Analysis and forecasting of airborne pollen-induced symptoms with the aid of computational intelligence methods

Allergies due to airborne pollen affect a considerable percentage of Europeans; thus, the provision of health-related information services concerning pollen-induced symptoms can improve the overall quality of life. In this paper, we demonstrate the development of personalized, health-related, quality-of-life information services by adopting a data-driven approach. The data we use consist of allergic symptoms reported by people as well as detailed pollen count information of the most allergenic taxa. We apply computational intelligence methods in order to analyze symptoms, identify possible interrelationships with several pollen taxa and develop models that associate pollen count levels with allergic symptoms on a personal level. The results for the case of Austria show that this approach can lead to accurate personalized symptom forecasting models; we report an average correlation coefficient of r = 0.70 for a sample of 102 users of the Patients Hayfever Diary. We conclude that some of these models could serve as the basis for personalized health information services.