A Natural Language Processing and Machine Learning Approach to Identification of Incidental Radiology Findings in Trauma Patients Discharged from the Emergency Department.

Journal: Annals of emergency medicine
Published Date: Oct 31, 2022

Abstract

STUDY OBJECTIVE: Patients undergoing diagnostic imaging studies in the emergency department (ED) commonly have incidental findings, which may represent unrecognized serious medical conditions, including cancer. Recognition of incidental findings frequently relies on manual review of textual radiology reports and can be overlooked in a busy clinical environment. Our study aimed to develop and validate a supervised machine learning model using natural language processing to automate the recognition of incidental findings in radiology reports of patients discharged from the ED.

Authors

  • Christopher S Evans
    Information Services, ECU Health, Greenville, NC; Department of Emergency Medicine, Brody School of Medicine, East Carolina University, Greenville, NC. Electronic address: Chris.s.evans.md@gmail.com.
  • Hugh D Dorris
    Department of Medicine, the University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC.
  • Michael T Kane
    UNC Hospitals Clinical Informatics Fellowship Program, UNC Hospitals, Chapel Hill, NC; Department of Psychiatry, the University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC.
  • Benjamin Mervak
    Department of Radiology, University of Michigan, Ann Arbor, MI.
  • Jane H Brice
    Department of Emergency Medicine, the University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC.
  • Benjamin Gray
    School of Medicine, the University of North Carolina at Chapel Hill, Chapel Hill, NC.
  • Carlton Moore
    Department of Medicine, the University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC; UNC Hospitals Clinical Informatics Fellowship Program, UNC Hospitals, Chapel Hill, NC.

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