TY - GEN
T1 - A Multi-Layered GRU Model for COVID-19 Patient Representation and Phenotyping from Large-Scale EHR Data
AU - Saha, Arpita
AU - Samaan, Maggie
AU - Peng, Bo
AU - Ning, Xia
N1 - Publisher Copyright:
© 2023 Owner/Author(s).
PY - 2023/9/3
Y1 - 2023/9/3
N2 - The unprecedented scale of the COVID-19 pandemic created an alarming shortage of healthcare resources. To enable a more efficient resource allocation and targeted treatment, in this manuscript, we conducted a data-driven study of COVID-19 patients to predict patient outcomes and identify patient phenotypes. Specifically, we developed a multi-layered gated recurrent units-based model, referred to as mGRU-CP, to learn patient embeddings and estimate patient survival probabilities by leveraging their electronic health record (EHR) data in the COVID-19 Research Data Commons. We empirically compared mGRU-CP against four state-of-the-art baseline methods on three sets of patient features. The experimental results demonstrate that mGRU-CP could achieve competitive or superior performance over the baseline methods in all the settings. Our analysis also shows that the learned patient embeddings in mGRU-CP could enable meaningful patient phenotyping to better understand patient mortalities. Our study is significant in understanding patients in the past COVID-19 pandemic, and provides computational tools to predict patient outcomes and inform associated healthcare resource allocation for the future pandemics proactively.
AB - The unprecedented scale of the COVID-19 pandemic created an alarming shortage of healthcare resources. To enable a more efficient resource allocation and targeted treatment, in this manuscript, we conducted a data-driven study of COVID-19 patients to predict patient outcomes and identify patient phenotypes. Specifically, we developed a multi-layered gated recurrent units-based model, referred to as mGRU-CP, to learn patient embeddings and estimate patient survival probabilities by leveraging their electronic health record (EHR) data in the COVID-19 Research Data Commons. We empirically compared mGRU-CP against four state-of-the-art baseline methods on three sets of patient features. The experimental results demonstrate that mGRU-CP could achieve competitive or superior performance over the baseline methods in all the settings. Our analysis also shows that the learned patient embeddings in mGRU-CP could enable meaningful patient phenotyping to better understand patient mortalities. Our study is significant in understanding patients in the past COVID-19 pandemic, and provides computational tools to predict patient outcomes and inform associated healthcare resource allocation for the future pandemics proactively.
KW - COVID-19
KW - deep learning
KW - phenotyping
UR - http://www.scopus.com/inward/record.url?scp=85175790612&partnerID=8YFLogxK
U2 - 10.1145/3584371.3612986
DO - 10.1145/3584371.3612986
M3 - Conference contribution
AN - SCOPUS:85175790612
T3 - ACM-BCB 2023 - 14th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics
BT - ACM-BCB 2023 - 14th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics
PB - Association for Computing Machinery, Inc
T2 - 14th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, ACM-BCB 2023
Y2 - 3 September 2023 through 6 September 2023
ER -