@inproceedings{Saha2023, 
author = {Saha$^\circ$, Arpita and Samaan$^\circ$, Maggie and Peng$^\circ$, Bo and 
\textbf{Ning}*$^\dagger$, \textbf{Xia}}, 
title = {A Multi-Layered GRU Model for COVID-19 Patient Representation and Phenotyping from Large-Scale EHR Data},
year = {2023},
isbn = {9798400701269},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3584371.3612986},
doi = {10.1145/3584371.3612986},
abstract = {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.},
booktitle = {Proceedings of the 14th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics},
articleno = {21},
pages = {1-6},
keywords = {deep learning, COVID-19, phenotyping},
location = {Houston, TX, USA},
series = {BCB '23}
}

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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.","booktitle":"Proceedings of the 14th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics","articleno":"21","pages":"1-6","keywords":"deep learning, COVID-19, phenotyping","location":"Houston, TX, USA","series":"BCB '23","bibtex":"@inproceedings{Saha2023, \nauthor = {Saha$^\\circ$, Arpita and Samaan$^\\circ$, Maggie and Peng$^\\circ$, Bo and \n\\textbf{Ning}*$^\\dagger$, \\textbf{Xia}}, \ntitle = {A Multi-Layered GRU Model for COVID-19 Patient Representation and Phenotyping from Large-Scale EHR Data},\nyear = {2023},\nisbn = {9798400701269},\npublisher = {Association for Computing Machinery},\naddress = {New York, NY, USA},\nurl = {https://doi.org/10.1145/3584371.3612986},\ndoi = {10.1145/3584371.3612986},\nabstract = {The unprecedented scale of the COVID-19 pandemic created an alarming shortage of healthcare resources. 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