Longitudinal Visual Analytics for Unpacking the Cancer Journey

Abstract

Retrospective cancer research requires identification of patients matching both categorical and temporal inclusion criteria, often based on factors exclusively available in clinical notes. Although natural language processing approaches for inferring higher-level concepts have shown promise for bringing structure to clinical texts, interpreting results is often challenging, involving the need to move between abstracted representations and constituent text elements. We discuss qualitative inquiry into user tasks and goals, data elements and models resulting in an innovative natural language processing pipeline and a visual analytics tool designed to facilitate interpretation of patient summaries and identification of cohorts for retrospective research.