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Machine Learning in Rheumatic Diseases


With advances in information technology, the demand for using data science to enhance healthcare and disease management is rapidly increasing. Among these technologies, machine learning (ML) has become ubiquitous and indispensable for solving complex problems in many scientific fields, including medical science. ML allows the development of guidelines and framing of the evaluation system for complex diseases based on massive data. In the analysis of rheumatic diseases, which are chronic and remarkably heterogeneous, ML can be anticipated to be extremely helpful in deciphering and revealing the inherent interrelationships in disease development and progression, which can further enhance the overall understanding of the disease, optimize patients’ stratification, calibrate therapeutic strategies, and predict prognosis and outcomes. In this review, the basics of ML, its potential clinical applications in rheumatology, together with its strengths and limitations are summarized.

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  1. 1.

    Obermeyer Z, Emanuel EJ (2016) Predicting the future - big data, machine learning, and clinical medicine. N Engl J Med 375(13):1216–1219

    PubMed  PubMed Central  Google Scholar 

  2. 2.

    Du-Harpur X, Watt FM, Luscombe NM, Lynch MD (2020) What is AI? Applications of artificial intelligence to dermatology. Br J Dermatol

  3. 3.

    Schmidt-Erfurth U, Sadeghipour A, Gerendas BS, Waldstein SM, Bogunovic H (2018) Artificial intelligence in retina. Prog Retin Eye Res 67:1–29

    PubMed  Google Scholar 

  4. 4.

    Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts H (2018) Artificial intelligence in radiology. Nat Rev Cancer 18(8):500–510

    CAS  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Bera K, Schalper KA, Rimm DL, Velcheti V, Madabhushi A (2019) Artificial intelligence in digital pathology - new tools for diagnosis and precision oncology. Nat Rev Clin Oncol 16(11):703–715

    PubMed  PubMed Central  Google Scholar 

  6. 6.

    Fava A, Petri M (2019) Systemic lupus erythematosus: diagnosis and clinical management. J Autoimmun 96:1–13

    PubMed  Google Scholar 

  7. 7.

    Rida MA, Chandran V (2020) Challenges in the clinical diagnosis of psoriatic arthritis. Clin Immunol 214:108390

    CAS  PubMed  Google Scholar 

  8. 8.

    Rosenberg AM (2020) Do we need a new classification of juvenile idiopathic arthritis? Clin Immunol 211:108298

    CAS  PubMed  Google Scholar 

  9. 9.

    Rajkomar A, Dean J, Kohane I (2019) Machine learning in medicine. N Engl J Med 380(14):1347–1358

    PubMed  Google Scholar 

  10. 10.

    Catalina MD, Owen KA, Labonte AC, Grammer AC, Lipsky PE (2020) The pathogenesis of systemic lupus erythematosus: harnessing big data to understand the molecular basis of lupus. J Autoimmun 110:102359

    CAS  PubMed  Google Scholar 

  11. 11.

    Esteva A, Robicquet A, Ramsundar B, Kuleshov V, DePristo M, Chou K, Cui C, Corrado G, Thrun S, Dean J (2019) A guide to deep learning in healthcare. Nat Med 25(1):24–29

    CAS  Google Scholar 

  12. 12.

    Pandit A, Radstake T (2020) Machine learning in rheumatology approaches the clinic. Nat Rev Rheumatol 16(2):69–70

    PubMed  Google Scholar 

  13. 13.

    Guan Y, Zhang H, Quang D, Wang Z, Parker SCJ, Pappas DA, Kremer JM, Zhu F (2019) Machine learning to predict anti-tumor necrosis factor drug responses of rheumatoid arthritis patients by integrating clinical and genetic markers. Arthritis Rheumatol. 71(12):1987–1996

    CAS  PubMed  Google Scholar 

  14. 14.

    Van Nieuwenhove E, Lagou V, Van Eyck L, Dooley J, Bodenhofer U, Roca C et al (2019) Machine learning identifies an immunological pattern associated with multiple juvenile idiopathic arthritis subtypes. Ann Rheum Dis 78(5):617–628

    PubMed  Google Scholar 

  15. 15.

    Plant D, Maciejewski M, Smith S, Nair N, Hyrich K, Ziemek D et al (2019) Profiling of gene expression biomarkers as a classifier of methotrexate nonresponse in patients with rheumatoid arthritis. Arthritis Rheumatol. 71(5):678–684

    CAS  PubMed  Google Scholar 

  16. 16.

    Franks JM, Martyanov V, Cai G, Wang Y, Li Z, Wood TA, Whitfield ML (2019) A machine learning classifier for assigning individual patients with systemic sclerosis to intrinsic molecular subsets. Arthritis Rheumatol 71(10):1701–1710

    PubMed  PubMed Central  Google Scholar 

  17. 17.

    Kim KJ, Kim M, Adamopoulos IE, Tagkopoulos I (2019) Compendium of synovial signatures identifies pathologic characteristics for predicting treatment response in rheumatoid arthritis patients. Clin Immunol 202:1–10

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Jorge A, Castro VM, Barnado A, Gainer V, Hong C, Cai T, Cai T, Carroll R, Denny JC, Crofford L, Costenbader KH, Liao KP, Karlson EW, Feldman CH (2019) Identifying lupus patients in electronic health records: development and validation of machine learning algorithms and application of rule-based algorithms. Semin Arthritis Rheumatol 49(1):84–90

    Google Scholar 

  19. 19.

    Lezcano-Valverde JM, Salazar F, Leon L, Toledano E, Jover JA, Fernandez-Gutierrez B et al (2017) Development and validation of a multivariate predictive model for rheumatoid arthritis mortality using a machine learning approach. Sci Rep 7(1):10189

    PubMed  PubMed Central  Google Scholar 

  20. 20.

    Spielmann L, Nespola B, Severac F, Andres E, Kessler R, Guffroy A et al (2019) Anti-Ku syndrome with elevated CK and anti-Ku syndrome with anti-dsDNA are two distinct entities with different outcomes. Ann Rheum Dis 78(8):1101–1106

    CAS  PubMed  Google Scholar 

  21. 21.

    Figgett WA, Monaghan K, Ng M, Alhamdoosh M, Maraskovsky E, Wilson NJ, Hoi AY, Morand EF, Mackay F (2019) Machine learning applied to whole-blood RNA-sequencing data uncovers distinct subsets of patients with systemic lupus erythematosus. Clin Transl Immunology 8(12):e01093

    PubMed  PubMed Central  Google Scholar 

  22. 22.

    Heard BJ, Rosvold JM, Fritzler MJ, El-Gabalawy H, Wiley JP, Krawetz RJ (2014) A computational method to differentiate normal individuals, osteoarthritis and rheumatoid arthritis patients using serum biomarkers. J R Soc Interface 11(97):20140428

    PubMed  PubMed Central  Google Scholar 

  23. 23.

    Wolf BJ, Spainhour JC, Arthur JM, Janech MG, Petri M, Oates JC (2016) Development of biomarker models to predict outcomes in lupus nephritis. Arthritis Rheumatol. 68(8):1955–1963

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Mo X, Chen X, Li H, Li J, Zeng F, Chen Y, He F, Zhang S, Li H, Pan L, Zeng P, Xie Y, Li H, Huang M, He Y, Liang H, Zeng H (2019) Early and accurate prediction of clinical response to methotrexate treatment in juvenile idiopathic arthritis using machine learning. Front Pharmacol 10:1155

    CAS  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Norman B, Pedoia V, Noworolski A, Link TM, Majumdar S (2019) Applying densely connected convolutional neural networks for staging osteoarthritis severity from plain radiographs. J Digit Imaging 32(3):471–477

    PubMed  Google Scholar 

  26. 26.

    Reddy BK, Delen D (2018) Predicting hospital readmission for lupus patients: an RNN-LSTM-based deep-learning methodology. Comput Biol Med 101:199–209

    PubMed  Google Scholar 

  27. 27.

    Tolpadi AA, Lee JJ, Pedoia V, Majumdar S. Deep learning predicts total knee replacement from magnetic resonance images. arXiv e-prints [Internet]. 2020 February 01, 2020:[arXiv:2002.10591 p.]. Available from:

  28. 28.

    Géron A (2017) Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: concepts, tools, and techniques to build intelligent systems. O’Reilly Media, Inc, Sebastopol

    Google Scholar 

  29. 29.

    Madrid-Garcia A, Font-Urgelles J, Vega-Barbas M, Leon-Mateos L, Freites DD, Lajas CJ et al (2019) Outpatient readmission in rheumatology: a machine learning predictive model of patient’s return to the clinic. J Clin Med 8(8)

  30. 30.

    Joo YB, Baek IW, Park YJ, Park KS, Kim KJ (2020) Machine learning-based prediction of radiographic progression in patients with axial spondyloarthritis. Clin Rheumatol 39(4):983–991

    PubMed  Google Scholar 

  31. 31.

    McCulloch WS, Pitts W. A logical calculus of the ideas immanent in nervous activity. 1943. Bull Math Biol 1990;52(1–2):99–115; discussion 73-97

  32. 32.

    Hirano T, Nishide M, Nonaka N, Seita J, Ebina K, Sakurada K et al (2019) Development and validation of a deep-learning model for scoring of radiographic finger joint destruction in rheumatoid arthritis. Rheumatol Adv Pract 3(2):rkz047

    PubMed  PubMed Central  Google Scholar 

  33. 33.

    Ureten K, Erbay H, Maras HH (2020) Detection of rheumatoid arthritis from hand radiographs using a convolutional neural network. Clin Rheumatol 39(4):969–974

    PubMed  Google Scholar 

  34. 34.

    Stoel B (2020) Use of artificial intelligence in imaging in rheumatology - current status and future perspectives. RMD Open 6(1)

  35. 35.

    Andersen JKH, Pedersen JS, Laursen MS, Holtz K, Grauslund J, Savarimuthu TR, Just SA (2019) Neural networks for automatic scoring of arthritis disease activity on ultrasound images. RMD Open 5(1):e000891

    PubMed  PubMed Central  Google Scholar 

  36. 36.

    Ceccarelli F, Sciandrone M, Perricone C, Galvan G, Morelli F, Vicente LN, Leccese I, Massaro L, Cipriano E, Spinelli FR, Alessandri C, Valesini G, Conti F (2017) Prediction of chronic damage in systemic lupus erythematosus by using machine-learning models. PLoS One 12(3):e0174200

    PubMed  PubMed Central  Google Scholar 

  37. 37.

    Lotsch J, Alfredsson L, Lampa J (2020) Machine-learning-based knowledge discovery in rheumatoid arthritis-related registry data to identify predictors of persistent pain. Pain. 161(1):114–126

    PubMed  Google Scholar 

  38. 38.

    Zhou SM, Fernandez-Gutierrez F, Kennedy J, Cooksey R, Atkinson M, Denaxas S, Siebert S, Dixon WG, O’Neill TW, Choy E, Sudlow C, UK Biobank Follow-up and Outcomes Group, Brophy S (2016) Defining disease phenotypes in primary care electronic health records by a machine learning approach: a case study in identifying rheumatoid arthritis. PLoS One 11(5):e0154515

    PubMed  PubMed Central  Google Scholar 

  39. 39.

    Orange DE, Agius P, DiCarlo EF, Robine N, Geiger H, Szymonifka J et al (2018) Identification of three rheumatoid arthritis disease subtypes by machine learning integration of synovial histologic features and RNA sequencing data. Arthritis Rheumatol. 70(5):690–701

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Norgeot B, Glicksberg BS, Trupin L, Lituiev D, Gianfrancesco M, Oskotsky B, Schmajuk G, Yazdany J, Butte AJ (2019) Assessment of a deep learning model based on electronic health record data to forecast clinical outcomes in patients with rheumatoid arthritis. JAMA Netw Open 2(3):e190606

    PubMed  PubMed Central  Google Scholar 

  41. 41.

    Ceccarelli F, Sciandrone M, Perricone C, Galvan G, Cipriano E, Galligari A, Levato T, Colasanti T, Massaro L, Natalucci F, Spinelli FR, Alessandri C, Valesini G, Conti F (2018) Biomarkers of erosive arthritis in systemic lupus erythematosus: application of machine learning models. PLoS One 13(12):e0207926

    PubMed  PubMed Central  Google Scholar 

  42. 42.

    Murray SG, Avati A, Schmajuk G, Yazdany J (2019) Automated and flexible identification of complex disease: building a model for systemic lupus erythematosus using noisy labeling. J Am Med Inform Assoc 26(1):61–65

    PubMed  Google Scholar 

  43. 43.

    Kegerreis B, Catalina MD, Bachali P, Geraci NS, Labonte AC, Zeng C, Stearrett N, Crandall KA, Lipsky PE, Grammer AC (2019) Machine learning approaches to predict lupus disease activity from gene expression data. Sci Rep 9(1):9617

    PubMed  PubMed Central  Google Scholar 

  44. 44.

    Ward MM, Pajevic S, Dreyfuss J, Malley JD (2006) Short-term prediction of mortality in patients with systemic lupus erythematosus: classification of outcomes using random forests. Arthritis Rheum 55(1):74–80

    PubMed  Google Scholar 

  45. 45.

    Eng SWM, Aeschlimann FA, van Veenendaal M, Berard RA, Rosenberg AM, Morris Q, Yeung RSM, on behalf of the ReACCh-Out Research Consortium (2019) Patterns of joint involvement in juvenile idiopathic arthritis and prediction of disease course: a prospective study with multilayer non-negative matrix factorization. PLoS Med 16(2):e1002750

    PubMed  PubMed Central  Google Scholar 

  46. 46.

    Eng SW, Duong TT, Rosenberg AM, Morris Q, Yeung RS (2014) The biologic basis of clinical heterogeneity in juvenile idiopathic arthritis. Arthritis Rheumatol. 66(12):3463–3475

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Poppenberg KE, Jiang K, Li L, Sun Y, Meng H, Wallace CA, Hennon T, Jarvis JN (2019) The feasibility of developing biomarkers from peripheral blood mononuclear cell RNAseq data in children with juvenile idiopathic arthritis using machine learning approaches. Arthritis Res Ther 21(1):230

    PubMed  PubMed Central  Google Scholar 

  48. 48.

    Rezaei E, Hogan D, Trost B, Kusalik AJ, Boire G, Cabral DA et al (2020) Clinical and associated inflammatory biomarker features predictive of short-term outcomes in non-systemic juvenile idiopathic arthritis. Rheumatology (Oxford)

  49. 49.

    Walsh JA, Shao Y, Leng J, He T, Teng CC, Redd D, Treitler Zeng Q, Burningham Z, Clegg DO, Sauer BC (2017) Identifying axial spondyloarthritis in electronic medical records of US veterans. Arthritis Care Res (Hoboken) 69(9):1414–1420

    Google Scholar 

  50. 50.

    Liu J, Zhu Q, Han J, Zhang H, Li Y, Ma Y, He D, Gu J, Zhou X, Reveille JD, Jin L, Zou H, Ren S, Wang J (2019) IgG Galactosylation status combined with MYOM2-rs2294066 precisely predicts anti-TNF response in ankylosing spondylitis. Mol Med 25(1):25

    PubMed  PubMed Central  Google Scholar 

  51. 51.

    Patrick MT, Stuart PE, Raja K, Gudjonsson JE, Tejasvi T, Yang J, Chandran V, Das S, Callis-Duffin K, Ellinghaus E, Enerbäck C, Esko T, Franke A, Kang HM, Krueger GG, Lim HW, Rahman P, Rosen CF, Weidinger S, Weichenthal M, Wen X, Voorhees JJ, Abecasis GR, Gladman DD, Nair RP, Elder JT, Tsoi LC (2018) Genetic signature to provide robust risk assessment of psoriatic arthritis development in psoriasis patients. Nat Commun 9(1):4178

    PubMed  PubMed Central  Google Scholar 

  52. 52.

    Navarini L, Sperti M, Currado D, Costa L, Deriu MA, Margiotta DPE et al (2020) A machine-learning approach to cardiovascular risk prediction in psoriatic arthritis. Rheumatology

  53. 53.

    Hu T, Oksanen K, Zhang W, Randell E, Furey A, Sun G et al (2018) An evolutionary learning and network approach to identifying key metabolites for osteoarthritis. PLoS Comput Biol 14(3)

  54. 54.

    Lazzarini N, Runhaar J, Bay-Jensen AC, Thudium CS, Bierma-Zeinstra SMA, Henrotin Y, Bacardit J (2017) A machine learning approach for the identification of new biomarkers for knee osteoarthritis development in overweight and obese women. Osteoarthr Cartil 25(12):2014–2021

    CAS  Google Scholar 

  55. 55.

    Lim J, Kim J, Cheon S (2019) A deep neural network-based method for early detection of osteoarthritis using statistical data. Int J Environ Res Public Health 16(7)

  56. 56.

    Brahim A, Jennane R, Riad R, Janvier T, Khedher L, Toumi H, Lespessailles E (2019) A decision support tool for early detection of knee OsteoArthritis using X-ray imaging and machine learning: data from the OsteoArthritis initiative. Comput Med Imaging Graph 73:11–18

    PubMed  Google Scholar 

  57. 57.

    Tiulpin A, Klein S, Bierma-Zeinstra SMA, Thevenot J, Rahtu E, Meurs JV et al (2019) Multimodal machine learning-based knee osteoarthritis progression prediction from plain radiographs and clinical data. Sci Rep 9(1):20038

    CAS  PubMed  PubMed Central  Google Scholar 

  58. 58.

    Thomas SL, Edwards CJ, Smeeth L, Cooper C, Hall AJ (2008) How accurate are diagnoses for rheumatoid arthritis and juvenile idiopathic arthritis in the general practice research database? Arthritis Rheum 59(9):1314–1321

    CAS  PubMed  Google Scholar 

  59. 59.

    Carroll RJ, Eyler AE, Denny JC (2011) Naive electronic health record phenotype identification for rheumatoid arthritis. AMIA Annu Symp Proc. 2011:189–196

    PubMed  PubMed Central  Google Scholar 

  60. 60.

    Feldman CH, Yoshida K, Xu C, Frits ML, Shadick NA, Weinblatt ME, Connolly SE, Alemao E, Solomon DH (2019) Supplementing claims data with electronic medical records to improve estimation and classification of rheumatoid arthritis disease activity: a machine learning approach. ACR Open Rheumatol 1(9):552–559

    PubMed  PubMed Central  Google Scholar 

  61. 61.

    Pfeil A, Renz DM, Hansch A, Kainberger F, Lehmann G, Malich A, Wolf G, Böttcher J (2013) The usefulness of computer-aided joint space analysis in the assessment of rheumatoid arthritis. Joint Bone Spine 80(4):380–385

    PubMed  Google Scholar 

  62. 62.

    Hall M, Doherty S, Courtney P, Latief K, Zhang W, Doherty M (2014) Synovial pathology detected on ultrasound correlates with the severity of radiographic knee osteoarthritis more than with symptoms. Osteoarthr Cartil 22(10):1627–1633

    CAS  PubMed Central  Google Scholar 

  63. 63.

    Cupek R, Ziebinski A (2016) Automated assessment of joint synovitis activity from medical ultrasound and power doppler examinations using image processing and machine learning methods. Reumatologia. 54(5):239–242

    PubMed  PubMed Central  Google Scholar 

  64. 64.

    Salliot C, van der Heijde D (2009) Long-term safety of methotrexate monotherapy in patients with rheumatoid arthritis: a systematic literature research. Ann Rheum Dis 68(7):1100–1104

    CAS  PubMed  Google Scholar 

  65. 65.

    Smolen JS (2020) Insights into the treatment of rheumatoid arthritis: a paradigm in medicine. J Autoimmun 110:102425

    CAS  PubMed  Google Scholar 

  66. 66.

    Mankia K, Di Matteo A, Emery P (2020) Prevention and cure: the major unmet needs in the management of rheumatoid arthritis. J Autoimmun 110:102399

    CAS  PubMed  Google Scholar 

  67. 67.

    Lin C, Karlson EW, Dligach D, Ramirez MP, Miller TA, Mo H, Braggs NS, Cagan A, Gainer V, Denny JC, Savova GK (2015) Automatic identification of methotrexate-induced liver toxicity in patients with rheumatoid arthritis from the electronic medical record. J Am Med Inform Assoc 22(e1):e151–e161

    PubMed  Google Scholar 

  68. 68.

    Iaccarino L, Andreoli L, Bocci EB, Bortoluzzi A, Ceccarelli F, Conti F, de Angelis R, de Marchi G, de Vita S, di Matteo A, Emmi G, Emmi L, Gatto M, Gerli R, Gerosa M, Govoni M, Larosa M, Meroni PL, Mosca M, Pazzola G, Reggia R, Saccon F, Salvarani C, Tani C, Zen M, Frigo AC, Tincani A, Doria A (2018) Clinical predictors of response and discontinuation of belimumab in patients with systemic lupus erythematosus in real life setting. Results of a large, multicentric, nationwide study. J Autoimmun 86:1–8

    CAS  PubMed  Google Scholar 

  69. 69.

    Hoots BE, Xu L, Kariisa M (2018) 2018 Annual surveillance report of drug-related risks and outcomes–United States. CDC National Center for Injury Prevention and Control, Atlanta

    Google Scholar 

  70. 70.

    Gao XW, Hui R, Tian Z (2017) Classification of CT brain images based on deep learning networks. Comput Methods Prog Biomed 138:49–56

    Google Scholar 

  71. 71.

    Walsh SLF, Calandriello L, Silva M, Sverzellati N (2018) Deep learning for classifying fibrotic lung disease on high-resolution computed tomography: a case-cohort study. Lancet Respir Med 6(11):837–845

    PubMed  Google Scholar 

  72. 72.

    Moores KG, Sathe NA (2013) A systematic review of validated methods for identifying systemic lupus erythematosus (SLE) using administrative or claims data. Vaccine. 31(Suppl 10):K62–K73

    PubMed  Google Scholar 

  73. 73.

    Agarwal V, Podchiyska T, Banda JM, Goel V, Leung TI, Minty EP, Sweeney TE, Gyang E, Shah NH (2016) Learning statistical models of phenotypes using noisy labeled training data. J Am Med Inform Assoc 23(6):1166–1173

    PubMed  PubMed Central  Google Scholar 

  74. 74.

    Labonte AC, Kegerreis B, Geraci NS, Bachali P, Madamanchi S, Robl R, Catalina MD, Lipsky PE, Grammer AC (2018) Identification of alterations in macrophage activation associated with disease activity in systemic lupus erythematosus. PLoS One 13(12):e0208132

    PubMed  PubMed Central  Google Scholar 

  75. 75.

    Kasitanon N, Intaniwet T, Wangkaew S, Pantana S, Sukitawut W, Louthrenoo W (2015) The clinically quiescent phase in early-diagnosed SLE patients: inception cohort study. Rheumatology (Oxford) 54(5):868–875

    CAS  Google Scholar 

  76. 76.

    Churpek MM, Yuen TC, Winslow C, Meltzer DO, Kattan MW, Edelson DP (2016) Multicenter comparison of machine learning methods and conventional regression for predicting clinical deterioration on the wards. Crit Care Med 44(2):368–374

    PubMed  PubMed Central  Google Scholar 

  77. 77.

    Cristoferi L, Nardi A, Ronca V, Invernizzi P, Mells G, Carbone M (2018) Prognostic models in primary biliary cholangitis. J Autoimmun 95:171–178

    CAS  PubMed  Google Scholar 

  78. 78.

    Sun Z, Zhang Z, Fu K, Zhao Y, Liu D, Ma X (2012) Diagnostic accuracy of parotid CT for identifying Sjögren’s syndrome. Eur J Radiol 81(10):2702–2709

    PubMed  Google Scholar 

  79. 79.

    Kise Y, Ikeda H, Fujii T, Fukuda M, Ariji Y, Fujita H, Katsumata A, Ariji E (2019) Preliminary study on the application of deep learning system to diagnosis of Sjogren’s syndrome on CT images. Dentomaxillofac Radiol 48(6):20190019

    PubMed  PubMed Central  Google Scholar 

  80. 80.

    Quartuccio L, Baldini C, Bartoloni E, Priori R, Carubbi F, Corazza L, Alunno A, Colafrancesco S, Luciano N, Giacomelli R, Gerli R, Valesini G, Bombardieri S, de Vita S (2015) Anti-SSA/SSB-negative Sjogren’s syndrome shows a lower prevalence of lymphoproliferative manifestations, and a lower risk of lymphoma evolution. Autoimmun Rev 14(11):1019–1022

    CAS  PubMed  Google Scholar 

  81. 81.

    Kapsogeorgou EK, Voulgarelis M, Tzioufas AG (2019) Predictive markers of lymphomagenesis in Sjögren’s syndrome: from clinical data to molecular stratification. J Autoimmun 104:102316

    CAS  PubMed  Google Scholar 

  82. 82.

    Pezoulas VC, Exarchos TP, Tzioufas AG, De Vita S, Fotiadis DI (2019) Predicting lymphoma outcomes and risk factors in patients with primary Sjögren’s Syndrome using gradient boosting tree ensembles. Conf Proc IEEE Eng Med Biol Soc 2019:2165–2168

    Google Scholar 

  83. 83.

    Smistad E, Lovstakken L (2016) Vessel detection in ultrasound images using deep convolutional neural networks. 2nd Workshop on Deep Learning in Medical Image Analysis (DLMIA)

  84. 84.

    Burlina P, Billings S, Joshi N, Albayda J (2017) Automated diagnosis of myositis from muscle ultrasound: exploring the use of machine learning and deep learning methods. PLoS One 12(8):e0184059

    PubMed  PubMed Central  Google Scholar 

  85. 85.

    Nodera H, Sogawa K, Takamatsu N, Hashiguchi S, Saito M, Mori A et al (2019) Texture analysis of sonographic muscle images can distinguish myopathic conditions. J Med Invest 66(3.4):237–247

    PubMed  Google Scholar 

  86. 86.

    Grassi W, Salaffi F, Filippucci E (2005) Ultrasound in rheumatology. Best Pract Res Clin Rheumatol 19(3):467–485

    PubMed  Google Scholar 

  87. 87.

    Petty RE, Southwood TR, Baum J, Bhettay E, Glass DN, Manners P, Maldonado-Cocco J, Suarez-Almazor M, Orozco-Alcala J, Prieur AM (1998) Revision of the proposed classification criteria for juvenile idiopathic arthritis: Durban, 1997. J Rheumatol 25(10):1991–1994

    CAS  PubMed  Google Scholar 

  88. 88.

    Huang H, Fava A, Guhr T, Cimbro R, Rosen A, Boin F et al (2015) A methodology for exploring biomarker – phenotype associations: application to flow cytometry data and systemic sclerosis clinical manifestations. BMC Bioinforma 16(1)

  89. 89.

    Taroni JN, Martyanov V, Mahoney JM, Whitfield ML (2017) A functional genomic meta-analysis of clinical trials in systemic sclerosis: toward precision medicine and combination therapy. J Invest Dermatol 137(5):1033–1041

    CAS  PubMed  Google Scholar 

  90. 90.

    Jamian L, Wheless L, Crofford LJ, Barnado A (2019) Rule-based and machine learning algorithms identify patients with systemic sclerosis accurately in the electronic health record. Arthritis Res Ther. 21(1):305

    PubMed  PubMed Central  Google Scholar 

  91. 91.

    Ing E, Su W, Schonlau M, Torun N (2019) Support Vector Machines and logistic regression to predict temporal artery biopsy outcomes. Can J Ophthalmol 54(1):116–118

    PubMed  Google Scholar 

  92. 92.

    Lee M, Smit ED, Yuen AWT, Sarossy M (2014) The use of statistical modeling to predict temporal artery biopsy outcome from presenting symptoms and laboratory results. Acta Ophthalmol (Copenh) 92(s253)

  93. 93.

    Walsh JA, Rozycki M, Yi E, Park Y (2019) Application of machine learning in the diagnosis of axial spondyloarthritis. Curr Opin Rheumatol 31(4):362–367

    PubMed  PubMed Central  Google Scholar 

  94. 94.

    Sieper J, Poddubnyy D (2016) New evidence on the management of spondyloarthritis. Nat Rev Rheumatol 12(5):282–295

    CAS  PubMed  Google Scholar 

  95. 95.

    Hunter DJ, Bierma-Zeinstra S (Lancet, 2019) Osteoarthritis. 393(10182):1745–1759

  96. 96.

    Swan AL, Hillier KL, Smith JR, Allaway D, Liddell S, Bacardit J, Mobasheri A (2013) Analysis of mass spectrometry data from the secretome of an explant model of articular cartilage exposed to pro-inflammatory and anti-inflammatory stimuli using machine learning. BMC Musculoskelet Disord 14:349

    PubMed  PubMed Central  Google Scholar 

  97. 97.

    Abidin AZ, Deng B, Dsouza AM, Nagarajan MB, Coan P, Wismüller A (2018) Deep transfer learning for characterizing chondrocyte patterns in phase contrast X-Ray computed tomography images of the human patellar cartilage. Comput Biol Med 95:24–33

    PubMed  PubMed Central  Google Scholar 

  98. 98.

    Tiulpin A, Thevenot J, Rahtu E, Lehenkari P, Saarakkala S (2018) Automatic knee osteoarthritis diagnosis from plain radiographs: a deep learning-based approach. Sci Rep 8(1):1727

    PubMed  PubMed Central  Google Scholar 

  99. 99.

    Zheng C, Rashid N, Wu YL, Koblick R, Lin AT, Levy GD, Cheetham TC (2014) Using natural language processing and machine learning to identify gout flares from electronic clinical notes. Arthritis Care Res (Hoboken) 66(11):1740–1748

    Google Scholar 

  100. 100.

    Abhishek A, Neogi T, Choi H, Doherty M, Rosenthal AK, Terkeltaub R (2018) Review: unmet needs and the path forward in joint disease associated with calcium pyrophosphate crystal deposition. Arthritis Rheumatol. 70(8):1182–1191

    PubMed  PubMed Central  Google Scholar 

  101. 101.

    Tedeschi SK, Cai T, He Z, Ahuja Y, Hong C, Yates KA et al (2020) Classifying pseudogout using machine learning approaches with electronic health record data. Arthritis Care Res (Hoboken)

  102. 102.

    Mooney SJ, Pejaver V (2018) Big data in public health: terminology, machine learning, and privacy. Annu Rev Public Health 39:95–112

    PubMed  Google Scholar 

  103. 103.

    Aletaha D (2020) Precision medicine and management of rheumatoid arthritis. J Autoimmun 110:102405

    CAS  PubMed  Google Scholar 

  104. 104.

    Cruz JA, Wishart DS (2007) Applications of machine learning in cancer prediction and prognosis. Cancer Inform 2:59–77

    PubMed  PubMed Central  Google Scholar 

  105. 105.

    Che Z, Purushotham S, Khemani R, Liu Y (2016) Interpretable deep models for ICU outcome prediction. AMIA Annu Symp Proc 2016:371–380

    PubMed  Google Scholar 

  106. 106.

    Yang YJ, Bang CS (2019) Application of artificial intelligence in gastroenterology. World J Gastroenterol 25(14):1666–1683

    PubMed  PubMed Central  Google Scholar 

  107. 107.

    Halevy A, Norvig P, Pereira F (2009) The unreasonable effectiveness of data. IEEE Intell Syst 24(2):8–12

    Google Scholar 

  108. 108.

    Huber AM, Mamyrova G, Lachenbruch PA, Lee JA, Katz JD, Targoff IN, Miller FW, Rider LG, for the Childhood Myositis Heterogeneity Collaborative Study Group (2014) Early illness features associated with mortality in the juvenile idiopathic inflammatory myopathies. Arthritis Care Res (Hoboken) 66(5):732–740

    Google Scholar 

  109. 109.

    Menachemi N, Collum TH (2011) Benefits and drawbacks of electronic health record systems. Risk Manag Healthc Policy 4:47–55

    PubMed  PubMed Central  Google Scholar 

  110. 110.

    Kan H, Nagar S, Patel J, Wallace DJ, Molta C, Chang DJ (2016) Longitudinal treatment patterns and associated outcomes in patients with newly diagnosed systemic lupus erythematosus. Clin Ther 38(3):610–624

    PubMed  Google Scholar 

  111. 111.

    England JR, Cheng PM (2019) Artificial intelligence for medical image analysis: a guide for authors and reviewers. AJR Am J Roentgenol 212(3):513–519

    PubMed  Google Scholar 

  112. 112.

    Saba L, Biswas M, Kuppili V, Cuadrado Godia E, Suri HS, Edla DR, Omerzu T, Laird JR, Khanna NN, Mavrogeni S, Protogerou A, Sfikakis PP, Viswanathan V, Kitas GD, Nicolaides A, Gupta A, Suri JS (2019) The present and future of deep learning in radiology. Eur J Radiol 114:14–24

    PubMed  Google Scholar 

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This study was supported by grants from the National Natural Science Foundation of China (81788101, 81630044), Chinese Academy of Medical Science Innovation Fund for Medical Sciences (CIFMS2016-12M-1-003, 2017-12M-1-008, 2017-I2M-3-011, 2016-12M-1-008), Beijing Capital Health Development Fund (2020-2-4019), and Grant from Medical Epigenetics Research Center, Chinese Academy of Medical Sciences (2017PT31035).

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JMD and LYT wrote the article. JCD revised the figures. ZLD, ZX, and Lipsky P.E. revised the manuscript. All authors researched the data for the article, made substantial contributions to the content, and edited the manuscript.

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Correspondence to Lidan Zhao or Xuan Zhang.

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Jiang, M., Li, Y., Jiang, C. et al. Machine Learning in Rheumatic Diseases. Clinic Rev Allerg Immunol 60, 96–110 (2021).

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  • Machine learning
  • Rheumatic diseases
  • Medicine
  • Clinical application