Previously submitted to: Journal of Medical Internet Research (no longer under consideration since Sep 10, 2021)
Date Submitted: Feb 16, 2021
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Evaluating the state-of-the-art in missing data imputation for clinical data
ABSTRACT
The Data Analytics Challenge on Missing data Imputation (DACMI) presented a shared clinical dataset with ground truth for evaluating and advancing the state-of-the-art in imputing missing data for clinical time series. The challenge attracted 12 international teams spanning three continents across multiple industries and academia. The challenge participating systems practically advanced the state-of-the-art with considerable margins, and their designing principles will inform future efforts to better model clinical missing data.
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.