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Unsupervised learning methods for efficient geographic clustering and identification of disease disparities with applications to county-level colorectal cancer incidence in California

Crossref DOI link: https://doi.org/10.1007/s10729-022-09604-5

Published Online: 2022-06-23

Published Print: 2022-12

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

McMahon, Mallory E.

Doroshenko, Lyubov

Roostaei, Javad

Cho, Hyunsoon https://orcid.org/0000-0002-3261-3114
Haider, Mansoor A. https://orcid.org/0000-0002-3096-1203
Funding

Funding for this research was provided by:

Division of Mathematical Sciences (DMS-1638521)

Ministry of Education, Science and Technology (2020R1A2C1A01011584)
License Information

Text and Data Mining valid from 2022-06-23

Version of Record valid from 2022-06-23
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Article History

Received: 17 December 2020

Accepted: 6 June 2022

First Online: 23 June 2022

Declarations

:

: For all data used in this study ethics approval was not needed, in accordance with the policies of our institutions.

: None.

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