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dc.contributor.authorBardak, Batuhan
dc.contributor.authorTan, Mehmet
dc.date.accessioned2019-07-10T14:42:46Z
dc.date.available2019-07-10T14:42:46Z
dc.date.issued2017
dc.identifier.citationBardak, B., & Tan, M. (2017, August). Disease outbreak prediction by data integration and multi-task learning. In 2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) (pp. 1-7). IEEE.en_US
dc.identifier.isbn978-1-4673-8988-4
dc.identifier.urihttps://ieeexplore.ieee.org/document/8058551
dc.identifier.urihttp://hdl.handle.net/20.500.11851/2020
dc.description2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (2017 : Manchester; United Kingdom)
dc.description.abstractThe requirements for treatments vary for different diseases. These have to be considered in order to plan ahead the expenditures for the health care system. In this sense, disease surveillance has a significant impact on resource planning. To this end, we study the problem of predicting the number of incidences for a given disease based on the internet search and access log statistics. A number of papers appear in the literature that study this problem of predicting outbreaks, especially for Influenza. In this paper, in addition to investigating disease incidences other than Influenza, we propose to use the statistics for different diseases together for achieving transfer learning. We argue that we can increase prediction performance by considering diseases together in a multi-task learning setting due to our assumption of structure sharing. The results we obtained are promising as we achieved performance improvements in this setting. The code and data-sets used in the study are available from http: //mtan.etu.edu.tr/Supplementary/Outbreak-prediction/.en_US
dc.description.sponsorshipThis study was partially supported by STM Defense Technologies Engineering and Trade Inc.
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.relation.ispartof2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectoutbreak predictionen_US
dc.subjectmulti-task learningen_US
dc.subjectdata integrationen_US
dc.titleDisease outbreak prediction by data integration and multi-task learningen_US
dc.typeconferenceObjecten_US
dc.contributor.departmentTOBB ETU, Faculty of Engineering, Department of Computer Engineeringen_US
dc.contributor.departmentTOBB ETÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümütr_TR
dc.identifier.startpage204
dc.identifier.endpage210
dc.contributor.orcidhttps://orcid.org/0000-0002-1741-0570
dc.identifier.wosWOS:000463852500032
dc.identifier.scopus2-s2.0-85034670203
dc.contributor.tobbetuauthorTan, Mehmet
dc.contributor.YOKid110845
dc.identifier.doi10.1109/CIBCB.2017.8058551
dc.contributor.wosresearcherIDI-2328-2019
dc.contributor.ScopusAuthorID36984623900
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıtr_TR


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