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

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'.

Methods

We split the Whole Systems Integrated Care (WSIC) dataset into 70% training and 30% validation. Within the training set we created a case-control study with lung cancer cases and control cases of 'other' cancers or respiratory conditions or 'other' n

SUBMITTER: Wang L 

PROVIDER: S-EPMC11605461 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature