Artificial Intelligence in Healthcare

AI, Machine Learning, and Deep and Intelligent Medicine Simplified for Everyone

Academic Edition, Chapter 53 - References/Further Reading


  1. Hügle M, Omoumi P, van Laar JM, Boedecker J, Hügle T. Applied machine learning and artificial intelligence in rheumatology. Rheumatology Advances in Practice. 2020;4(1):rkaa005.
  2. Symptom checker chatbot [Internet]. [cited 2021 Jan 22]. Available from:
  3. Lin C, Karlson EW, Canhao H, Miller TA, Dligach D, Chen PJ, Perez RN, Shen Y, Weinblatt ME, Shadick NA, Plenge RM. Automatic prediction of rheumatoid arthritis disease activity from the electronic medical records. PloS one. 2013 Aug 16;8(8):e69932.
  4. Zhou SM, Fernandez-Gutierrez F, Kennedy J, Cooksey R, Atkinson M, Denaxas S, Siebert S, Dixon WG, O’Neill TW, Choy E, Sudlow C. Defining disease phenotypes in primary care electronic health records by a machine learning approach: a case study in identifying rheumatoid arthritis. PloS one. 2016 May 2;11(5):e0154515.
  5. Andersen JK, Pedersen JS, Laursen MS, Holtz K, Grauslund J, Savarimuthu TR, Just SA. Neural networks for automatic scoring of arthritis disease activity on ultrasound images. RMD open. 2019 Mar 1;5(1):e000891.
  6. Rohrbach J, Reinhard T, Sick B, Dürr O. Bone erosion scoring for rheumatoid arthritis with deep convolutional neural networks. Computers & Electrical Engineering. 2019 Sep 1;78:472-81.
  7. Foulquier N, Redou P, Le Gal C, Rouvière B, Pers JO, Saraux A. Pathogenesis-based treatments in primary Sjogren's syndrome using artificial intelligence and advanced machine learning techniques: a systematic literature review. Human vaccines & immunotherapeutics. 2018 Nov 2;14(11):2553-8.
  8. Vodenčarević A, van der Goes MC, Medina OA. Predicting flare probability in rheumatoid arthritis using machine learning methods. InProceedings of the 7th International Conference on Data Science, Technology and Applications. Hampshire, UK: SCITEPRESS–Science and Technology Publications 2018 (pp. 187-192).
  9. Plant D, Maciejewski M, Smith S, Nair N, Maximising Therapeutic Utility in Rheumatoid Arthritis Consortium, the RAMS Study Group, Hyrich K, Ziemek D, Barton A, Verstappen S. Profiling of gene expression biomarkers as a classifier of methotrexate nonresponse in patients with rheumatoid arthritis. Arthritis & Rheumatology. 2019 May;71(5):678-84.