Interactive Exploration of Longitudinal Cancer Patient Histories Extracted From Clinical Text

PURPOSE: Retrospective cancer research requires identification of patients matching both categorical and temporal inclusion criteria, often on the basis of 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. Our goal was to build interactive visual tools to support the process of interpreting rich representations of histories of patients with cancer. METHODS: Qualitative inquiry into user tasks and goals, a structured data model, and an innovative natural language processing pipeline were used to guide design. RESULTS: The resulting information visualization tool provides cohort- and patient-level views with linked interactions between components. CONCLUSION: Interactive tools hold promise for facilitating the interpretation of patient summaries and identification of cohorts for retrospective research.