Data Driven Science for Clinically Actionable Knowledge in Diseases

By: Daniel Catchpoole (Editor), Simeon Simoff (Editor), Paul Kennedy (Editor)

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Hardcover | 6 December 2023 | Edition Number 1

At a Glance

Hardcover
256 Pages

Dimensions(cm)
23.39 x 15.6 x 1.6

Hardcover

$243.75

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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.

Industry Reviews

"The intersection of the computational, biological, and medical sciences is poised to revolutionize personalized medicine across a vast spectrum of diseases and in low, medium, and high income countries. This new book, Data Driven Science for Clinically Actionable Knowledge in Diseases, serves as a fantastic overview of the space for all stakeholders. The text enables readers to learn both about the trajectory of the space, and to identify specific technical use cases where success has been shown and which can be re-deployed into new systems."

- Dr Noah Berlow, First Ascent Biomedical

"Health data is inherently complex and collected via wildly diverse channels. This book shows how leveraging health data is difficult, difficult to collect, and difficult to synthesise, but how much patient care can be improved when it is done well."

- Prof David Skillicorn, Queens University, Kingston, Ontario, Canada

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