<p>A framework for studying machine learning methods in healthcare: The first EHR DREAM Challenge</p>

Timothy Bergquist; Thomas Schaffter; Yao Yan; Thomas Yu; Vikas Pejaver; Noah Hammarlund; Justin Prosser; Sean Mooney; Justin Guinney

doi:10.7490/f1000research.1118425.1

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Bergquist T, Schaffter T, Yan Y et al. A framework for studying machine learning methods in healthcare: The first EHR DREAM Challenge [version 1; not peer reviewed]. F1000Research 2020, 9:1426 (poster) (https://doi.org/10.7490/f1000research.1118425.1)

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A framework for studying machine learning methods in healthcare: The first EHR DREAM Challenge

Timothy Bergquist, Thomas Schaffter, Yao Yan, Thomas Yu, Vikas Pejaver, Noah Hammarlund, Justin Prosser, Sean Mooney¹, Justin Guinney²

Published 08 Dec 2020 (https://doi.org/10.7490/f1000research.1118425.1)

Author Affiliations

¹ University of Washington, USA
² Sage Bionetworks, USA

Copyright © 2020 Bergquist T 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 08 Dec 2020

A framework for studying machine learning methods in healthcare: The first EHR DREAM Challenge
[version 1; not peer reviewed]

Timothy Bergquist, Thomas Schaffter, Yao Yan, Thomas Yu, Vikas Pejaver, Noah Hammarlund, Justin Prosser, Sean Mooney¹, Justin Guinney²

Author Affiliations

¹ University of Washington, USA
² Sage Bionetworks, USA

Abstract

Competing Interests

No competing interests were disclosed

Keywords

electronic health records, privacy, machine learning, artificial intelligence, learning health system

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