Automated Identification of Clinical Procedures in Free-Text Electronic Clinical Records with a Low-Code Named Entity Recognition Workflow.

Journal: Methods of information in medicine
PMID: 36096143

Abstract

BACKGROUND: Clinical procedures are often performed in outpatient clinics without prior scheduling at the administrative level, and documentation of the procedure often occurs solely in free-text clinical electronic notes. Natural language processing (NLP), particularly named entity recognition (NER), may provide a solution to extracting procedure data from free-text electronic notes.

Authors

  • Carmelo Macri
    Machine Learning Division, Ophthalmic Research Laboratory, University of Adelaide, Adelaide, South Australia, Australia.
  • Ian Teoh
    Machine Learning Division, Ophthalmic Research Laboratory, University of Adelaide, Adelaide, South Australia, Australia.
  • Stephen Bacchi
    Faculty of Health and Medical Sciences, Adelaide Medical School, University of Adelaide, Adelaide, SA 5000 Australia.
  • Michelle Sun
    Department of Ophthalmology, Royal Adelaide Hospital, Adelaide, South Australia, Australia.
  • Dinesh Selva
    South Australian Institute of Ophthalmology, Royal Adelaide Hospital, Adelaide, South Australia, Australia.
  • Robert Casson
    School of Medicine, University of Adelaide, Health & Medical Sciences Building, 4 North Terrace, Adelaide, SA, 5000, Australia.
  • WengOnn Chan
    South Australian Institute of Ophthalmology, Royal Adelaide Hospital, Adelaide, South Australia, Australia.

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