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Identification of clinical phenotypes and disease trajectories in SLE using AI through a natural language processing framework.

Rheumatology (Oxford, England) - 4 Feb 2026

Bosello Silvia Laura, Ortolan Augusta, Lanzo Lucia, Lilli Livia, Antenucci Laura, Cerasuolo Pier Giacomo, Piunno Silvia, Petricca Luca, Gigante Maria Rita, Pacucci Viviana Antonella, Gorini Marco, Castellino Gabriella, Masciocchi Carlotta, Lenkowicz Jacopo, Patarnello Stefano, D'Agostino Maria Antonietta

Abstract excerpt

OBJECTIVES: Electronic health records (EHRs) contain a wealth of unstructured patient data that can be leveraged using artificial intelligence (AI). This study aimed to develop a natural language processing (NLP) pipeline to identify clinical phenotypes and disease trajectories in patients with systemic lupus erythematosus (SLE) from EHRs. METHODS: EHR data from SLE patients were included. A standardized stepwise...

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PMID: 41701167
DOI: 10.1093/rheumatology/keag035

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PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 5 Sept 2026.

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