Advanced Data Analytics in Health [electronic resource] /
Giabbanelli, Philippe J.editor.
Mago, Vijay K.editor.
Papageorgiou, Elpiniki I.editor.
This book introduces readers to the methods, types of data, and scale of analysis used in the context of health. The challenges of working with big data are explored throughout the book, while the benefits are also emphasized through the discoveries made possible by linking large datasets. Methods include thorough case studies from statistics, as well as the newest facets of data analytics: data visualization, modeling and simulation, and machine learning. The diversity of datasets is illustrated through chapters on networked data, image processing, and text, in addition to typical structured numerical datasets. While the methods, types of data, and scale have been individually covered elsewhere, by bringing them all together under one “umbrella” the book highlights synergies, while also helping scholars fluidly switch between tools as needed. New challenges and emerging frontiers are also discussed, helping scholars grasp how methods will need to change in response to the latest challenges in health.
Dimensionality Reduction for Exploratory Data Analysis in Daily Medical Research -- Navigating Complex Systems for Policymaking using Simple Software Tools -- An Agent-based Model of Healthy Eating with Applications to Hypertension -- Young Adults, Health Insurance Expansions and Hospital Services Utilization -- The Impact of Patient Incentives on Comprehensive Diabetes Care Services and Medical Expenditures -- Challenges and Cases of Genomic Data Integration Across Technologies and Biological Scales.
This book introduces readers to the methods, types of data, and scale of analysis used in the context of health. The challenges of working with big data are explored throughout the book, while the benefits are also emphasized through the discoveries made possible by linking large datasets. Methods include thorough case studies from statistics, as well as the newest facets of data analytics: data visualization, modeling and simulation, and machine learning. The diversity of datasets is illustrated through chapters on networked data, image processing, and text, in addition to typical structured numerical datasets. While the methods, types of data, and scale have been individually covered elsewhere, by bringing them all together under one “umbrella” the book highlights synergies, while also helping scholars fluidly switch between tools as needed. New challenges and emerging frontiers are also discussed, helping scholars grasp how methods will need to change in response to the latest challenges in health.
Computational intelligence.
Artificial intelligence.
Health informatics.
Big data.
Computational Intelligence.
Artificial Intelligence.
Health Informatics.
Big Data/Analytics.
Health Informatics.