Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.11851/8189
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Bardak, B. | - |
dc.contributor.author | Tan, V.M. | - |
dc.date.accessioned | 2022-01-15T13:00:34Z | - |
dc.date.available | 2022-01-15T13:00:34Z | - |
dc.date.issued | 2021 | - |
dc.identifier.isbn | 9781665436496 | - |
dc.identifier.uri | https://doi.org/10.1109/SIU53274.2021.9477707 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.11851/8189 | - |
dc.description | 29th IEEE Conference on Signal Processing and Communications Applications, SIU 2021 -- 9 June 2021 through 11 June 2021 -- 170536 | en_US |
dc.description.abstract | Predicting 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.iso | tr | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en_US |
dc.relation.ispartof | SIU 2021 - 29th IEEE Conference on Signal Processing and Communications Applications, Proceedings | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | BERT | en_US |
dc.subject | Deep learning | en_US |
dc.subject | Electronic health record | en_US |
dc.subject | Healthcare | en_US |
dc.subject | Forecasting | en_US |
dc.subject | Intensive care units | en_US |
dc.subject | Signal processing | en_US |
dc.subject | Baseline models | en_US |
dc.subject | Complex nature | en_US |
dc.subject | Electronic health record | en_US |
dc.subject | Healthcare domains | en_US |
dc.subject | Length of stay | en_US |
dc.subject | Multi-modal approach | en_US |
dc.subject | Structural data | en_US |
dc.subject | Time series features | en_US |
dc.subject | Deep learning | en_US |
dc.title | Prediction of mortality and length of stay with deep learning | en_US |
dc.title.alternative | Derin öğrenme yöntemleri ile mortalite ve hastanede kalma süresinin tahmini | en_US |
dc.type | Conference Object | en_US |
dc.department | Faculties, Faculty of Engineering, Department of Computer Engineering | en_US |
dc.department | Fakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü | tr_TR |
dc.identifier.wos | WOS:000808100700011 | en_US |
dc.identifier.scopus | 2-s2.0-85111467354 | en_US |
dc.institutionauthor | Tan, Mehmet | - |
dc.identifier.doi | 10.1109/SIU53274.2021.9477707 | - |
dc.authorscopusid | 57188767392 | - |
dc.authorscopusid | 57226401969 | - |
dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
item.cerifentitytype | Publications | - |
item.fulltext | No Fulltext | - |
item.grantfulltext | none | - |
item.openairetype | Conference Object | - |
item.languageiso639-1 | tr | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
crisitem.author.dept | 02.3. 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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