Accessibility settings

Published on in Vol 9 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/70200, first published .
Two people analyzing an AI brain graphic on a futuristic digital display.

Prediction of 1-Year Activity in Systemic Lupus Erythematosus: Hierarchical Machine Learning Approach

Prediction of 1-Year Activity in Systemic Lupus Erythematosus: Hierarchical Machine Learning Approach

Journals

  1. Liu T, Qi X, Guo M, Ye X, Fan L, Yang D. Performance evaluation of five large language models for assisting in the interpretation of urinalysis reports for kidney diseases: a real-world study. Clinical Chemistry and Laboratory Medicine (CCLM) 2026 View
  2. Zi X, Xue H, Wang C. Comment on: Identification of clinical phenotypes and disease trajectories in SLE using AI through a natural language processing framework. Rheumatology 2026;65(5) View
  3. Bosello S, Ortolan A, Lanzo L, Lilli L, Antenucci L, Cerasuolo P, Piunno S, Petricca L, Gigante M, Pacucci V, Gorini M, Castellino G, Masciocchi C, Lenkowicz J, Patarnello S, D’Agostino M. Comment on: Identification of clinical phenotypes and disease trajectories in SLE using AI through a natural language processing framework: reply. Rheumatology 2026;65(5) View
  4. Vergani E, Iacomini C, Giampietro A, Milardi D, Chiloiro S, Mancini A, De Marinis L, Bianchi A, Patarnello S, Pontecorvi A. Building the adult growth hormone deficiency data mart: a Real-World model of AI-driven clinical data extraction in a single Italian center. Journal of Endocrinological Investigation 2026 View