Uncovering Variations in Clinical Notes for NLP Modeling.

Journal: Studies in health technology and informatics
Published Date: Jan 25, 2024

Abstract

Clinical text contains rich patient information and has attracted much research interest in applying Natural Language Processing (NLP) tools to model it. In this study, we quantified and analyzed the textual characteristics of five common clinical note types using multiple measurements, including lexical-level features, semantic content, and grammaticality. We found there exist significant linguistic variations in different clinical note types, while some types tend to be more similar than others.

Authors

  • Jinghui Liu
    School of Information, University of Michigan, Ann Arbor, Michigan, USA.
  • Daniel Capurro
    School of Computing and Information Systems, The University of Melbourne, Victoria, Australia; Centre for Digital Transformation of Health, Melbourne Medical School, The University of Melbourne, Victoria, Australia. Electronic address: dcapurro@unimelb.edu.au.
  • Anthony Nguyen
    Australian e-Health Research Centre, CSIRO, Brisbane, QLD, Australia.
  • Karin Verspoor
    Dept of Computing and Information Systems, School of Engineering, University of Melbourne, Melbourne, Australia.

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