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.
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The authors greatly acknowledge the contribution of all colleagues involved in the pollen concentration data collection: Manfred Bobek, Sigmar Bortenschlager, Inez Bortenschlager, Uschi Brosch, Edith Bucher, Bernard Clot, Pramod Harvey, Veronika Kofler, Andreja Kofol-Seliger, Herta Koll, Sabine Kottik, Margit Langanger, Rudolf Litschauer, Karol Micieta, Anna Paldy, Ondrej Rybnicek, Hanna Schantl, Jutta Schmidt, Roland Schmidt, Ingrid Weichenmeier, Helmut Zwander. We would also like to greatly acknowledge all those PHD users contributing information via the PHD.
The authors of this paper state that they have taken into account the ethical standards laid down in the 1964 Declaration of Helsinki. All users of the PHD have given their consent prior to their inclusion in the PHD; in addition, they had the option to give information anonymously. Details that might disclose the identity of the subjects participating in the study have been omitted. All data related to persons that have registered their symptoms and medication use to the PHD have been treated anonymously in this paper. On this basis, no ethical issues arise from this study and from the use of PHD data.
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Voukantsis, D., Karatzas, K., Jaeger, S. et al. Analysis and forecasting of airborne pollen–induced symptoms with the aid of computational intelligence methods. Aerobiologia 29, 175–185 (2013). https://doi.org/10.1007/s10453-012-9271-1
- Computational intelligence
- Symptoms forecasts
- Personalized health services
- Patients Hayfever Diary