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

Formative Evaluation of Digital Health Interventions (3229) Decision Support for Health Professionals (1539) Artificial Intelligence (2129) Chronic Conditions (434) Machine Learning (2076) Immunology, Allergy, Rheumatology (140) Clinical Information and Decision Making (1915)

Published on in Vol 8 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/55855, first published .
Using Automated Machine Learning to Predict Necessary Upcoming Therapy Changes in Patients With Psoriasis Vulgaris and Psoriatic Arthritis and Uncover New Influences on Disease Progression: Retrospective Study

Using Automated Machine Learning to Predict Necessary Upcoming Therapy Changes in Patients With Psoriasis Vulgaris and Psoriatic Arthritis and Uncover New Influences on Disease Progression: Retrospective Study

Using Automated Machine Learning to Predict Necessary Upcoming Therapy Changes in Patients With Psoriasis Vulgaris and Psoriatic Arthritis and Uncover New Influences on Disease Progression: Retrospective Study

Authors of this article:

Daniel Schaffert1 Author Orcid Image ;   Igor Bibi1 Author Orcid Image ;   Mara Blauth1 Author Orcid Image ;   Christian Lull1 Author Orcid Image ;   Jan Alwin von Ahnen1 Author Orcid Image ;   Georg Gross2 Author Orcid Image ;   Theresa Schulze-Hagen1 Author Orcid Image ;   Johannes Knitza3 Author Orcid Image ;   Sebastian Kuhn3 Author Orcid Image ;   Johannes Benecke1 Author Orcid Image ;   Astrid Schmieder4 Author Orcid Image ;   Jan Leipe2 Author Orcid Image ;   Victor Olsavszky1 Author Orcid Image
Article Authors Cited by (8) Tweetations Metrics

Journals

  1. Simon S, Bibi I, Schaffert D, Benecke J, Martin N, Leipe J, Vladescu C, Olsavszky V. AutoML-Driven Insights into Patient Outcomes and Emergency Care During Romania’s First Wave of COVID-19. Bioengineering 2024;11(12):1272 View
  2. McMullen E, Al Naser Y, Maazi M, Grewal R, Abdel Hafeez D, Folino T, Vender R. Predicting psoriasis severity using machine learning: a systematic review. Clinical and Experimental Dermatology 2025;50(3):520 View
  3. Ghorbian M, Ghobaei-Arani M, Ghorbian S. A comprehensive study on the application of machine learning in psoriasis diagnosis and treatment: taxonomy, challenges and recommendations. Artificial Intelligence Review 2024;58(2) View
  4. Oftring Z, Deutsch K, Tolks D, Jungmann F, Kuhn S. Novel Blended Learning on Artificial Intelligence for Medical Students: Qualitative Interview Study. JMIR Medical Education 2025;11:e65220 View
  5. Bilgin E. Current application, possibilities, and challenges of artificial intelligence in the management of rheumatoid arthritis, axial spondyloarthritis, and psoriatic arthritis. Therapeutic Advances in Musculoskeletal Disease 2025;17 View
  6. Cao C, Zhao W, Guo J, Wang Z, Lu A, Liang C. Leveraging artificial intelligence and machine learning for unraveling pathogenesis and advancing precision medicine in autoimmune diseases. The Innovation Medicine 2025;3(3):100154 View
  7. Simmachan T, Lerdpraserdpakorn N, Deesrisuk J, Sriwipat C, Shakya S, Boonkrong P. A penalized regression and machine learning approach for quality-of-life prediction in psoriasis patients. Healthcare Analytics 2025;8:100417 View
  8. Bibi I, Schaffert D, Blanke P, Illian L, Lenzing F, Martin N, Leipe J, März W, Stach K, Olsavszky V. Cardiovascular risk assessment enhanced by automated machine learning in a multi-phase study. Scientific Reports 2025;15(1) View