<p>Optimization of genomic classifiers for clinical deployment: evaluation of Bayesian optimization to select predictive models of acute infection and in-hospital mortality</p>

Michael Mayhew; Elizabeth Tran; Kirindi Choi; Uros Midic; Roland Luethy; Nandita Damaraju; Ljubomir Buturovic

doi:10.7490/f1000research.1118405.1

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Mayhew M, Tran E, Choi K et al. Optimization of genomic classifiers for clinical deployment: evaluation of Bayesian optimization to select predictive models of acute infection and in-hospital mortality [version 1; not peer reviewed]. F1000Research 2020, 9:1406 (poster) (https://doi.org/10.7490/f1000research.1118405.1)

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Optimization of genomic classifiers for clinical deployment: evaluation of Bayesian optimization to select predictive models of acute infection and in-hospital mortality

Michael Mayhew¹, Elizabeth Tran, Kirindi Choi, Uros Midic, Roland Luethy, Nandita Damaraju, Ljubomir Buturovic

Published 06 Dec 2020 (https://doi.org/10.7490/f1000research.1118405.1)

Author Affiliations

¹ Inflammatix, Inc., USA

Copyright © 2020 Mayhew M et al. This is an open access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Published 06 Dec 2020

Optimization of genomic classifiers for clinical deployment: evaluation of Bayesian optimization to select predictive models of acute infection and in-hospital mortality
[version 1; not peer reviewed]

Michael Mayhew¹, Elizabeth Tran, Kirindi Choi, Uros Midic, Roland Luethy, Nandita Damaraju, Ljubomir Buturovic

Author Affiliations

¹ Inflammatix, Inc., USA

Presented at

Pacific Symposium on Biocomputing (PSB) 2021

Abstract

Competing Interests

All authors are employees and shareholders of Inflammatix, Inc.

Keywords

hyperparameter optimization, Bayesian optimization, acute infection, sepsis, disease severity, mortality, classiﬁcation, molecular diagnostics, genomics

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