Towards data-driven medical imaging using natural language processing in patients with suspected urolithiasis.

Journal: International journal of medical informatics
Published Date: May 1, 2020

Abstract

OBJECTIVE: The majority of radiological reports are still written as free text and lack structure. Further evaluation of free-text reports is difficult to achieve without a great deal of manual effort, and is not possible in everyday clinical practice. This study aims to automatically capture clinical information and positive hit rates from narrative radiological reports of suspected urolithiasis using natural language processing (NLP).

Authors

  • Florian Jungmann
    Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg University Mainz, Germany. Electronic address: florian.jungmann@unimedizin-mainz.de.
  • Benedikt Kämpgen
    Empolis Information Management, Kaiserslautern, Germany.
  • Philipp Mildenberger
    Institute for Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center of the Johannes Gutenberg University Mainz, Germany.
  • Igor Tsaur
    Department of Urology and Pediatric Urology, University Medical Center of the Johannes Gutenberg University Mainz, Germany.
  • Tobias Jorg
    Klinik und Poliklinik für diagnostische und interventionelle Radiologie, Universitätsmedizin, Johannes-Gutenberg-Universität Mainz, Langenbeckstraße 1, 55131, Mainz, Deutschland. Tobias.jorg@unimedizin-mainz.de.
  • Christoph Düber
    Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg University Mainz, Germany.
  • Peter Mildenberger
    Klinik und Poliklinik für diagnostische und interventionelle Radiologie, Universitätsmedizin, Johannes-Gutenberg-Universität Mainz, Langenbeckstraße 1, 55131, Mainz, Deutschland.
  • Roman Kloeckner
    Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg University Mainz, Germany.

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