Comparison of an Ensemble of Machine Learning Models and the BERT Language Model for Analysis of Text Descriptions of Brain CT Reports to Determine the Presence of Intracranial Hemorrhage.

Journal: Sovremennye tekhnologii v meditsine
Published Date: Feb 28, 2024

Abstract

UNLABELLED: is to train and test an ensemble of machine learning models, as well as to compare its performance with the BERT language model pre-trained on medical data to perform simple binary classification, i.e., determine the presence/absence of the signs of intracranial hemorrhage (ICH) in brain CT reports.

Authors

  • A N Khoruzhaya
    Moscow Research Practical Clinical Center for Diagnostics and Telemedicine Technologies, Moscow, Russia.
  • D V Kozlov
    Junior Researcher, Department of Medical Informatics, Radiomics and Radiogenomics; Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department, Bldg 1, 24 Petrovka St., Moscow, 127051, Russia.
  • K M Arzamasov
    Head of the Department of Medical Informatics, Radiomics and Radiogenomics; Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department, Bldg 1, 24 Petrovka St., Moscow, 127051, Russia.
  • E I Kremneva
    Leading Researcher, Department of Innovative Thechnologies; Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department, Bldg 1, 24 Petrovka St., Moscow, 127051, Russia; Senior Researcher; Research Center of Neurology, 80 Volokolamskoye Shosse, Moscow, 125367, Russia.

Keywords

External Resources

Popular Topics
Recent Journals