Personalized symptoms forecasting for pollen-induced allergic rhinitis sufferers
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, fo...
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| Veröffentlicht in: | International journal of biometeorology 2015-07, Vol.59 (7), p.889-897 |
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| creator | Voukantsis, D. Berger, U. Tzima, F. Karatzas, K. Jaeger, S. Bergmann, K. C. |
| description | 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. |
| doi_str_mv | 10.1007/s00484-014-0905-6 |
| format | Article |
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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.</description><identifier>ISSN: 0020-7128</identifier><identifier>EISSN: 1432-1254</identifier><identifier>DOI: 10.1007/s00484-014-0905-6</identifier><identifier>PMID: 25277722</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Algorithms ; Allergies ; Animal Physiology ; Austria ; Biological and Medical Physics ; Biophysics ; Correlation coefficient ; Earth and Environmental Science ; Environment ; Environmental Health ; Forecasting ; Hay fever ; Health Communication - methods ; Humans ; Immunotherapy ; Meteorology ; Models, Theoretical ; Original Paper ; Plant Physiology ; Pollen ; Pollen - immunology ; Quality of life ; Rhinitis ; Rhinitis, Allergic - immunology ; Rhinitis, Allergic - physiopathology ; Sinuses ; Weather forecasting</subject><ispartof>International journal of biometeorology, 2015-07, Vol.59 (7), p.889-897</ispartof><rights>ISB 2014</rights><rights>ISB 2015</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c475t-3a9b1626d400203d4e11c76de63f6b1c50eece10f95c6b57d25fdda8670459f73</citedby><cites>FETCH-LOGICAL-c475t-3a9b1626d400203d4e11c76de63f6b1c50eece10f95c6b57d25fdda8670459f73</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00484-014-0905-6$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00484-014-0905-6$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/25277722$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Voukantsis, D.</creatorcontrib><creatorcontrib>Berger, U.</creatorcontrib><creatorcontrib>Tzima, F.</creatorcontrib><creatorcontrib>Karatzas, K.</creatorcontrib><creatorcontrib>Jaeger, S.</creatorcontrib><creatorcontrib>Bergmann, K. C.</creatorcontrib><title>Personalized symptoms forecasting for pollen-induced allergic rhinitis sufferers</title><title>International journal of biometeorology</title><addtitle>Int J Biometeorol</addtitle><addtitle>Int J Biometeorol</addtitle><description>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.</description><subject>Algorithms</subject><subject>Allergies</subject><subject>Animal Physiology</subject><subject>Austria</subject><subject>Biological and Medical Physics</subject><subject>Biophysics</subject><subject>Correlation coefficient</subject><subject>Earth and Environmental Science</subject><subject>Environment</subject><subject>Environmental Health</subject><subject>Forecasting</subject><subject>Hay fever</subject><subject>Health Communication - methods</subject><subject>Humans</subject><subject>Immunotherapy</subject><subject>Meteorology</subject><subject>Models, Theoretical</subject><subject>Original Paper</subject><subject>Plant Physiology</subject><subject>Pollen</subject><subject>Pollen - immunology</subject><subject>Quality of life</subject><subject>Rhinitis</subject><subject>Rhinitis, Allergic - immunology</subject><subject>Rhinitis, Allergic - physiopathology</subject><subject>Sinuses</subject><subject>Weather forecasting</subject><issn>0020-7128</issn><issn>1432-1254</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><recordid>eNqFkUtLxDAUhYMoOj5-gBspuHFTvUnzaJcivkBwFroOneRmjPQxJu1Cf72poyKCuAhJyHdODvcQckjhlAKoswjAS54DTasCkcsNMqO8YDllgm-SGQCDXFFW7pDdGJ8haUqptskOE0wpxdiMzOcYYt_VjX9Dm8XXdjX0bcxcH9DUcfDdcjpnq75psMt9Z0eTuDrdwtKbLDz5zg8-ZnF0DkPy2idbrm4iHnzue-Tx6vLh4ia_u7--vTi_yw1XYsiLulpQyaTlU8jCcqTUKGlRFk4uqBGAaJCCq4SRC6EsE87aOsUHLiqnij1ysvZdhf5lxDjo1keDTVN32I9RU5XGUkoQxf-oLIXisqBVQo9_oc_9GNJ4PiiumAQlE0XXlAl9jAGdXgXf1uFVU9BTM3rdjE4R9NSMnjRHn87jokX7rfiqIgFsDcT01C0x_Pj6T9d3ffqYkQ</recordid><startdate>20150701</startdate><enddate>20150701</enddate><creator>Voukantsis, D.</creator><creator>Berger, U.</creator><creator>Tzima, F.</creator><creator>Karatzas, K.</creator><creator>Jaeger, S.</creator><creator>Bergmann, K. 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C.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Personalized symptoms forecasting for pollen-induced allergic rhinitis sufferers</atitle><jtitle>International journal of biometeorology</jtitle><stitle>Int J Biometeorol</stitle><addtitle>Int J Biometeorol</addtitle><date>2015-07-01</date><risdate>2015</risdate><volume>59</volume><issue>7</issue><spage>889</spage><epage>897</epage><pages>889-897</pages><issn>0020-7128</issn><eissn>1432-1254</eissn><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.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>25277722</pmid><doi>10.1007/s00484-014-0905-6</doi><tpages>9</tpages></addata></record> |
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| source | MEDLINE; Springer Nature - Complete Springer Journals |
| subjects | Algorithms Allergies Animal Physiology Austria Biological and Medical Physics Biophysics Correlation coefficient Earth and Environmental Science Environment Environmental Health Forecasting Hay fever Health Communication - methods Humans Immunotherapy Meteorology Models, Theoretical Original Paper Plant Physiology Pollen Pollen - immunology Quality of life Rhinitis Rhinitis, Allergic - immunology Rhinitis, Allergic - physiopathology Sinuses Weather forecasting |
| title | Personalized symptoms forecasting for pollen-induced allergic rhinitis sufferers |
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