Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/8189
Title: Prediction of mortality and length of stay with deep learning
Other Titles: Derin öğrenme yöntemleri ile mortalite ve hastanede kalma süresinin tahmini
Authors: Bardak, B.
Tan, V.M.
Keywords: BERT
Deep learning
Electronic health record
Healthcare
Forecasting
Intensive care units
Signal processing
Baseline models
Complex nature
Electronic health record
Healthcare domains
Length of stay
Multi-modal approach
Structural data
Time series features
Deep learning
Issue Date: 2021
Publisher: Institute of Electrical and Electronics Engineers Inc.
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.
Description: 29th IEEE Conference on Signal Processing and Communications Applications, SIU 2021 -- 9 June 2021 through 11 June 2021 -- 170536
URI: https://doi.org/10.1109/SIU53274.2021.9477707
https://hdl.handle.net/20.500.11851/8189
ISBN: 9781665436496
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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