A Transformer-Based Framework for Counterfactual Estimation of Antihypertensive Treatment Effect on COVID-19 Infection Risk - A Proof-of-Concept Study.

Journal: American journal of hypertension
Published Date: Jul 15, 2025

Abstract

BACKGROUND: Transformer-based neural networks excel in modelling high-dimensional, time-series data with complex dependencies. This proof-of-concept study applies a transformer-X-learner framework to estimate treatment effects using real-world data, using antihypertensive drug exposure and COVID-19 risk as an exemplar.

Authors

  • Tran Q B Tran
    School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, UK.
  • Stefanie Lip
    School of Cardiovascular and Metabolic Health University of Glasgow Glasgow United Kingdom.
  • Honghan Wu
    University College London, London, United Kingdom.
  • Shyam Visweswaran
    University of Pittsburgh, Pittsburgh, PA, USA.
  • Jill P Pell
    Institute of Health & Wellbeing, University of Glasgow, Glasgow, Scotland, United Kingdom.
  • Sandosh Padmanabhan
    BHF Glasgow Cardiovascular Research Centre, Institute of Cardiovascular and Medical Sciences, University of Glasgow.

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