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Hierarchical Gaussian Processes and Mixtures of Experts to Model COVID-19 Patient Trajectories
View ORCID ProfileSunny Cui, View ORCID ProfileElizabeth C. Yoo, View ORCID ProfileDidong Li, Krzysztof Laudanski, View ORCID ProfileBarbara E. Engelhardt
doi: https://doi.org/10.1101/2021.10.01.462821
Sunny Cui
1Department of Computer Science, Princeton University, Princeton, NJ, USA
Elizabeth C. Yoo
2Department of Operations Research and Financial Engineering, Princeton University, Princeton, NJ, USA
Didong Li
1Department of Computer Science, Princeton University, Princeton, NJ, USA
3Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA, USA
Krzysztof Laudanski
4Department of Anesthesiology and Critical Care, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
Barbara E. Engelhardt
1Department of Computer Science, Princeton University, Princeton, NJ, USA
5Center for Statistics and Machine Learning, Princeton University, Princeton, NJ, USA
6Gladstone Institutes, San Francisco, CA, USA
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Posted October 04, 2021.
Hierarchical Gaussian Processes and Mixtures of Experts to Model COVID-19 Patient Trajectories
Sunny Cui, Elizabeth C. Yoo, Didong Li, Krzysztof Laudanski, Barbara E. Engelhardt
bioRxiv 2021.10.01.462821; doi: https://doi.org/10.1101/2021.10.01.462821
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