Explainable artificial intelligence model predicting the risk of all-cause mortality in patients with type 2 diabetes mellitus
The developed model exhibited strong predictive performance for mortality risk assessment. Its clinically interpretable outputs enable potential bedside application, improving the identification of high-risk patients and supporting timely treatment optimization.
Authors
- Anna Rita Bonfigli (ORCID: https://orcid.org/0000-0002-9619-0181)
- Arseniy Trukhanov
- Olga Vershinina (ORCID: https://orcid.org/0000-0002-3917-9592)
- Jacopo Sabbatinelli (ORCID: https://orcid.org/0000-0001-9947-6778)
- Mikhail Krivonosov (ORCID: https://orcid.org/0000-0002-1169-5149)
- Olivieri Fabiola (ORCID: https://orcid.org/0000-0002-9606-1144)
- Mikhail Ivanchenko (ORCID: https://orcid.org/0000-0002-1903-7423)
- Angelica Giuliani (ORCID: https://orcid.org/0000-0002-8477-8519)
- Claudio Franceschi (ORCID: https://orcid.org/0000-0001-9841-6386)
- Dalila Colombaretti
Institutions
- Marche Polytechnic University (IT)
- International Longevity Centre (GB)
- Istituto Nazionale di Riposo e Cura per Anziani (IT)
- N. I. Lobachevsky State University of Nizhny Novgorod (RU)
Publication Details
- Journal
- Frontiers in Endocrinology
- Published
- 2025-10-17
- DOI
- https://doi.org/10.3389/fendo.2025.1689312
- Citations
- 1
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 2.48
Funders
- Ministero dello Sviluppo Economico