Automatic abstraction of imaging observations with their characteristics from mammography reports.

Journal: Journal of the American Medical Informatics Association : JAMIA
Published Date: Oct 28, 2014

Abstract

BACKGROUND: Radiology reports are usually narrative, unstructured text, a format which hinders the ability to input report contents into decision support systems. In addition, reports often describe multiple lesions, and it is challenging to automatically extract information on each lesion and its relationships to characteristics, anatomic locations, and other information that describes it. The goal of our work is to develop natural language processing (NLP) methods to recognize each lesion in free-text mammography reports and to extract its corresponding relationships, producing a complete information frame for each lesion.

Authors

  • Selen Bozkurt
    Department of Biostatistics and Medical Informatics, Akdeniz University Faculty of Medinice, 48000 Antalya, Turkey.
  • Jafi A Lipson
    Department of Radiology, Stanford University, Stanford, California, USA.
  • Utku Senol
    Department of Radiology, Akdeniz University Faculty of Medicine, Antalya, Turkey.
  • Daniel L Rubin
    Department of Biomedical Data Science, Stanford University School of Medicine Medical School Office Building, Stanford CA 94305-5479.

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