Identification of clinical phenotypes and disease trajectories in SLE using AI through a natural language processing framework
This novel framework, based on real-world data, enables longitudinal phenotype characterization of SLE patients. It demonstrates promise as a feasible tool to study the heterogeneity of SLE and its progression over time, offering insights into potential applications in clinical research and patient management.
Authors
- Viviana Antonella Pacucci
- Silvia Laura Bosello (ORCID: https://orcid.org/0000-0002-4837-447X)
- P. G. Cerasuolo
- Jacopo Lenkowicz (ORCID: https://orcid.org/0000-0002-8366-1474)
- L. Lanzo
- M Gigante
- G. Castellino
- Livia Lilli (ORCID: https://orcid.org/0009-0005-3319-7211)
- Laura Antenucci
- Augusta Ortolan (ORCID: https://orcid.org/0000-0002-3131-0939)
- Stefano Patarnello (ORCID: https://orcid.org/0009-0008-2765-5935)
- Marco Gorini
- C Masciocchi
- Silvia Piunno
- Maria Antonietta D’Agostino
- Luca Petricca
Institutions
- Agostino Gemelli University Polyclinic (IT)
- AstraZeneca (Italy) (IT)
- University of the Sacred Heart (JP)
Publication Details
- Journal
- Lara D. Veeken
- Published
- 2026-02-01
- DOI
- https://doi.org/10.1093/rheumatology/keag035
- Primary Topic
- Systemic Lupus Erythematosus Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00