Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/8189
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dc.contributor.authorBardak, B.-
dc.contributor.authorTan, V.M.-
dc.date.accessioned2022-01-15T13:00:34Z-
dc.date.available2022-01-15T13:00:34Z-
dc.date.issued2021-
dc.identifier.isbn9781665436496-
dc.identifier.urihttps://doi.org/10.1109/SIU53274.2021.9477707-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/8189-
dc.description29th IEEE Conference on Signal Processing and Communications Applications, SIU 2021 -- 9 June 2021 through 11 June 2021 -- 170536en_US
dc.description.abstractPredicting the mortality and the length of stay of patients during ICU stay is important for better acute care and planning of ICU resources. The recent advancements in deep learning and with the Electronic Health Record data becoming available for researchers, there has been an increasing interest in the healthcare domain. While patient's structural data are frequently used, most of the studies in the literature do not use the clinical notes due to the complex nature. In this study, we use the clinical notes besides time-series features to improve our predictions. The results have shown that the proposed deep learning based multimodal approach outperforms on all clinical tasks over baseline models. © 2021 IEEE.en_US
dc.language.isotren_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofSIU 2021 - 29th IEEE Conference on Signal Processing and Communications Applications, Proceedingsen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBERTen_US
dc.subjectDeep learningen_US
dc.subjectElectronic health recorden_US
dc.subjectHealthcareen_US
dc.subjectForecastingen_US
dc.subjectIntensive care unitsen_US
dc.subjectSignal processingen_US
dc.subjectBaseline modelsen_US
dc.subjectComplex natureen_US
dc.subjectElectronic health recorden_US
dc.subjectHealthcare domainsen_US
dc.subjectLength of stayen_US
dc.subjectMulti-modal approachen_US
dc.subjectStructural dataen_US
dc.subjectTime series featuresen_US
dc.subjectDeep learningen_US
dc.titlePrediction of Mortality and Length of Stay With Deep Learningen_US
dc.title.alternativeDerin Öğrenme Yöntemleri ile Mortalite ve Hastanede Kalma Süresinin Tahminien_US
dc.typeConference Objecten_US
dc.departmentFaculties, Faculty of Engineering, Department of Computer Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümütr_TR
dc.identifier.wosWOS:000808100700011en_US
dc.identifier.scopus2-s2.0-85111467354en_US
dc.institutionauthorTan, Mehmet-
dc.identifier.doi10.1109/SIU53274.2021.9477707-
dc.authorscopusid57188767392-
dc.authorscopusid57226401969-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
item.openairetypeConference Object-
item.languageiso639-1tr-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
crisitem.author.dept02.3. Department of Computer Engineering-
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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