Information Visualization for Chronic Disease Risk Assessment

Here, the authors describe and evaluate a new information-visualization method and prototype software tool that support risk assessment for negative health outcomes. Their framework uses principal component analysis and linear discriminant analysis to plot high-dimensional patient data in 2D. It also incorporates interactive visualization techniques to aid the identification of high versus low risk patients, critical risk factors, and the estimated effect of hypothetical interventions on the likelihood of negative outcomes. The authors quantitatively evaluated the visualization method using a secondary dataset describing 588 people with diabetes and their estimated future risk of heart attack. Their results show that the method visually classifies high- and low-risk people with accuracy that's similar to other common statistical methods. The framework also provides an interactive, visualization-based tool for clinicians to explore the nuances of their patients' data and disease risk.

Authors

• Christopher A. Harle
• Daniel B. Neill New York University
• Rema Padman

Keywords

• Visualization
• Information technology
• Medical information processing
• Medical services
• Risk assessment
• Software development
• High Risk
• Low Risk
• Effective Interventions
• Myocardial Infarction
• Patient Data
• Individual Patient
• Population Level
• High-risk Patients
• Linear Discriminant Analysis
• Bar Graphs
• Interactive Visualization
• 2D Data
• Critical Risk Factor
• Linear Discriminant
• Interaction Techniques
• Chronic Disease Care
• Complex Chronic Disease
• Hypothetical Intervention
• 2D Visualization
• Support Vector Machine
• Lower Quadrant
• Variety Of Risk Factors
• American Diabetes Association
• Congestive Heart Failure
• Diastolic Blood Pressure
• Classification Accuracy
• Systolic Blood Pressure
• Body Mass Index
• Dimensionality Reduction
• information visualization
• healthcare