications in computational and visual analytics for analyzing biomedical and health data. It demonstrates how analytics influences the healthcare domain by improving decision-making, specifying diagnostics, selecting the best treatments, and generating clinical certainty.

Format: Paperback / softback
Length: 236 pages
Publication date: 06 December 2023
Publisher: Taylor & Francis Ltd

Data-driven science has emerged as a crucial tool in the diagnosis and treatment of diseases, leveraging computational and visual analytics to effectively explore and interpret large and complex data sets. This edited volume presents the latest advancements in computational and visual analytics, covering theoretical foundations, methodologies, models, designs, evaluations, and applications in desktop, mobile, and immersive environments for analyzing biomedical and health data. The book emphasizes data-driven integral analysis, encompassing computational methods and visual analytics practices and solutions for uncovering actionable knowledge to support clinical decisions in real-world scenarios. By examining the integration of data and visual analytics in the healthcare domain, the book showcases how analytics drives innovation by enhancing decision-making, refining diagnostics, selecting optimal treatments, and fostering clinical certainty.

This edited volume serves as a comprehensive resource for researchers, practitioners, and students in the fields of computer science, data science, healthcare, and medicine. It provides a deep understanding of the latest techniques and methodologies in computational and visual analytics, enabling them to analyze and interpret biomedical and health data more effectively. The book's chapters are written by leading experts in the field, ensuring that the content is up-to-date and relevant to current research and practice.

One of the key strengths of this book is its focus on real-world applications. The chapters discuss case studies and practical examples that demonstrate how data and visual analytics have been implemented in healthcare settings to improve patient outcomes. These examples showcase the potential of analytics to transform healthcare by providing insights into disease patterns, predicting disease progression, and identifying personalized treatment strategies.

Another notable aspect of the book is its emphasis on interdisciplinary collaboration. The chapters discuss the interplay between computational and visual analytics, computer science, healthcare, and medicine, highlighting the importance of collaboration in developing effective solutions. This interdisciplinary approach ensures that the insights generated from data analysis are translated into practical applications that can benefit patients and healthcare professionals alike.

In conclusion, this edited volume on computational and visual analytics in healthcare is a valuable resource for researchers, practitioners, and students seeking to advance their knowledge and expertise in this field. By presenting the latest advancements in theory, methodology, models, designs, evaluations, and applications, the book provides a comprehensive understanding of the role of data and visual analytics in diagnosing and treating diseases. The real-world applications and interdisciplinary collaboration showcased in the chapters highlight the potential of analytics to transform healthcare and improve patient outcomes.

Weight: 392g
Dimension: 156 x 234 x 18 (mm)
ISBN-13: 9781032273518