An AI-driven framework for the prediction of personalised health response to air pollution

• URL: http://arxiv.org/abs/2505.10556v1
• Date: Thu, 15 May 2025 17:59:07 GMT
• Title: An AI-driven framework for the prediction of personalised health response to air pollution
• Authors: Nazanin Zounemat Kermani, Sadjad Naderi, Claire H. Dilliway, Claire E. Heaney, Shrreya Behll, Boyang Chen, Hisham Abubakar-Waziri, Alexandra E. Porter, Marc Chadeau-Hyam, Fangxin Fang, Ian M. Adcock, Kian Fan Chung, Christopher C. Pain,
• Abstract summary: Air pollution poses a significant threat to public health, causing or exacerbating many respiratory and cardiovascular diseases.<n>Recent advances in personal sensing have transformed the collection of behavioural and physiological data.<n>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.
• Score: 30.858937705130106
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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