📣 Help Shape the Future of UKRI's Gateway to Research (GtR)

We're improving UKRI's Gateway to Research and are seeking your input! If you would be interested in being interviewed about the improvements we're making and to have your say about how we can make GtR more user-friendly, impactful, and effective for the Research and Innovation community, please email gateway@ukri.org.

Identifying subtypes of chronic kidney disease with machine learning: development, internal validation and prognostic validation using linked electronic health records in 350,067 individuals. (2023)

First Author: Dashtban A

Attributed to: Phase 1 COVID-19 Longitudinal Health and Wellbeing - National Core Study funded by COVID

Abstract

No abstract provided

Bibliographic Information

Digital Object Identifier: http://dx.doi.org/10.1016/j.ebiom.2023.104489

PubMed Identifier: 36857859

Publication URI: http://europepmc.org/abstract/MED/36857859

Type: Journal Article/Review

Volume: 89

Parent Publication: EBioMedicine

ISSN: 2352-3964