From admission to discharge: a systematic review of clinical natural language processing along the patient journey.

Journal: BMC medical informatics and decision making
Published Date: Aug 29, 2024

Abstract

BACKGROUND: Medical text, as part of an electronic health record, is an essential information source in healthcare. Although natural language processing (NLP) techniques for medical text are developing fast, successful transfer into clinical practice has been rare. Especially the hospital domain offers great potential while facing several challenges including many documents per patient, multiple departments and complex interrelated processes.

Authors

  • Katrin Klug
    Fraunhofer IAIS, Sankt Augustin, Germany. katrin.klug@iais.fraunhofer.de.
  • Katharina Beckh
    Fraunhofer IAIS, Sankt Augustin, Germany.
  • Dario Antweiler
    Fraunhofer Institut für Intelligente Analyse und Informationssysteme IAIS, Abteilung Knowledge Discovery, Schloss Birlinghoven 1, 53757, Sankt Augustin, Deutschland. dario.antweiler@iais.fraunhofer.de.
  • Nilesh Chakraborty
    Fraunhofer IAIS, Sankt Augustin, Germany.
  • Giulia Baldini
    Institute for Artificial Intelligence in Medicine, University Medicine Essen, Essen, Germany.
  • Katharina Laue
    West German Cancer Centre, University Hospital Essen, Essen, Germany.
  • René Hosch
    Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Germany.
  • Felix Nensa
    Institute for AI in Medicine (IKIM), University Hospital Essen, 45131 Essen, Germany.
  • Martin Schuler
    Department of Medical Oncology, West German Cancer Center, University Hospital Essen (AöR), Essen, Germany.
  • Sven Giesselbach
    Fraunhofer IAIS, Sankt Augustin, Germany.

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