Full Description
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.
Contents
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.
