7140037
doi
10.5281/zenodo.7140037
oai:zenodo.org:7140037
user-escience-2022
Roselyne Tchoua
DePaul University
Jay Lofstead
Sandia National Laboratories
Failure Sources in Machine Learning for Medicine—A Study
Hana Ahmed
Sandia National Laboratories
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
workshop-presentation
paper-presentation
<p>Machine learning (ML) inherently suffers from at least a small amount of inaccuracy. Typically, these errors are acceptable in trade for either speed to an answer or the ability to find an answer at all. For high consequence domains, such as medicine where a wrong diagnosis can mean the difference between catching a disease early or not or prescribing debilitating treatment when it may not be needed, certain kinds and types errors are less acceptable. In a study attempting to reproduce ML for medicine research, many difficulties are encountered. These difficulties highlight both the need for higher standards to achieve reproducible ML in general and especially when it comes to high-stakes domains. This paper explores some of those difficulties with a focus on the error sources and discussions about how they may be addressed.</p>
Zenodo
2022-10-03
info:eu-repo/semantics/lecture
7140036
user-escience-2022
1664903801.849352
583240
md5:e94db5919eb1580d76d0f2e41286aa99
https://zenodo.org/records/7140037/files/ERROR_presentation_4214.pdf
711778
md5:3927d8d158bfcda6916d4403122525ca
https://zenodo.org/records/7140037/files/ERROR_presentation_4214.pptx
public
10.5281/zenodo.7140036
isVersionOf
doi