Natural language processing and visualization in the molecular imaging domain

  • Authors:

  • P. Karina Tulipano;Ying Tao;William S. Millar;Pat Zanzonico;Katherine Kolbert;Hua Xu;Hong Yu;Lifeng Chen;Yves A. Lussier;Carol Friedman

  • Affiliations:

  • Department of Biomedical Informatics, Columbia University, 622 West 168th Street, Vanderbilt Clinic Floor 5, NY 10032, USA;Department of Biomedical Informatics, Columbia University, 622 West 168th Street, Vanderbilt Clinic Floor 5, NY 10032, USA;Department of Radiology, New York Presbyterian Hospital, NY, USA;Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, NY, USA;Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, NY, USA;Department of Biomedical Informatics, Columbia University, 622 West 168th Street, Vanderbilt Clinic Floor 5, NY 10032, USA;Department of Biomedical Informatics, Columbia University, 622 West 168th Street, Vanderbilt Clinic Floor 5, NY 10032, USA;Department of Biomedical Informatics, Columbia University, 622 West 168th Street, Vanderbilt Clinic Floor 5, NY 10032, USA;Department of Biomedical Informatics, Columbia University, 622 West 168th Street, Vanderbilt Clinic Floor 5, NY 10032, USA;Department of Biomedical Informatics, Columbia University, 622 West 168th Street, Vanderbilt Clinic Floor 5, NY 10032, USA

  • Venue:
  • Journal of Biomedical Informatics
  • Year:
  • 2007

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Abstract

Molecular imaging is at the crossroads of genomic sciences and medical imaging. Information within the molecular imaging literature could be used to link to genomic and imaging information resources and to organize and index images in a way that is potentially useful to researchers. A number of natural language processing (NLP) systems are available to automatically extract information from genomic literature. One existing NLP system, known as BioMedLEE, automatically extracts biological information consisting of biomolecular substances and phenotypic data. This paper focuses on the adaptation, evaluation, and application of BioMedLEE to the molecular imaging domain. In order to adapt BioMedLEE for this domain, we extend an existing molecular imaging terminology and incorporate it into BioMedLEE. BioMedLEE's performance is assessed with a formal evaluation study. The system's performance, measured as recall and precision, is 0.74 (95% CI: [.70-.76]) and 0.70 (95% CI [.63-.76]), respectively. We adapt a JAVA(TM) viewer known as PGviewer for the simultaneous visualization of images with NLP extracted information.