Ranganathan V, Gracey E, Brown MA, Inman RD, Haroon N. Pathogenesis of ankylosing spondylitis—recent advances and future directions. Nat Rev Rheumatol. 2017;13(6):359–67.

CAS  PubMed  Article  Google Scholar 

Fiorillo MT, Haroon N, Ciccia F, Breban M. Editorial: ankylosing spondylitis and related immune-mediated disorders. Front Immunol. 2019;10:1232.

CAS  PubMed  PubMed Central  Article  Google Scholar 

Mauro D, Thomas R, Guggino G, Lories R, Brown MA, Ciccia F. Ankylosing spondylitis: an autoimmune or autoinflammatory disease? Nat Rev Rheumatol. 2021;17(7):387–404.

CAS  PubMed  Article  Google Scholar 

Wright GC, Kaine J, Deodhar A. Understanding differences between men and women with axial spondyloarthritis. Semin Arthritis Rheum. 2020;50(4):687–94.

PubMed  Article  Google Scholar 

Morin M, Hellgren K, Frisell T. Familial aggregation and heritability of ankylosing spondylitis—a Swedish nested case–control study. Rheumatology (Oxford). 2020;59(7):1695–702.

Article  Google Scholar 

Lee S, Kang S, Eun Y, Won HH, Kim H, Lee J, et al. Machine learning-based prediction model for responses of bDMARDs in patients with rheumatoid arthritis and ankylosing spondylitis. Arthritis Res Ther. 2021;23(1):254.

CAS  PubMed  PubMed Central  Article  Google Scholar 

Van Calster B, Wynants L. Machine learning in medicine. N Engl J Med. 2019;380(26):2588.

PubMed  Article  Google Scholar 

Liang T, Chen J, Xu G, Zhang Z, Xue J, Zeng H, et al. Platelet-to-lymphocyte ratio as an independent factor was associated with the severity of ankylosing spondylitis. Front Immunol. 2021;12: 760214.

CAS  PubMed  PubMed Central  Article  Google Scholar 

Zhang Z, Ho KM, Hong Y. Machine learning for the prediction of volume responsiveness in patients with oliguric acute kidney injury in critical care. Crit Care (London, England). 2019;23(1):112.

Article  Google Scholar 

He D, Wang R, Liang S, Liang D, Xu F, Zeng C, et al. Comparison of secondary IgA nephropathy in patients with ankylosing spondylitis and rheumatoid arthritis. Mod Rheumatol. 2020;30(4):648–56.

CAS  PubMed  Article  Google Scholar 

Ding T, Li B, Su R, Su R, Wang Y, Gao C, et al. Elevated Th17 cells are associated with cardiovascular complications in ankylosing spondylitis. Rheumatology (Oxford). 2021 Keab888.

van der Linden S, Valkenburg HA, Cats A. Evaluation of diagnostic criteria for ankylosing spondylitis. A proposal for modification of the New York criteria. Arthritis Rheum. 1984;27(4):361–8.

PubMed  Article  Google Scholar 

Pan X, Jin X, Wang J, Hu Q, Dai B. Placenta inflammation is closely associated with gestational diabetes mellitus. Am J Transl Res. 2021;13(5):4068–79.

CAS  PubMed  PubMed Central  Google Scholar 

Zhang S, Tong YX, Zhang XH, Zhang YJ, Xu XS, Xiao AT, et al. A novel and validated nomogram to predict overall survival for gastric neuroendocrine neoplasms. J Cancer. 2019;10(24):5944–54.

PubMed  PubMed Central  Article  Google Scholar 

Jiang R, He S, Sun H, Gong H, Yang X, Cai X, et al. Identifying the risk factors and estimating the prognosis in patients with pelvis and spine ewing sarcoma: a population-based study. Spine. 2021;46(19):1315–25.

PubMed  Article  Google Scholar 

Wu M, Li X, Zhang T, Liu Z, Zhao Y. Identification of a nine-gene signature and establishment of a prognostic nomogram predicting overall survival of pancreatic cancer. Front Oncol. 2019;9:996.

PubMed  PubMed Central  Article  Google Scholar 

Zhang H, Liu R, Sun L, Guo W, Ji X, Hu X. Comprehensive analysis of gene expression changes and validation in hepatocellular carcinoma. Onco Targets Ther. 2021;14:1021–31.

PubMed  PubMed Central  Article  Google Scholar 

Vickers AJ, Holland F. Decision curve analysis to evaluate the clinical benefit of prediction models. Spine J. 2021;21(10):1643–8.

PubMed  Article  Google Scholar 

Wang H, Zhang L, Liu Z, Wang X, Geng S, Li J, et al. Predicting medication nonadherence risk in a Chinese inflammatory rheumatic disease population: development and assessment of a new predictive nomogram. Patient Prefer Adherence. 2018;12:1757–65.

PubMed  PubMed Central  Article  Google Scholar 

Alderden J, Pepper GA, Wilson A, Whitney JD, Richardson S, Butcher R, et al. Predicting pressure injury in critical care patients: a machine-learning model. Am J Crit Care. 2018;27(6):461–8.

PubMed  PubMed Central  Article  Google Scholar 

Zhang H, Wang W, Haggerty J, Schuster T. Predictors of patient satisfaction and outpatient health services in China: evidence from the WHO SAGE survey. Fam Pract. 2020;37(4):465–72.

PubMed  PubMed Central  Article  Google Scholar 

Pfau M, von der Emde L, Dysli C, Möller PT, Thiele S, Lindner M, et al. Determinants of cone and rod functions in geographic atrophy: AI-based structure–function correlation. Am J Ophthalmol. 2020;217:162–73.

PubMed  Article  Google Scholar 

Zhang M, Zhu K, Pu H, Wang Z, Zhao H, Zhang J, et al. An immune-related signature predicts survival in patients with lung adenocarcinoma. Front Oncol. 2019;9:1314.

PubMed  PubMed Central  Article  Google Scholar 

Wang S, Su W, Zhong C, Yang T, Chen W, Chen G, et al. An Eight-CircRNA assessment model for predicting biochemical recurrence in prostate cancer. Front Cell Dev Biol. 2020;8: 599494.

PubMed  PubMed Central  Article  Google Scholar 

Duan KB, Rajapakse JC, Wang H, Azuaje F. Multiple SVM-RFE for gene selection in cancer classification with expression data. IEEE Trans Nanobiosci. 2005;4(3):228–34.

Article  Google Scholar 

Zhao E, Xie H, Zhang Y. Predicting diagnostic gene biomarkers associated with immune infiltration in patients with acute myocardial infarction. Front Cardiovasc Med. 2020;7: 586871.

CAS  PubMed  PubMed Central  Article  Google Scholar 

Huang YQ, Liang CH, He L, Tian J, Liang CS, Chen X, et al. Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer. J Clin Oncol. 2016;34(18):2157–64.

PubMed  Article  Google Scholar 

Sorrentino FS, Jurman G, De Nadai K, Campa C, Furlanello C, Parmeggiani F. Application of artificial intelligence in targeting retinal diseases. Curr Drug Targets. 2020;21(12):1208–15.

CAS  PubMed  Article  Google Scholar 

Ngiam KY, Khor IW. Big data and machine learning algorithms for health-care delivery. Lancet Oncol. 2019;20(5):e262–73.

PubMed  Article  Google Scholar 

Handelman GS, Kok HK, Chandra RV, Razavi AH, Lee MJ, Asadi H. eDoctor: machine learning and the future of medicine. J Intern Med. 2018;284(6):603–19.

CAS  PubMed  Article  Google Scholar 

Thrall JH, Li X, Li Q, Cruz C, Do S, Dreyer K, et al. Artificial intelligence and machine learning in radiology: opportunities, challenges, pitfalls, and criteria for success. J Am Coll Radiol JACR. 2018;15(3 Pt B):504–8.

PubMed  Article  Google Scholar 

Herzog NJ, Magoulas GD. Brain asymmetry detection and machine learning classification for diagnosis of early dementia. Sensors (Basel, Switzerland). 2021;21(3):778.

PubMed Central  Article  Google Scholar 

Lynch CM, Abdollahi B, Fuqua JD, de Carlo AR, Bartholomai JA, Balgemann RN, et al. Prediction of lung cancer patient survival via supervised machine learning classification techniques. Int J Med Inf. 2017;108:1–8.

Article  Google Scholar 

Doupe P, Faghmous J, Basu S. Machine learning for health services researchers. Value Health. 2019;22(7):808–15.

PubMed  Article  Google Scholar 

Nygaard A, Ljungdalh PS, Iachina M, Nikolov TN, Schiottz-Christensen B. Incidence of ankylosing spondylitis and spondyloarthritis in 2000–2013: a nationwide Danish cohort study. Scand J Rheumatol. 2020;49(1):21–7.

CAS  PubMed  Article  Google Scholar 

Crossfield SSR, Marzo-Ortega H, Kingsbury SR, Pujades-Rodriguez M, Conaghan PG. Changes in ankylosing spondylitis incidence, prevalence and time to diagnosis over two decades. RMD Open. 2021;7(3):e001888.

PubMed  PubMed Central  Article  Google Scholar 

Ibn Yacoub Y, Amine B, Laatiris A, Hajjaj-Hassouni N. Gender and disease features in Moroccan patients with ankylosing spondylitis. Clin Rheumatol. 2012;31(2):293–7.

PubMed  Article  Google Scholar 

Jiao JB, Huang JC, Chen X, Jin Y. Albumin to globulin ratio, neutrophil to lymphocyte ratio, and globulin levels do not outperform ESR or CRP when diagnosing periprosthetic joint infection. BMC Musculoskelet Disord. 2022;23(1):404.

CAS  PubMed  PubMed Central  Article  Google Scholar 

Kang KY, Chung MK, Kim HN, Hong YS, Ju JH, Park SH. Severity of sacroiliitis and erythrocyte sedimentation rate are associated with a low trabecular bone score in young male patients with ankylosing spondylitis. J Rheumatol. 2018;45(3):349–56.

PubMed  Article  Google Scholar 

Chen CH, Chen HA, Liao HT, Liu CH, Tsai CY, Chou CT. The clinical usefulness of ESR, CRP, and disease duration in ankylosing spondylitis: the product of these acute-phase reactants and disease duration is associated with patient’s poor physical mobility. Rheumatol Int. 2015;35(7):1263–7.

CAS  PubMed  Article  Google Scholar 

Carson JL, Stanworth SJ, Dennis JA, Trivella M, Roubinian N, Fergusson DA, et al. Transfusion thresholds for guiding red blood cell transfusion. Cochrane Database Syst Rev. 2021;12(12):Cd002042.

PubMed