Interactive Exploration of Longitudinal Cancer Patient Histories Extracted From Clinical Text.

Journal: JCO clinical cancer informatics
Published Date: May 1, 2020

Abstract

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.

Authors

  • Zhou Yuan
    University of Pittsburgh, Pittsburgh, PA.
  • Sean Finan
    From Research Information Systems and Computing (V.M.C., V.G., S.M.), Partners Healthcare; Boston Children's Hospital Informatics Program (D.D., S.F., G.S.); Harvard Medical School (D.D., S.Y., A.C., M.A.-E.-B., N.A.S., S.M., S.T.W., R.D.); Department of Medicine (S.Y., S.T.W.), Department of Neurosurgery (A.C., M.A.-E.-B., R.D.), Division of Rheumatology, Immunology and Allergy (N.A.S.), and Channing Division of Network Medicine (S.T.W., R.D.), Brigham and Women's Hospital, Boston, MA; Center for Statistical Science (S.Y.), Tsinghua University, Beijing, China; Department of Neurology (S.M.), Massachusetts General Hospital; and Biostatistics (T.C.), Harvard School of Public Health, Boston, MA.
  • Jeremy Warner
    Vanderbilt University, Nashville, TN.
  • Guergana Savova
    Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, USA.
  • Harry Hochheiser
    University of Pittsburgh, Pittsburgh, PA, USA.

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