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

Artificial Intelligence (2129) Machine Learning (2076) Clinical Information and Decision Making (1915) Natural Language Processing (917) Electronic Health Records (1319) Decision Support for Health Professionals (1539) Clinical Informatics (1325)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/39972, first published .
Machine Learning Approaches for Predicting Psoriatic Arthritis Risk Using Electronic Medical Records: Population-Based Study

Machine Learning Approaches for Predicting Psoriatic Arthritis Risk Using Electronic Medical Records: Population-Based Study

Machine Learning Approaches for Predicting Psoriatic Arthritis Risk Using Electronic Medical Records: Population-Based Study

Authors of this article:

Leon Tsung-Ju Lee1, 2, 3, 4 Author Orcid Image ;   Hsuan-Chia Yang1 Author Orcid Image ;   Phung Anh Nguyen5, 6 Author Orcid Image ;   Muhammad Solihuddin Muhtar5 Author Orcid Image ;   Yu-Chuan Jack Li1, 7 Author Orcid Image
Article Authors Cited by (20) Tweetations (3) Metrics

Journals

  1. Bogdanova E. Predictive model and classification for psoriatic arthritis risk assessment for Russian patients with psoriasis (on registry data). Vestnik dermatologii i venerologii 2023;99(4):96 View
  2. Omiye J, Gui H, Daneshjou R, Cai Z, Muralidharan V. Principles, applications, and future of artificial intelligence in dermatology. Frontiers in Medicine 2023;10 View
  3. Cazzato G, Rongioletti F. Artificial intelligence in dermatopathology: Updates, strengths, and challenges. Clinics in Dermatology 2024;42(5):437 View
  4. Schaffert D, Bibi I, Blauth M, Lull C, von Ahnen J, Gross G, Schulze-Hagen T, Knitza J, Kuhn S, Benecke J, Schmieder A, Leipe J, Olsavszky V. 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. JMIR Formative Research 2024;8:e55855 View
  5. Tan M, Chen J, Cheng J, Hu J, Hu K, Yang J, Li X, Zhang M, Zhu W, Liao L, Kuang Y. A novel nomogram to predict psoriatic arthritis in patients with plaque psoriasis. JDDG: Journal der Deutschen Dermatologischen Gesellschaft 2024;22(10):1350 View
  6. Tan M, Chen J, Cheng J, Hu J, Hu K, Yang J, Li X, Zhang M, Zhu W, Liao L, Kuang Y. Vorhersage von Psoriasis‐Arthritis bei Patienten mit Plaque‐Psoriasis durch neuartiges Nomogramm. JDDG: Journal der Deutschen Dermatologischen Gesellschaft 2024;22(10):1350 View
  7. Grohmann T, Vivekanantham A, Coates L, Pennington S, FitzGerald O. Clinical, genetic and omics-based biomarkers that might support the identification of the development of psoriatic arthritis in individuals with psoriasis: a narrative review of the literature. RMD Open 2024;10(4):e004176 View
  8. Shastri M, Wahengbam G, Arora M, Pandey P, Sharma A, Sharma A, Dogra S, Chhabra S. In Search of Predictive Indicators for the Development of Psoriatic Arthritis in Patients with Cutaneous Psoriasis: The Path Traversed and the Road Ahead. Journal of Skin and Stem Cell 2024;11(4) View
  9. Obeidat R, Alsmadi I, Baker Q, Al-Njadat A, Srinivasan S. Researching public health datasets in the era of deep learning: a systematic literature review. Health Informatics Journal 2025;31(1) View
  10. Hama T, Alsaleh M, Allery F, Choi J, Tomlinson C, Wu H, Lai A, Pontikos N, Thygesen J. Enhancing Patient Outcome Prediction Through Deep Learning With Sequential Diagnosis Codes From Structured Electronic Health Record Data: Systematic Review. Journal of Medical Internet Research 2025;27:e57358 View
  11. Vlad A, Popazu C, Lescai A, Voinescu D, Baltă A. The Role of Artificial Intelligence in the Diagnosis and Management of Rheumatoid Arthritis. Medicina 2025;61(4):689 View
  12. Xu L, Bressem K, Adams L, Poddubnyy D, Proft F. AI for imaging evaluation in rheumatology: applications of radiomics and computer vision—current status, future prospects and potential challenges. Rheumatology Advances in Practice 2025;9(2) View
  13. Yang Y, Li X, Wang H, Li X, Guan Y, Jiang J. Modeling clinical thinking based on knowledge hypergraph attention network and prompt learning for disease prediction. Expert Systems with Applications 2025;285:128033 View
  14. Ganatra D, Kotlyar M, Dohey A, Codner D, Li Q, Abji F, Rasti M, Eder L, Gladman D, Rahman P, Jurisica I, Chandran V. Combining Clinical, Genetic and Protein Markers Using Machine Learning Models Discriminates Psoriatic Arthritis Patients From Those With Psoriasis. Journal of Psoriasis and Psoriatic Arthritis® 2025;10(4):105 View
  15. Shehzadi N, Rehman A, Naz S, Rehman S, Khalifa F. Machine learning and deep learning based psoriasis recognition system: evaluation, management, prognosis—where we are and the way to the future. Artificial Intelligence Review 2025;58(9) View
  16. 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
  17. Chen Z, Wu Z, Tan H, Yu F, Wang D, Lin P. Diagnostic accuracy of machine learning approaches to identify psoriatic arthritis: a meta-analysis. Clinical and Experimental Medicine 2025;25(1) View
  18. Feldkamp A, Raychaudhuri S. Advances in treatment of psoriatic arthritis: current guidelines and emerging therapies. Current Opinion in Immunology 2025;97:102658 View
  19. Shucheng H, Armstrong A, Eidsmo L, Poddubnyy D. Hot Topics: Exploring Artificial Intelligence and Inflammatory Memory in the Management of Psoriatic Diseases. The Journal of Rheumatology 2025;52(Suppl 3):80 View

Conference Proceedings

  1. S C, V B, S H, B S, G G, Unni C. 2025 Third International Conference on Networks, Multimedia and Information Technology (NMITCON). Enhanced Psoriasis Classification Using a Hybrid Approach: a Case Study in Autoimmune Disease Diagnosis View