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Data Driven Science for Clinically Actionable Knowledge in Diseases

By: (Edited by) Daniel Catchpoole , (Edited by) Paul Kennedy , (Edited by) Quang Vinh Nguyen , (Edited by) Simeon Simoff

Manufacture on Demand

Ksh 8,650.00

Format: Paperback / Softback

ISBN-10: 1032273518

ISBN-13: 9781032273518

Series: Analytics and AI for Healthcare

Publisher: Taylor & Francis Ltd

Imprint: Chapman & Hall/CRC

Country of Manufacture: GB

Country of Publication: GB

Publication Date: Dec 6th, 2023

Print length: 236 Pages

Weight: 392 grams

Dimensions (height x width x thickness): 15.60 x 23.40 x 1.80 cms

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Computational and visual analytics enables effective exploration and sense making of large and complex data through the deployment of appropriate data science methods, meaningful visualization and human-information interaction.

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

This edited volume covers state-of-the-art theory, method, models, design, evaluation and applications in computational and visual analytics in desktop, mobile and immersive environments for analysing biomedical and health data. The book is focused on data-driven integral analysis, including computational methods and visual analytics practices and solutions for discovering actionable knowledge in support of clinical actions in real environments.

By studying how data and visual analytics have been implemented into the healthcare domain, the book demonstrates how analytics influences the domain through improving decision making, specifying diagnostics, selecting the best treatments and generating clinical certainty.


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