Integrating Rule-Based eGFR Labels with Expert GP Annotations: A Multi-method Framework for CKD Classification

Conference Paper/Proceeding/Abstract 349 views 59 downloads

Ali Guran, Avishek Siris, Gary Tam

, James Chess, Xianghua Xie

Lecture Notes in Computer Science, Volume: 16039, Pages: 17 - 30

PDF | Accepted Manuscript

Author accepted manuscript document released under the terms of a Creative Commons CC-BY licence using the Swansea University Research Publications Policy (rights retention).
Download (333KB)

DOI (Published version): 10.1007/978-3-032-00656-1_2

Published in:	Lecture Notes in Computer Science
ISBN:	9783032006554 9783032006561
ISSN:	0302-9743 1611-3349
Published:	Cham Springer Nature Switzerland 2026
Online Access:	Check full text
URI:	https://cronfa.swan.ac.uk/Record/cronfa69645

Keywords:	Chronic kidney disease; Classification
College:	Faculty of Science and Engineering
Funders:	Ali Guran was funded by the Turkish Ministry of National Education through the Postgraduate Study Abroad Program. Gary Tam received support from the CHERISH-DE Centre via the International Mobil- ity Award [62] and the Collaboration and Knowledge Exchange Support [92S] (EP/M022722/1). This work was also partially supported by the EPSRC National Edge AI Hub (EP/Y007697/1). This study makes use of anonymised data held in the Secure Anonymised Information Linkage (SAIL) Databank. We would like to acknowledge all the data providers who make anonymised data available for research. The responsibility for the interpretation of the information we supplied is the authors’ alone. All research conducted has been completed under the permission and approval of the SAIL independent Information Governance Review Panel (IGRP) project number 1220. For Open Access, the author has applied a CC BY license to any Author Accepted Manuscript resulting from this submission.
Start Page:	17
End Page:	30