Transformer-based deep learning model for the diagnosis of suspected lung cancer in primary care based on electronic health record data.

Journal: EBioMedicine
PMID: 39536394

Abstract

BACKGROUND: Due to its late stage of diagnosis lung cancer is the commonest cause of death from cancer in the UK. Existing epidemiological risk models in clinical usage, which have Positive Predictive Values (PPV) of less than 10%, do not consider the temporal relations expressed in sequential electronic health record (EHR) data. We aimed to build a model for lung cancer early detection in primary care using machine learning with deep 'transformer' models on EHR data to learn from these complex sequential 'care pathways'.

Authors

  • Lan Wang
    The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu 611731, China.
  • Yonghua Yin
    Intelligent Systems and Networks Group, Department of Electrical and Electronic Engineering, Imperial College, London SW7 2AZ, UK. y.yin14@imperial.ac.uk.
  • Ben Glampson
    Imperial College Healthcare NHS Trust, London, UK.
  • Robert Peach
    Dept of Brain Sciences, Imperial College London, UK.
  • Mauricio Barahona
  • Brendan C Delaney
    IX, Imperial College London, UK; Dept of Surgery and Cancer, Imperial College London, UK. Electronic address: brendan.delaney@imperial.ac.uk.
  • Erik K Mayer
    ICARE SDE, Imperial College Healthcare NHS Trust, London, UK; Dept of Surgery and Cancer, Imperial College London, UK.

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