ConVIScope: Visual Analytics for Exploring Patient Conversations
Raymond Li (1), Enamul Hoque (2), Giuseppe Carenini (1), Richard, Lester (3), Raymond Chau (3) ((1) Department of Computer Science, University, of British Columbia, (2) School of Information Technology, York University,, (3) Department of Medicine, University of British Columbia)
TL;DR
ConVIScope is a visual analytics tool that combines interactive visualization and NLP techniques to analyze patient-doctor conversations, aiding healthcare professionals in extracting valuable insights from large text datasets.
Contribution
This paper introduces ConVIScope, a novel visual analytics system specifically designed for exploring and understanding patient-doctor conversations through integrated visualization and NLP.
Findings
Demonstrated utility through case studies with healthcare professionals
Revealed insights into patient-doctor communication patterns
Provided lessons for future system improvements
Abstract
The proliferation of text messaging for mobile health is generating a large amount of patient-doctor conversations that can be extremely valuable to health care professionals. We present ConVIScope, a visual text analytic system that tightly integrates interactive visualization with natural language processing in analyzing patient-doctor conversations. ConVIScope was developed in collaboration with healthcare professionals following a user-centered iterative design. Case studies with six domain experts suggest the potential utility of ConVIScope and reveal lessons for further developments.
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Taxonomy
TopicsData Visualization and Analytics · Advanced Text Analysis Techniques · Video Analysis and Summarization