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

Machine Learning (1401) Artificial Intelligence (1216) Clinical Information and Decision Making (1340) Immunology, Allergy, Rheumatology (79) Decision Support for Health Professionals (1147) Natural Language Processing (650)

Published on in Vol 8, No 11 (2020): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/23930, first published .
Machine Learning Electronic Health Record Identification of Patients with Rheumatoid Arthritis: Algorithm Pipeline Development and Validation Study

Machine Learning Electronic Health Record Identification of Patients with Rheumatoid Arthritis: Algorithm Pipeline Development and Validation Study

Machine Learning Electronic Health Record Identification of Patients with Rheumatoid Arthritis: Algorithm Pipeline Development and Validation Study

Authors of this article:

Tjardo D Maarseveen1 Author Orcid Image ;   Timo Meinderink2, 3 Author Orcid Image ;   Marcel J T Reinders4, 5 Author Orcid Image ;   Johannes Knitza2, 3 Author Orcid Image ;   Tom W J Huizinga1 Author Orcid Image ;   Arnd Kleyer2, 3 Author Orcid Image ;   David Simon2, 3 Author Orcid Image ;   Erik B van den Akker4, 5 Author Orcid Image ;   Rachel Knevel1, 6 Author Orcid Image
Article Authors Cited by (27) Tweetations (5) Metrics

Journals

  1. Maarseveen T, Maurits M, Niemantsverdriet E, van der Helm-van Mil A, Huizinga T, Knevel R. Handwork vs machine: a comparison of rheumatoid arthritis patient populations as identified from EHR free-text by diagnosis extraction through machine-learning or traditional criteria-based chart review. Arthritis Research & Therapy 2021;23(1) View
  2. Obayya M, Alamgeer M, S. Alzahrani J, Alabdan R, N. Al-Wesabi F, Mohamed A, Alsaid Hassan M. Artificial Intelligence Driven Biomedical Image Classification for Robust Rheumatoid Arthritis Classification. Biomedicines 2022;10(11):2714 View
  3. Humbert‐Droz M, Izadi Z, Schmajuk G, Gianfrancesco M, Baker M, Yazdany J, Tamang S. Development of a Natural Language Processing System for Extracting Rheumatoid Arthritis Outcomes From Clinical Notes Using the National Rheumatology Informatics System for Effectiveness Registry. Arthritis Care & Research 2023;75(3):608 View
  4. Fajardo E, Graf C. Artificial Intelligence, the transformation of rheumatology? Part II. Global Rheumatology 2022 View
  5. Kedra J, Davergne T, Braithwaite B, Servy H, Gossec L. Machine learning approaches to improve disease management of patients with rheumatoid arthritis: review and future directions. Expert Review of Clinical Immunology 2021;17(12):1311 View
  6. Knevel R, Liao K. From real-world electronic health record data to real-world results using artificial intelligence. Annals of the Rheumatic Diseases 2023;82(3):306 View
  7. Felten R, Rosine N. Responding to and Driving Change in Rheumatology: Report from the 12th International Immunology Summit 2021. Rheumatology and Therapy 2022;9(2):705 View
  8. Fajardo E, Graf C. Inteligência artificial, transformação da reumatologia? Parte II. Global Rheumatology 2022 View
  9. Knevel R, Hügle T. E-health as a sine qua non for modern healthcare. RMD Open 2022;8(2):e002401 View
  10. Lazarova E, Mora S, Maggi N, Ruggiero C, Vitale A, Rubartelli P, Giacomini M. An Interoperable Electronic Health Record System for Clinical Cardiology. Informatics 2022;9(2):47 View
  11. Fajardo E, Graf C. Inteligencia artificial, ¿transformación de la reumatología? - II Parte. Global Rheumatology 2022 View
  12. Momtazmanesh S, Nowroozi A, Rezaei N. Artificial Intelligence in Rheumatoid Arthritis: Current Status and Future Perspectives: A State-of-the-Art Review. Rheumatology and Therapy 2022;9(5):1249 View
  13. Redd D, Shao Y, Zeng-Treitler Q, Myers L, Barker B, Nelson S, Imperiale T. Identification of colorectal cancer using structured and free text clinical data. Health Informatics Journal 2022;28(4):146045822211344 View
  14. Nurmambetova E, Pan J, Zhang Z, Wu G, Lee S, Southern D, Martin E, Ho C, Xu Y, Eastwood C. Developing an Inpatient Electronic Medical Record Phenotype for Hospital-Acquired Pressure Injuries: Case Study Using Natural Language Processing Models. JMIR AI 2023;2:e41264 View
  15. Chung C, Chou S, Hsiao T, Zhang G, Chen Y. A Novel Hybrid Machine Learning Approach for the Prediction of Lupus Nephritis Using Polygenic Risk Score and Electronic Health Record. SSRN Electronic Journal 2022 View
  16. Saviana M, Romano G, McElroy J, Nigita G, Distefano R, Toft R, Calore F, Le P, Morales D, Atmajoana S, Deppen S, Wang K, Lee L, Acunzo M, Nana-Sinkam P. A plasma miRNA-based classifier for small cell lung cancer diagnosis. Frontiers in Oncology 2023;13 View
  17. Park D, Son S, Kim M, Kim T, Choi J, Lee S, Hong D, Kim M. Machine learning predictive model for aspiration screening in hospitalized patients with acute stroke. Scientific Reports 2023;13(1) View
  18. Madrid-García A, Merino-Barbancho B, Rodríguez-González A, Fernández-Gutiérrez B, Rodríguez-Rodríguez L, Menasalvas-Ruiz E. Understanding the role and adoption of artificial intelligence techniques in rheumatology research: An in-depth review of the literature. Seminars in Arthritis and Rheumatism 2023;61:152213 View
  19. van Ouwerkerk L, Bergstra S, Maarseveen T, Huizinga T, Knevel R, Allaart C. Is glucocorticoid bridging therapy associated with later use of glucocorticoids and biological DMARDs during the disease course of patients with rheumatoid arthritis in daily practice? A real-world data analysis. Seminars in Arthritis and Rheumatism 2024;64:152305 View
  20. van Leeuwen J, Penne E, Rabelink T, Knevel R, Teng Y. Using an artificial intelligence tool incorporating natural language processing to identify patients with a diagnosis of ANCA-associated vasculitis in electronic health records. Computers in Biology and Medicine 2024;168:107757 View
  21. Danieli M, Brunetto S, Gammeri L, Palmeri D, Claudi I, Shoenfeld Y, Gangemi S. Machine learning application in autoimmune diseases: State of art and future prospectives. Autoimmunity Reviews 2024;23(2):103496 View
  22. Mickley J, Grove A, Rouzrokh P, Yang L, Larson A, Sanchez‐Sotello J, Maradit Kremers H, Wyles C. A Stepwise Approach to Analyzing Musculoskeletal Imaging Data With Artificial Intelligence. Arthritis Care & Research 2024;76(5):590 View
  23. Benavent D, Muñoz-Fernández S, De la Morena I, Fernández-Nebro A, Marín-Corral J, Castillo Rosa E, Taberna M, Sanabra C, Sastre C. Using natural language processing to explore characteristics and management of patients with axial spondyloarthritis and psoriatic arthritis treated under real-world conditions in Spain: SpAINET study. Therapeutic Advances in Musculoskeletal Disease 2023;15 View
  24. Román Ivorra J, Trallero-Araguas E, Lopez Lasanta M, Cebrián L, Lojo L, López-Muñíz B, Fernández-Melon J, Núñez B, Silva-Fernández L, Veiga Cabello R, Ahijado P, De la Morena Barrio I, Costas Torrijo N, Safont B, Ornilla E, Restrepo J, Campo A, Andreu J, Díez E, López Robles A, Bollo E, Benavent D, Vilanova D, Luján Valdés S, Castellanos-Moreira R. Prevalence and clinical characteristics of patients with rheumatoid arthritis with interstitial lung disease using unstructured healthcare data and machine learning. RMD Open 2024;10(1):e003353 View
  25. Benavent D, Plasencia-Rodríguez C. Redefining comorbidity understanding in rheumatoid arthritis through novel approaches using real-world data. Exploration of Musculoskeletal Diseases 2024;2(1):40 View
  26. Zhou X, Cai F, Li S, Li G, Zhang C, Xie J, Yang Y. Machine learning techniques for prediction in pregnancy complicated by autoimmune rheumatic diseases: Applications and challenges. International Immunopharmacology 2024;134:112238 View

Books/Policy Documents

  1. Detert J, Detert M. Innovationen in der Gesundheitsversorgung. View