Unsupervised machine learning identifies biomarkers of disease progression in post-kala-azar dermal leishmaniasis in Sudan
A combination of 14 biochemical variables identified by unsupervised ML was able to detect a worsening PKDL state in Sudanese patients. This approach could prove instrumental to train future supervised algorithms based on larger patient cohorts both for a more precise diagnosis and to gain insight into fundamental aspects of this complication of visceral leishmaniasis.
Authors
- Eugenia Carrillo (ORCID: https://orcid.org/0000-0002-7869-7915)
- José Carlos Solana (ORCID: https://orcid.org/0000-0002-0485-6127)
- Javier Moreno (ORCID: https://orcid.org/0000-0003-0087-4659)
- Antonio J. Martín-Galiano (ORCID: https://orcid.org/0000-0002-6662-329X)
- Fabiana Alves (ORCID: https://orcid.org/0000-0001-6213-5224)
- Ahmed Musa (ORCID: https://orcid.org/0000-0003-0590-6170)
- A. Torres (ORCID: https://orcid.org/0000-0001-5178-1917)
- Brima M. Younis
- Samuel Tesema
Institutions
- Drugs for Neglected Diseases Initiative (CH)
- University of Khartoum (SD)
- Instituto de Salud Carlos III (ES)
- Kenya AIDS Vaccine Initiative (KE)
- Universidad Autónoma de Madrid (ES)
Publication Details
- Journal
- PLoS neglected tropical diseases
- Published
- 2025-03-11
- DOI
- https://doi.org/10.1371/journal.pntd.0012924
- Primary Topic
- Research on Leishmaniasis Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Drugs for Neglected Diseases initiative
- Instituto de Salud Carlos III