Large language models vs human for classifying clinical documents.

Journal: International journal of medical informatics
PMID: 39848078

Abstract

BACKGROUND: Accurate classification of medical records is crucial for clinical documentation, particularly when using the 10th revision of the International Classification of Diseases (ICD-10) coding system. The use of machine learning algorithms and Systematized Nomenclature of Medicine (SNOMED) mapping has shown promise in performing these classifications. However, challenges remain, particularly in reducing false negatives, where certain diagnoses are not correctly identified by either approach.

Authors

  • Akram Mustafa
    College of Science and Engineering, James Cook University, Townsville, 4811, QLD, Australia. Electronic address: akram.mohdmustafa@my.jcu.edu.au.
  • Usman Naseem
    School of Computer Science, The University of Sydney, Sydney, Australia. usman.naseem@sydney.edu.au.
  • Mostafa Rahimi Azghadi
    College of Science and Engineering, James Cook University, James Cook Dr, Townsville, 4811, QLD, Australia. Electronic address: mostafa.rahimiazghadi@jcu.edu.au.

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