Data Driven Science for Clinically Actionable Knowledge in Diseases (Hardcover)

Daniel Catchpoole

ISBN 10: 1032273534 / ISBN 13: 9781032273532

Verlag: Taylor & Francis Ltd, London, 2023

Beschreibung

Hardcover. 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. 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. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Bestandsnummer des Verkäufers 9781032273532

Bibliografische Details

• Titel: Data Driven Science for Clinically Actionable Knowledge in Diseases (Hardcover)
• Verlag: Taylor & Francis Ltd, London
• Veröffentlichungsdatum: 2023
• Sprache: Englisch
• ISBN-10: 1032273534
• ISBN-13: 9781032273532
• Bindung: Hardcover
• Zustand: new

Über diesen Titel

Inhaltsangabe

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.

Über die Autorin bzw. den Autor

Daniel R. Catchpoole is the Group Leader of the Tumour Bank, Children’s Cancer Research Unit, Children’s Hospital, Westmead, Australia. He is also affiliated with the Faculty of Medicine at the University of Sydney and the Department of Information Technology at the University of Technology Sydney.

Simeon J. Simoff is the Cluster Pro Vice Chancellor (Science, Technology, Engineering and Mathematics) and Dean of the School of Computer, Data and Mathematical Sciences at Western Sydney University.

Paul J. Kennedy is the Director of the Biomedical Data Science Laboratory at the Australia Artificial Intelligence Institute and the Head of Computer Science in the Faculty of Engineering and Information Technology at the University of Technology Sydney.

Quang Vinh Nguyen is the Director of Academic Programs for Postgraduate ICT at the School of Computer, Data and Mathematical Sciences and the MARCS Institute for Brain, Behaviour and Development at Western Sydney University.