Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/8616
Title: Using clinical drug representations for improving mortality and length of stay predictions
Authors: Bardak B.
Tan, Mehmet
Keywords: Clinical drug representation
Clinical task
Deep learning
EHR
Healthcare
Bioinformatics
Deep learning
Forecasting
Time series
Cheminformatics
Clinical drug representation
Clinical drugs
Clinical outcome
Clinical tasks
Deep learning
Healthcare domains
High-dimensional
Length of stay
Receiver operating characteristics
Health care
Issue Date: 2021
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: Bardak, B., & Tan, M. (2021, October). Using Clinical Drug Representations for Improving Mortality and Length of Stay Predictions. In 2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) (pp. 1-8). IEEE.
Abstract: Drug representations have played an important role in cheminformatics. However, in the healthcare domain, drug representations have been underused relative to the rest of Electronic Health Record (EHR) data, due to the complexity of high dimensional drug representations and the lack of proper pipeline that will allow to convert clinical drugs to their representations. Time-varying vital signs, laboratory measurements, and related time-series signals are commonly used to predict clinical outcomes. In this work, we demonstrated that using clinical drug representations in addition to other clinical features has significant potential to increase the performance of mortality and length of stay (LOS) models. We evaluate the two different drug representation methods (Extended-Connectivity Fingerprint-ECFP and SMILES-Transformer embedding) on clinical outcome predictions. The results have shown that the proposed multimodal approach achieves substantial enhancement on clinical tasks over baseline models. Using clinical drug representations as additional features improve the LOS prediction for Area Under the Receiver Operating Characteristics (AUROC) around %6 and for Area Under Precision-Recall Curve (AUPRC) by around %5. Furthermore, for the mortality prediction task, there is an improvement of around %2 over the time series baseline in terms of AUROC and %3.5 in terms of AUPRC. The code for the proposed method is available at https://github.com/tanlab/MIMIC-III-Clinical-Drug-Representations. © 2021 IEEE.
Description: 2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2021 -- 13 October 2021 through 15 October 2021 -- -- 176925
URI: https://doi.org/10.1109/CIBCB49929.2021.9562819
https://hdl.handle.net/20.500.11851/8616
ISBN: 9781665401128
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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