Data Driven Science for Clinically Actionable Knowledge in Diseases

Inbunden, Engelska, 2023

Del i serien Analytics and AI for Healthcare

1 233 kr

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Beskrivning

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.

Produktinformation

Utgivningsdatum:2023-12-06
Mått:156 x 234 x 18 mm
Vikt:630 g
Format:Inbunden
Språk:Engelska
Serie:Analytics and AI for Healthcare
Antal sidor:236
Förlag:Taylor & Francis Ltd
ISBN:9781032273532

Utforska kategorier

Sjukdomar och rubbningar inom Medicin
Epidemiologi och medicinsk statistik inom Medicin
Tillämpad datateknik inom Data och IT

Mer om författaren

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.

Recensioner i media

"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

Innehållsförteckning

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 Parkinson’s 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 Visualisation—A Methodological Framework. Chapter 9. Visualisation for Explainable Machine Learning in Biomedical Data Analysis. Chapter 10. Visual Communication and Trust in the Health Domain.