Learning to model pediatric asthma exacerbation from multiple risk factors: a case study in coastal Virginia
arXiv:2606.06174v1 Announce Type: new Abstract: Childhood asthma is a common illness exacerbated by air pollution as well as meteorological and neighborhood-level socioeconomic factors. Modeling asthma exacerbation (AE) in large spatiotemporal datasets requires disentangling impacts from multiple contributors. In this case study, we compared three techniques that balance predictive power with interpretability to predict AE in Hampton Roads, a coastal Virginia region comprising 7 cities and over 1.5 million people. After collating ambient air pollution measurements, weather data, and measures o
The increasing availability of large spatiotemporal datasets and advanced AI/ML techniques allows for more sophisticated modeling of complex health conditions influenced by multiple environmental and socioeconomic factors.
This research demonstrates progress in using AI to understand and predict health outcomes, which could lead to better public health interventions and resource allocation, especially in regions vulnerable to environmental changes.
The ability to more accurately model and predict health exacerbations from diverse risk factors provides a clearer path for targeted preventative strategies and personalized medicine approaches.
- · Public health researchers
- · Healthcare providers
- · AI/ML developers
- · Coastal communities
- · Chronic disease incidence rates (potentially)
Improved early warning systems for asthma exacerbations in vulnerable populations.
Development of more effective, data-driven public health policies to mitigate environmental health risks.
Integration of environmental and socioeconomic AI models into broader urban planning and healthcare infrastructure for preventative care.
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Read at arXiv cs.LG