An AI-driven framework for the prediction of personalised health response to air pollution
Journal:
arXiv
Published Date:
May 15, 2025
Abstract
Air pollution poses a significant threat to public health, causing or
exacerbating many respiratory and cardiovascular diseases. In addition, climate
change is bringing about more extreme weather events such as wildfires and
heatwaves, which can increase levels of pollution and worsen the effects of
pollution exposure. Recent advances in personal sensing have transformed the
collection of behavioural and physiological data, leading to the potential for
new improvements in healthcare. We wish to capitalise on this data, alongside
new capabilities in AI for making time series predictions, in order to monitor
and predict health outcomes for an individual. Thus, we present a novel
workflow for predicting personalised health responses to pollution by
integrating physiological data from wearable fitness devices with real-time
environmental exposures. The data is collected from various sources in a secure
and ethical manner, and is used to train an AI model to predict individual
health responses to pollution exposure within a cloud-based, modular framework.
We demonstrate that the AI model -- an Adversarial Autoencoder neural network
in this case -- accurately reconstructs time-dependent health signals and
captures nonlinear responses to pollution. Transfer learning is applied using
data from a personal smartwatch, which increases the generalisation abilities
of the AI model and illustrates the adaptability of the approach to real-world,
user-generated data.
