Exploring the potential and limitations of deep learning and explainable AI for longitudinal life course analysis.

Journal: BMC public health
PMID: 40275204

Abstract

BACKGROUND: Understanding the complex interplay between life course exposures, such as adverse childhood experiences and environmental factors, and disease risk is essential for developing effective public health interventions. Traditional epidemiological methods, such as regression models and risk scoring, are limited in their ability to capture the non-linear and temporally dynamic nature of these relationships. Deep learning (DL) and explainable artificial intelligence (XAI) are increasingly applied within healthcare settings to identify influential risk factors and enable personalised interventions. However, significant gaps remain in understanding their utility and limitations, especially for sparse longitudinal life course data and how the influential patterns identified using explainability are linked to underlying causal mechanisms.

Authors

  • Helen Coupland
    Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark.
  • Neil Scheidwasser
    Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark.
  • Alexandros Katsiferis
    Section for Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark.
  • Megan Davies
    Big Data Centre for Environment and Health (BERTHA), Aarhus University, Aarhus V, Denmark.
  • Seth Flaxman
    Department of Mathematics and Data Science Institute, Imperial College London, London, SW7 2AZ, UK.
  • Naja Hulvej Rod
    Copenhagen Health Complexity Center, Department of Public Health, University of Copenhagen, Copenhagen, Denmark.
  • Swapnil Mishra
    MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK.
  • Samir Bhatt
    Department of Infectious Disease Epidemiology, Imperial College London, London, W2 1PG, UK.
  • H Juliette T Unwin
    School of Mathematics, University of Bristol, Bristol, UK.

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