A novel machine learning methodology for the systematic extraction of chronic kidney disease comorbidities from abstracts
These findings have the potential to guide future CKD investigations, by facilitating the inclusion of a broader array of comorbidities in CKD prognostic models. Ultimately, our study enhances understanding of prognostic comorbidities and supports clinical practice by enabling improved patient monitoring, preventive strategies, and early detection for individuals at higher CKD development or progression risk.
Authors
- Eszter Sághy
- Antal Tamás Zemplenyi (ORCID: https://orcid.org/0000-0002-0177-0264)
- István Wittmann (ORCID: https://orcid.org/0000-0001-5163-5733)
- Frank Moriarty (ORCID: https://orcid.org/0000-0001-9838-3625)
- Sándor J. Kovács (ORCID: https://orcid.org/0000-0003-0927-6014)
- Mostafa Elsharkawy
Institutions
- Royal College of Surgeons in Ireland (IE)
- University of Colorado Anschutz Medical Campus (US)
- University of Montana (US)
- University of Pecs (HU)
Publication Details
- Journal
- Frontiers in Digital Health
- Published
- 2025-02-04
- DOI
- https://doi.org/10.3389/fdgth.2025.1495879
- Citations
- 1
- Primary Topic
- Artificial Intelligence in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 2.94