REFERENCES
1. Lazar S, Kahlenberg JM. Systemic lupus erythematosus: new diagnostic and therapeutic approaches. Annu Rev Med. 2023;74:339-52.
2. Zhan K, Buhler KA, Chen IY, Fritzler MJ, Choi MY. Systemic lupus in the era of machine learning medicine. Lupus Sci Med. 2024;11:e001140.
3. Nelson AE, Arbeeva L. Narrative review of machine learning in rheumatic and musculoskeletal diseases for clinicians and researchers: biases, goals, and future directions. J Rheumatol. 2022;49:1191-200.
4. Kingsmore KM, Puglisi CE, Grammer AC, Lipsky PE. An introduction to machine learning and analysis of its use in rheumatic diseases. Nat Rev Rheumatol. 2021;17:710-30.
5. Ceccarelli F, Natalucci F, Picciariello L, et al. Application of machine learning models in systemic lupus erythematosus. Int J Mol Sci. 2023;24:4514.
6. Hubbard EL, Bachali P, Kingsmore KM, et al. Analysis of transcriptomic features reveals molecular endotypes of SLE with clinical implications. Genome Med. 2023;15:84.
7. Jordan MI, Mitchell TM. Machine learning: trends, perspectives, and prospects. Science. 2015;349:255-60.
8. Kim KJ, Tagkopoulos I. Application of machine learning in rheumatic disease research. Korean J Intern Med. 2019;34:708-22.
9. Bhaya WS. Review of data preprocessing techniques in data mining. ARPN J Eng Appl Sci. 2017;12:4102-7.
11. Tuikkala J, Elo LL, Nevalainen OS, Aittokallio T. Missing value imputation improves clustering and interpretation of gene expression microarray data. BMC Bioinf. 2008;9:202.
12. Clay B, Bergman HI, Salim S, Pergola G, Shalhoub J, Davies AH. Natural language processing techniques applied to the electronic health record in clinical research and practice - an introduction to methodologies. Comput Biol Med. 2025;188:109808.
13. Cao XH, Stojkovic I, Obradovic Z. A robust data scaling algorithm to improve classification accuracies in biomedical data. BMC Bioinf. 2016;17:359.
14. Bouchefry KE, de Souza RS. Chapter 12 - Learning in big data: introduction to machine learning. In: Knowledge discovery in big data from astronomy and earth observation. Elsevier; 2020, pp. 225-49.
15. Stanczyk U. Feature evaluation by filter, wrapper, and embedded approaches. Stud Comput Intell. 2015;584:29-44.
16. Guyon I, Elisseeff A. An introduction to variable and feature selection. J Mach Learn Res. 2023;3:1157-82.
17. Dietterich TG. Ensemble methods in machine learning. In: Multiple classifier systems. Berlin, Heidelberg: Springer Berlin Heidelberg; 2000, pp. 1-15.
18. Altman N, Krzywinski M. Ensemble methods: bagging and random forests. Nat Methods. 2017;14:933-4.
19. Schapire RE. The Boosting approach to machine learning: an overview. In: Denison DD, Hansen MH, Holmes CC, Mallick B, Yu B, editors. Nonlinear estimation and classification. New York, NY: Springer New York; 2003, pp. 149-71.
20. Snoek J, Larochelle H, Adams RP. Practical bayesian optimization of machine learning algorithms. ArXiv. 2021:arXiv:1206.2944. Available from: https://arxiv.org/abs/1206.2944 [Last accessed on 27 Apr 2026].
25. Kotsiantis SB, Zaharakis ID, Pintelas PE. Machine learning: a review of classification and combining techniques. Artif Intell Rev. 2006;26:59-190.
26. Arnold L, Rebecchi S, Chevallier S, Paugam-Moisy H. An introduction to deep learning. Available from: https://www.esann.org/sites/default/files/proceedings/legacy/es2011-4.pdf [Last accessed on 27 Apr 2026].
28. Kim JH. Estimating classification error rate: repeated cross-validation, repeated hold-out and bootstrap. Comput Stat Data Anal. 2009;53:3735-45.
31. Adamichou C, Genitsaridi I, Nikolopoulos D, et al. Lupus or not? SLE Risk Probability Index (SLERPI): a simple, clinician-friendly machine learning-based model to assist the diagnosis of systemic lupus erythematosus. Ann Rheum Dis. 2021;80:758-66.
32. Kapsala N, Nikolopoulos D, Flouda S, et al. First diagnosis of systemic lupus erythematosus in hospitalized patients: clinical phenotypes and pitfalls for the non-specialist. Am J Med. 2022;135:244-53.e3.
33. Batu ED, Kaya Akca U, Basaran O, Bilginer Y, Ozen S. Correspondence on 'Lupus or not? SLE Risk Probability Index (SLERPI): a simple, clinician-friendly machine-learning-based model to assist the diagnosis of systemic lupus erythematosus'. Ann Rheum Dis. 2023;82:e144.
34. Erden A, Apaydın H, Fanouriakis A, et al. Performance of the systemic lupus erythematosus risk probability index in a cohort of undifferentiated connective tissue disease. Rheumatology. 2022;61:3606-13.
35. Zhang L, Lu W, Yan D, Liu Z, Xue L. Systemic lupus erythematosus risk probability index: ready for routine use? Results from a Chinese cohort. Lupus Sci Med. 2023;10:e000988.
36. Castañeda-González JP, Mogollón Hurtado SA, Rojas-Villarraga A, et al. Comparison of the SLE Risk Probability Index (SLERPI) scale against the European League Against Rheumatism/American College of Rheumatology (ACR/EULAR) and Systemic Lupus International Collaborating Clinics (SLICC) criteria. Lupus. 2024;33:520-4.
37. Tan BCH, Tang I, Bonin J, Koelmeyer R, Hoi A. The performance of different classification criteria for systemic lupus erythematosus in a real-world rheumatology department. Rheumatology. 2022;61:4509-13.
38. Ceccarelli F, Lapucci M, Olivieri G, et al. Can machine learning models support physicians in systemic lupus erythematosus diagnosis? Results from a monocentric cohort. Joint Bone Spine. 2022;89:105292.
39. Park M. Improving the diagnosis of systemic lupus erythematosus with machine learning algorithms based on real-world data. Mathematics. 2024;12:2849.
40. Mok CC. Polygenic risk score: the potential role in the management of systemic lupus erythematosus. RMD Open. 2024;10:e004156.
41. Ma W, Lau YL, Yang W, Wang YF. Random forests algorithm boosts genetic risk prediction of systemic lupus erythematosus. Front Genet. 2022;13:902793.
42. Panousis NI, Bertsias GK, Ongen H, et al. Combined genetic and transcriptome analysis of patients with SLE: distinct, targetable signatures for susceptibility and severity. Ann Rheum Dis. 2019;78:1079-89.
43. Li H, Zhang X, Shang J, et al. Identification of NETs-related biomarkers and molecular clusters in systemic lupus erythematosus. Front Immunol. 2023;14:1150828.
44. Wang Y, Huang Z, Xiao Y, Wan W, Yang X. The shared biomarkers and pathways of systemic lupus erythematosus and metabolic syndrome analyzed by bioinformatics combining machine learning algorithm and single-cell sequencing analysis. Front Immunol. 2022;13:1015882.
45. Nikolopoulos D, Loukogiannaki C, Sentis G, et al. Disentangling the riddle of systemic lupus erythematosus with antiphospholipid syndrome: blood transcriptome analysis reveals a less-pronounced IFN-signature and distinct molecular profiles in venous versus arterial events. Ann Rheum Dis. 2024;83:1132-43.
46. Hocaoǧlu M, Valenzuela-Almada MO, Dabit JY, et al. Incidence, prevalence, and mortality of lupus nephritis: a population-based study over four decades using the lupus midwest network. Arthritis Rheumatol. 2023;75:567-73.
47. Agar JW, Webb GI. Application of machine learning to a renal biopsy database. Nephrol Dial Transplant. 1992;7:472-8.
48. Zheng Z, Zhang X, Ding J, et al. Deep learning-based artificial intelligence system for automatic assessment of glomerular pathological findings in lupus nephritis. Diagnostics. 2021;11:1983.
49. Tang Y, Zhang W, Zhu M, et al. Lupus nephritis pathology prediction with clinical indices. Sci Rep. 2018;8:10231.
50. Fava A, Buyon J, Magder L, et al. Urine proteomic signatures of histological class, activity, chronicity, and treatment response in lupus nephritis. JCI Insight. 2024;9:e172569.
51. Frangou E, Garantziotis P, Grigoriou M, et al. Cross-species transcriptome analysis for early detection and specific therapeutic targeting of human lupus nephritis. Ann Rheum Dis. 2022;81:1409-19.
52. Wang L, Yang Z, Yu H, et al. Predicting diagnostic gene expression profiles associated with immune infiltration in patients with lupus nephritis. Front Immunol. 2022;13:839197.
53. Wang F, Miao HB, Pei ZH, Chen Z. Serological, fragmentomic, and epigenetic characteristics of cell-free DNA in patients with lupus nephritis. Front Immunol. 2022;13:1001690.
54. Tang C, Zhang S, Teymur A, et al. V-set immunoglobulin domain-containing protein 4 as a novel serum biomarker of lupus nephritis and renal pathology activity. Arthritis Rheumatol. 2023;75:1573-85.
55. Mondal S, Singh MP, Kumar A, et al. Rapid molecular evaluation of human kidney tissue sections by in situ mass spectrometry and machine learning to classify the nephrotic syndrome. J Proteome Res. 2023;22:967-76.
56. Wu H, Yin H, Chen H, et al. A deep learning-based smartphone platform for cutaneous lupus erythematosus classification assistance: Simplifying the diagnosis of complicated diseases. J Am Acad Dermatol. 2021;85:792-3.
57. Guo LN, Said JT, Woodbury MJ, Nambudiri VE, Merola JF. Development and validation of algorithms to identify individuals with cutaneous lupus from healthcare databases. J Cutan Med Surg. 2025;29:131-6.
58. Martínez BA, Shrotri S, Kingsmore KM, Bachali P, Grammer AC, Lipsky PE. Machine learning reveals distinct gene signature profiles in lesional and nonlesional regions of inflammatory skin diseases. Sci Adv. 2022;8:eabn4776.
59. Lee DJ, Tsai PH, Chen CC, Dai YH. Incorporating knowledge of disease-defining hub genes and regulatory network into a machine learning-based model for predicting treatment response in lupus nephritis after the first renal flare. J Transl Med. 2023;21:76.
60. Ayoub I, Wolf BJ, Geng L, et al. Prediction models of treatment response in lupus nephritis. Kidney Int. 2022;101:379-89.
61. Wolf BJ, Spainhour JC, Arthur JM, Janech MG, Petri M, Oates JC. Development of biomarker models to predict outcomes in lupus nephritis. Arthritis Rheumatol. 2016;68:1955-63.
62. Helget LN, Dillon DJ, Wolf B, et al. Development of a lupus nephritis suboptimal response prediction tool using renal histopathological and clinical laboratory variables at the time of diagnosis. Lupus Sci Med. 2021;8:e000489.
63. Stojanowski J, Konieczny A, Rydzyńska K, et al. Artificial neural network - an effective tool for predicting the lupus nephritis outcome. BMC Nephrol. 2022;23:381.
64. Chen Y, Huang S, Chen T, et al. Machine learning for prediction and risk stratification of lupus nephritis renal flare. Am J Nephrol. 2021;52:152-60.
65. McDonald S, Yiu S, Su L, et al. Predictors of treatment response in a lupus nephritis population: lessons from the Aspreva Lupus Management Study (ALMS) trial. Lupus Sci Med. 2022;9:e000584.
66. Ceccarelli F, Olivieri G, Sortino A, et al. Comprehensive disease control in systemic lupus erythematosus. Semin Arthritis Rheum. 2021;51:404-8.
67. Shipa M, Santos LR, Nguyen DX, et al. Identification of biomarkers to stratify response to B-cell-targeted therapies in systemic lupus erythematosus: an exploratory analysis of a randomised controlled trial. Lancet Rheumatol. 2023;5:e24-35.
68. Moysidou GS, Garantziotis P, Sentis G, et al. Molecular basis for the disease-modifying effects of belimumab in systemic lupus erythematosus and molecular predictors of early response: blood transcriptome analysis implicates the innate immunity and DNA damage response pathways. Ann Rheum Dis. 2025;84:262-73.
69. Munroe ME, Blankenship D, DeFreese D, et al. A flare risk index informed by select immune mediators in systemic lupus erythematosus. Arthritis Rheumatol. 2023;75:723-35.
70. Garantziotis P, Nikolakis D, Doumas S, et al. Molecular taxonomy of systemic lupus erythematosus through data-driven patient stratification: molecular endotypes and cluster-tailored drugs. Front Immunol. 2022;13:860726.
71. Qiao J, Zhang SX, Chang MJ, et al. Deep stratification by transcriptome molecular characters for precision treatment of patients with systemic lupus erythematosus. Rheumatology. 2023;62:2574-84.
72. Toro-Domínguez D, Lopez-Domínguez R, García Moreno A. et al. Differential treatments based on drug-induced gene expression signatures and longitudinal systemic lupus erythematosus stratification. Sci Rep. 2019;9:15502.
73. Kan H, Nagar S, Patel J, Wallace DJ, Molta C, Chang DJ. Longitudinal treatment patterns and associated outcomes in patients with newly diagnosed systemic lupus erythematosus. Clin Ther. 2016;38:610-24.
74. Maeda S, Hashimoto H, Maeda T, et al. High-dimensional analysis of T-cell profiling variations following belimumab treatment in systemic lupus erythematosus. Lupus Sci Med. 2023;10:e000976.
75. Jorge AM, Smith D, Wu Z, et al. Exploration of machine learning methods to predict systemic lupus erythematosus hospitalizations. Lupus. 2022;31:1296-305.
