An AI-driven framework for the prediction of personalised health response to air pollution
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arXiv
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915724782469120 |
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| author | Zounemat-Kermani, Nazanin Naderi, Sadjad Dilliway, Claire H. Heaney, Claire E. Behll, Shrreya Chen, Boyang Abubakar-Waziri, Hisham Porter, Alexandra E. Chadeau-Hyam, Marc Fang, Fangxin Adcock, Ian M. Chung, Kian Fan Pain, Christopher C. |
| author_facet | Zounemat-Kermani, Nazanin Naderi, Sadjad Dilliway, Claire H. Heaney, Claire E. Behll, Shrreya Chen, Boyang Abubakar-Waziri, Hisham Porter, Alexandra E. Chadeau-Hyam, Marc Fang, Fangxin Adcock, Ian M. Chung, Kian Fan Pain, Christopher C. |
| contents | Air pollution is a growing global health threat, exacerbated by climate change and linked to cardiovascular and respiratory diseases. While personal sensing devices enable real-time physiological monitoring, their integration with environmental data for individualised health prediction remains underdeveloped. Here, we present a modular, cloud-based framework that predicts personalised physiological responses to pollution by combining wearable-derived data with real-time environmental exposures. At its core is an Adversarial Autoencoder (AAE), initially trained on high-resolution pollution-health data from the INHALE study and fine-tuned using smartwatch data via transfer learning to capture individual-specific patterns. Consistent with changes in pollution levels commonly observed in the real-world, simulated pollution spikes (+100%) revealed modest but measurable increases in vital signs (e.g., +2.5% heart rate, +3.5% breathing rate). To assess clinical relevance, we analysed U-BIOPRED data and found that individuals with such subclinical vital sign elevations had higher asthma burden scores or elevated Fractional Exhaled Nitric Oxide (FeNO), supporting the physiological validity of these AI-predicted responses. This integrative approach demonstrates the feasibility of anticipatory, personalised health modelling in response to environmental challenges, offering a scalable and secure infrastructure for AI-driven environmental health monitoring. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_10556 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An AI-driven framework for the prediction of personalised health response to air pollution Zounemat-Kermani, Nazanin Naderi, Sadjad Dilliway, Claire H. Heaney, Claire E. Behll, Shrreya Chen, Boyang Abubakar-Waziri, Hisham Porter, Alexandra E. Chadeau-Hyam, Marc Fang, Fangxin Adcock, Ian M. Chung, Kian Fan Pain, Christopher C. Machine Learning Atmospheric and Oceanic Physics Air pollution is a growing global health threat, exacerbated by climate change and linked to cardiovascular and respiratory diseases. While personal sensing devices enable real-time physiological monitoring, their integration with environmental data for individualised health prediction remains underdeveloped. Here, we present a modular, cloud-based framework that predicts personalised physiological responses to pollution by combining wearable-derived data with real-time environmental exposures. At its core is an Adversarial Autoencoder (AAE), initially trained on high-resolution pollution-health data from the INHALE study and fine-tuned using smartwatch data via transfer learning to capture individual-specific patterns. Consistent with changes in pollution levels commonly observed in the real-world, simulated pollution spikes (+100%) revealed modest but measurable increases in vital signs (e.g., +2.5% heart rate, +3.5% breathing rate). To assess clinical relevance, we analysed U-BIOPRED data and found that individuals with such subclinical vital sign elevations had higher asthma burden scores or elevated Fractional Exhaled Nitric Oxide (FeNO), supporting the physiological validity of these AI-predicted responses. This integrative approach demonstrates the feasibility of anticipatory, personalised health modelling in response to environmental challenges, offering a scalable and secure infrastructure for AI-driven environmental health monitoring. |
| title | An AI-driven framework for the prediction of personalised health response to air pollution |
| topic | Machine Learning Atmospheric and Oceanic Physics |
| url | https://arxiv.org/abs/2505.10556 |