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Personalized symptoms forecasting for pollen-induced allergic rhinitis sufferers

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Abstract

Hay fever is a pollen-induced allergic reaction that strongly affects the overall quality of life of many individuals. The disorder may vary in severity and symptoms depending on patient-specific factors such as genetic disposition, individual threshold of pollen concentration levels, medication, former immunotherapy, and others. Thus, information services that improve the quality of life of hay fever sufferers must address the needs of each individual separately. In this paper, we demonstrate the development of information services that offer personalized pollen-induced symptoms forecasts. The backbone of these services consists of data of allergic symptoms reported by the users of the Personal Hay Fever Diary system and pollen concentration levels (European Aeroallergen Network) in several sampling sites. Data were analyzed using computational intelligence methods, resulting in highly customizable forecasting models that offer personalized warnings to users of the Patient Hay Fever Diary system. The overall system performance for the pilot area (Vienna and Lower Austria) reached a correlation coefficient of r = 0.71 ± 0.17 (average ± standard deviation) in a sample of 219 users with major contribution to the Pollen Hay Fever Diary system and an overall performance of r = 0.66 ± 0.18 in a second sample of 393 users, with minor contribution to the system. These findings provide an example of combining data from different sources using advanced data engineering in order to develop innovative e-health services with the capacity to provide more direct and personalized information to allergic rhinitis sufferers.

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Acknowledgments

This research has been partly founded by the Research Committee of Aristotle University of Thessaloniki through the “Excellence Fellowships for Postdoctoral Studies” program.

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Authors and Affiliations

  1. Aristotle University, P.O. Box 483, GR-54124, Thessaloniki, Greece

    D. Voukantsis, F. Tzima & K. Karatzas

  2. Medical University, Wien, Austria

    U. Berger & S. Jaeger

  3. Allergy Center Charité, Berlin, Germany

    K. C. Bergmann

Authors
  1. D. Voukantsis
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  2. U. Berger
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  3. F. Tzima
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  4. K. Karatzas
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  5. S. Jaeger
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  6. K. C. Bergmann
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Corresponding author

Correspondence to D. Voukantsis.

Appendix A: algorithmic parameters

Appendix A: algorithmic parameters

MLP parameters

We evaluated network architectures consisting of one hidden layer, setting the number of neurons equal to n = (inputs + outputs)/2, where inputs is the number of input variables of the model and outputs is the number of output variables (in this case, outputs = 1). We used the Levenberg-Marquardt back-propagation algorithm during the training of the models, with a maximum number of epochs set to 500.

kNN parameters

We used the Euclidean distance as the metric of the kNN algorithm. The number of nearest neighbors used was k = 5, while the simple average was used to combine existing training examples.

M5P parameters

We set the minimum number of instances (M) per node at M = 4.

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Voukantsis, D., Berger, U., Tzima, F. et al. Personalized symptoms forecasting for pollen-induced allergic rhinitis sufferers. Int J Biometeorol 59, 889–897 (2015). https://doi.org/10.1007/s00484-014-0905-6

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  • DOI: https://doi.org/10.1007/s00484-014-0905-6

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