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This paper is in the following e-collection/theme issue:

Natural Language Processing (997) Tools, Programs and Algorithms (427) Electronic Health Records (1425) Ontologies, Classifications, and Coding (358) Secondary Use of Clinical Data for Research and Surveillance (504) Machine Learning (2316) Immunology, Allergy, Rheumatology (163) Digital Biomarkers and Digital Phenotyping (341)

Published on in Vol 13 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/68704, first published .
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Improving Phenotyping of Patients With Immune-Mediated Inflammatory Diseases Through Automated Processing of Discharge Summaries: Multicenter Cohort Study

Improving Phenotyping of Patients With Immune-Mediated Inflammatory Diseases Through Automated Processing of Discharge Summaries: Multicenter Cohort Study

Authors of this article:

Adam Remaki1 Author Orcid Image ;   Jacques Ung2 Author Orcid Image ;   Pierre Pages2 Author Orcid Image ;   Perceval Wajsburt2 Author Orcid Image ;   Elise Liu3 Author Orcid Image ;   Guillaume Faure1 Author Orcid Image ;   Thomas Petit-Jean2 Author Orcid Image ;   Xavier Tannier1 Author Orcid Image ;   Christel Gérardin1, 4 Author Orcid Image
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Journals

  1. Julla J, Jolivet T, Estellat C, Varoquaux G, Carlier A, Gautier J, Alberge J, Abouleka Y, Bergès A, Liu E, Abecassis J, Tubach F, Potier L. Incidence of death and amputation in patients with a first diabetic foot ulcer: results from the CODIA cohort. Diabetes & Metabolism 2025;51(6):101700 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