Prediction of 1-Year Activity in Systemic Lupus Erythematosus: Hierarchical Machine Learning Approach
Our study introduces an explainable and reliable tool for predicting 1-year SLE activity, supporting physicians with an advanced decision-support system to improve patient management. The model identifies key features that may help characterize patient phenotypes, enabling personalized treatment plans and better outcomes. In addition, the methodology can be generalized for predictive analytics in other chronic autoimmune diseases.
Authors
- Silvia Laura Bosello (ORCID: https://orcid.org/0000-0002-4837-447X)
- Carlotta Masciocchi (ORCID: https://orcid.org/0000-0001-6415-7267)
- Marco Gorini (ORCID: https://orcid.org/0009-0008-1455-3884)
- Alfredo Cesario (ORCID: https://orcid.org/0000-0003-4687-0709)
- Jacopo Lenkowicz (ORCID: https://orcid.org/0000-0002-8366-1474)
- G. Castellino
- Livia Lilli (ORCID: https://orcid.org/0009-0005-3319-7211)
- Laura Antenucci
- Maria Antonietta D’Agostino (ORCID: https://orcid.org/0000-0002-5347-0060)
- Augusta Ortolan (ORCID: https://orcid.org/0000-0002-3131-0939)
- Stefano Patarnello (ORCID: https://orcid.org/0009-0008-2765-5935)
Institutions
- Università Cattolica del Sacro Cuore (IT)
- Agostino Gemelli University Polyclinic (IT)
- AstraZeneca (Italy) (IT)
Publication Details
- Journal
- JMIR Formative Research
- Published
- 2025-08-22
- DOI
- https://doi.org/10.2196/70200
- Primary Topic
- Systemic Lupus Erythematosus Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- AstraZeneca