Ontology-driven and weakly supervised rare disease identification from clinical notes.

Journal: BMC medical informatics and decision making
Published Date: May 5, 2023

Abstract

BACKGROUND: Computational text phenotyping is the practice of identifying patients with certain disorders and traits from clinical notes. Rare diseases are challenging to be identified due to few cases available for machine learning and the need for data annotation from domain experts.

Authors

  • Hang Dong
    Centre for Medical Informatics, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, United Kingdom; Health Data Research UK, London, United Kingdom. Electronic address: hang.dong@ed.ac.uk.
  • Víctor Suárez-Paniagua
    Department of Computer Science, University Carlos III of Madrid Leganés 28911, Madrid, Spain.
  • Huayu Zhang
    Einthoven Laboratory for Vascular and Regenerative Medicine, Department of Internal Medicine, Leiden University Medical Center, Albinusdreef, 22333 ZA Leiden, The Netherlands.
  • Minhong Wang
    Institute of Health Informatics, University College London, London, United Kingdom.
  • Arlene Casey
    School of Literatures, Languages and Cultures (LLC), University of Edinburgh, Edinburgh, Scotland, UK.
  • Emma Davidson
    Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom.
  • Jiaoyan Chen
    College of Computer Science, Zhejiang University, Hangzhou, China.
  • Beatrice Alex
    The Alan Turing Institute, British Library, 96 Euston Road, London, UK.
  • William Whiteley
    Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK. william.whiteley@ed.ac.uk.
  • Honghan Wu
    University College London, London, United Kingdom.

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