Using natural language processing to identify emergency department patients with incidental lung nodules requiring follow-up.

Journal: Academic emergency medicine : official journal of the Society for Academic Emergency Medicine
PMID: 39821298

Abstract

OBJECTIVES: For emergency department (ED) patients, lung cancer may be detected early through incidental lung nodules (ILNs) discovered on chest CTs. However, there are significant errors in the communication and follow-up of incidental findings on ED imaging, particularly due to unstructured radiology reports. Natural language processing (NLP) can aid in identifying ILNs requiring follow-up, potentially reducing errors from missed follow-up. We sought to develop an open-access, three-step NLP pipeline specifically for this purpose.

Authors

  • Christopher L Moore
    Department of Emergency Medicine, Yale University School of Medicine, New Haven CT, United States of America.
  • Vimig Socrates
    Department for Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT, United States; Program of Computational Biology and Bioinformaticsm Yale University, New Haven, CT, United States.
  • Mina Hesami
    Department of Emergency Medicine, Yale University, New Haven, Connecticut, USA.
  • Ryan P Denkewicz
    Department of Emergency Medicine, Yale University, New Haven, Connecticut, USA.
  • Joe J Cavallo
    Department of Radiology and Biomedical Imaging, Yale University, New Haven, Connecticut, USA.
  • Arjun K Venkatesh
    Department of Emergency Medicine, Yale University, Yale-New Haven Hospital, New Haven, CT.
  • R Andrew Taylor
    Department of Emergency Medicine, Yale School of Medicine, New Haven, Connecticut.

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