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Data Driven Science for Clinically Actionable Knowledge in Diseases [Hardback]

Edited by (Western Sydney University), Edited by (The Children's Hospital at West Mead), Edited by (Western Sydney University), Edited by (University of Technology, Sydney)
  • Formāts: Hardback, 236 pages, height x width: 234x156 mm, weight: 503 g, 25 Tables, black and white; 38 Line drawings, black and white; 21 Halftones, black and white; 59 Illustrations, black and white
  • Sērija : Analytics and AI for Healthcare
  • Izdošanas datums: 06-Dec-2023
  • Izdevniecība: Chapman & Hall/CRC
  • ISBN-10: 1032273534
  • ISBN-13: 9781032273532
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  • Hardback
  • Cena: 87,22 €
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Data Driven Science for Clinically Actionable Knowledge in DiseasesPalielināt
  • Formāts: Hardback, 236 pages, height x width: 234x156 mm, weight: 503 g, 25 Tables, black and white; 38 Line drawings, black and white; 21 Halftones, black and white; 59 Illustrations, black and white
  • Sērija : Analytics and AI for Healthcare
  • Izdošanas datums: 06-Dec-2023
  • Izdevniecība: Chapman & Hall/CRC
  • ISBN-10: 1032273534
  • ISBN-13: 9781032273532
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9781032273532.html
Keywords:

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.

Recenzijas

"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

Chapter
1. Understanding the Impact of Patient Journey Patterns on
Health Outcomes for Patients with Diabetes.
Chapter
2. COVID-19 Impact
Analysis on Patients with Complex Health Conditions: A Literature Review.
Chapter
3. Estimating the Relative Contribution of Transmission to the
Prevalence of Drug Resistance in Tuberculosis.
Chapter
4. A Novel Diagnosis
System for Parkinsons Disease Based on Ensemble Random Forest.
Chapter
5.
Harmonization of Brain Data across Sites and Scanners.
Chapter
6.
Feature-Ranking Methods for RNA Sequencing Data.
Chapter
7. Graph Neural
Networks for Brain Tumour Segmentation.
Chapter
8. Biomedical Data Analytics
and VisualisationA Methodological Framework.
Chapter
9. Visualisation for
Explainable Machine Learning in Biomedical Data Analysis.
Chapter
10. Visual
Communication and Trust in the Health Domain.
Daniel R. Catchpoole is the Group Leader of the Tumour Bank, Childrens Cancer Research Unit, Childrens 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.