%0 Conference Proceedings
%T Investigation of Medication Dosage Influences from Biological Weather
%+ Aristotle University of Thessaloniki
%+ Göteborgs Universitet = University of Gothenburg (GU)
%A Karatzas, Kostas
%A Riga, Marina
%A Voukantsis, Dimitris
%A Dahl, Åslög
%Z Part 20: Informatics and Intelligent Systems Applications for Quality of Life information Services (ISQLIS) Workshop
%< avec comité de lecture
%( IFIP Advances in Information and Communication Technology
%B 12th Engineering Applications of Neural Networks (EANN 2011) and 7th Artificial Intelligence Applications and Innovations (AIAI)
%C Corfu, Greece
%Y Lazaros Iliadis
%Y Chrisina Jayne
%I Springer
%3 Engineering Applications of Neural Networks
%V AICT-363
%N Part I
%P 481-490
%8 2011-09-15
%D 2011
%R 10.1007/978-3-642-23957-1_53
%K Allergy
%K Pollen
%K Medication Dosage Forecasting
%K Information Gain Criterion
%K Self-Organizing Maps
%K Decision Trees
%Z Computer Science [cs]Conference papers
%X Airborne pollen has been associated with allergic symptoms in sensitized individuals, whereas atmospheric pollution indisputably aggravates the impact on the overall quality of life. Therefore, it is of major importance to correlate, forecast and disseminate information concerning high concentration levels of allergic pollen types and air pollutants to the public, in order to safeguard the quality of life of the population. In this study, we investigate the relationship between the Defined Daily Dose (DDD) given to patients in a triggered allergy reaction and the different levels of air pollutants and pollen types. By profiling specific atmospheric conditions, specialists may define the need for medication to individuals suffering from pollen allergy, not only according to their personal medical record but also to the existing air quality observations. Paper results indicate some interesting interrelationships between the use of medication and atmospheric quality conditions and shows that the forecasting of daily medication is possible with the aid of proper algorithms.
%G English
%Z TC 12
%Z WG 12.5
%2 https://inria.hal.science/hal-01571373/document
%2 https://inria.hal.science/hal-01571373/file/978-3-642-23957-1_53_Chapter.pdf
%L hal-01571373
%U https://inria.hal.science/hal-01571373
%~ IFIP
%~ IFIP-AICT
%~ IFIP-TC
%~ IFIP-WG
%~ IFIP-TC12
%~ IFIP-AIAI
%~ IFIP-WG12-5
%~ IFIP-AICT-363
%~ IFIP-EANN