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Combinatorial Chemistry & High Throughput Screening

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ISSN (Print): 1386-2073
ISSN (Online): 1875-5402

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Mini-Review Article

Application of Machine Learning in Rheumatoid Arthritis Diseases Research: Review and Future Directions

Author(s): Aparna Hiren Patil Kose* and Kiran Mangaonkar

Volume 26, Issue 13, 2023

Published on: 11 April, 2023

Page: [2259 - 2266] Pages: 8

DOI: 10.2174/1386207326666230306114626

Price: $65

Abstract

Rheumatoid arthritis (RA) is a chronic, destructive condition that affects and destroys the joints of the hand, fingers, and legs. Patients may forfeit the ability to conduct a normal lifestyle if neglected. The requirement for implementing data science to improve medical care and disease monitoring is emerging rapidly as a consequence of advancements in computational technologies. Machine learning (ML) is one of these approaches that has emerged to resolve complicated issues across various scientific disciplines. Based on enormous amounts of data, ML enables the formulation of standards and drafting of the assessment process for complex diseases. ML can be expected to be very beneficial in assessing the underlying interdependencies in the disease progression and development of RA. This could perhaps improve our comprehension of the disease, promote health stratification, optimize treatment interventions, and speculate prognosis and outcomes.

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[1]
Rashidi, H.H.; Tran, N.; Albahra, S.; Dang, L.T. Machine learning in health care and laboratory medicine: General overview of supervised learning and Auto‐ML. Int. J. Lab. Hematol., 2021, 43(S1)(Suppl. 1), 15-22.
[http://dx.doi.org/10.1111/ijlh.13537] [PMID: 34288435]
[2]
Wiens, J.; Saria, S.; Sendak, M.; Ghassemi, M.; Liu, V.X.; Doshi-Velez, F.; Jung, K.; Heller, K.; Kale, D.; Saeed, M.; Ossorio, P.N.; Thadaney-Israni, S.; Goldenberg, A. Do no harm: A roadmap for responsible machine learning for health care. Nat. Med., 2019, 25(9), 1337-1340.
[http://dx.doi.org/10.1038/s41591-019-0548-6] [PMID: 31427808]
[3]
Jiang, M.; Li, Y.; Jiang, C.; Zhao, L.; Zhang, X.; Lipsky, P.E. Machine learning in rheumatic diseases. Clin. Rev. Allergy Immunol., 2021, 60(1), 96-110.
[http://dx.doi.org/10.1007/s12016-020-08805-6] [PMID: 32681407]
[4]
Hügle, M.; Omoumi, P.; van Laar, J.M.; Boedecker, J.; Hügle, T. Applied machine learning and artificial intelligence in rheumatology. Rheumatol. Adv. Pract., 2020, 4(1), rkaa005.
[http://dx.doi.org/10.1093/rap/rkaa005] [PMID: 32296743]
[5]
Kim, K.J.; Tagkopoulos, I. Application of machine learning in rheumatic disease research. Korean J. Intern. Med. (Korean. Assoc. Intern. Med.), 2019, 34(4), 708-722.
[http://dx.doi.org/10.3904/kjim.2018.349] [PMID: 30616329]
[6]
Kotsiantis, S.B.; Zaharakis, I.D.; Pintelas, P.E. Machine learning: A review of classification and combining techniques. Artif. Intell. Rev., 2006, 26(3), 159-190.
[http://dx.doi.org/10.1007/s10462-007-9052-3]
[7]
Ray, S. A quick review of machine learning algorithms. 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon), India, 2019, pp. 35-39.
[http://dx.doi.org/10.1109/COMITCon.2019.8862451]
[8]
Caie, P.D.; Dimitriou, N.; Arandjelović, O. Precision medicine in digital pathology via image analysis and machine learning. Artificial Intelligence and Deep Learning in Pathology; Elsevier, 2021, pp. 149-173.
[http://dx.doi.org/10.1016/B978-0-323-67538-3.00008-7]
[9]
Kobylarz Ribeiro, J.; dos Santos, H.D.P.; Barletta, F.; Cichelero da Silva, M.; Vieira, R. A machine learning early warning system: Multicenter validation in Brazilian hospitals. 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS), 2020, pp. 321-326.
[10]
Nasteski, V. An overview of the supervised machine learning methods. HORIZONS.B, 2017, 4, 51-62.
[http://dx.doi.org/10.20544/HORIZONS.B.04.1.17.P05]
[11]
Jorge, A.; Castro, V.M.; Barnado, A.; Gainer, V.; Hong, C.; Cai, T.; Cai, T.; Carroll, R.; Denny, J.C.; Crofford, L.; Costenbader, K.H.; Liao, K.P.; Karlson, E.W.; Feldman, C.H. Identifying lupus patients in electronic health records: Development and validation of machine learning algorithms and application of rule-based algorithms. Semin. Arthritis Rheum., 2019, 49(1), 84-90.
[http://dx.doi.org/10.1016/j.semarthrit.2019.01.002] [PMID: 30665626]
[12]
Jamian, L.; Wheless, L.; Crofford, L.J.; Barnado, A. Rule-based and machine learning algorithms identify patients with systemic sclerosis accurately in the electronic health record. Arthritis Res. Ther., 2019, 21(1), 305.
[http://dx.doi.org/10.1186/s13075-019-2092-7] [PMID: 31888720]
[13]
Usama, M.; Qadir, J.; Raza, A.; Arif, H.; Yau, K.A.; Elkhatib, Y.; Hussain, A.; Al-Fuqaha, A. Unsupervised machine learning for networking: Techniques, applications and research challenges. IEEE Access, 2019, 7, 65579-65615.
[http://dx.doi.org/10.1109/ACCESS.2019.2916648]
[14]
Andreu-Perez, J; Garcia-Gancedo, L; McKinnell, J; Van der Drift, A; Powell, A; Hamy, V; Keller, T; Yang, GZ Developing fine-grained actigraphies for rheumatoid arthritis patients from a single accelerometer using machine learning. Sensors., 2017, 17(9), 2113.
[http://dx.doi.org/10.31219/osf.io/qm2vs]
[15]
Castelli, M.; Vanneschi, L.; Largo, Á.R. Supervised learning: Classification.Encyclopedia of Bioinformatics and Computational Biology; Elsevier, 2019, pp. 342-349.
[http://dx.doi.org/10.1016/B978-0-12-809633-8.20332-4]
[16]
Zenke, F.; Ganguli, S. SuperSpike: Supervised learning in multilayer spiking neural networks. Neural Comput., 2018, 30(6), 1514-1541.
[http://dx.doi.org/10.1162/neco_a_01086] [PMID: 29652587]
[17]
Settouti, N.; El Habib Daho, M.; El Amine Lazouni, M.; Chikh, M.A. Random forest in semi-supervised learning (Co-Forest). 2013 8th International Workshop on Systems, Signal Processing and their Applications (WoSSPA), 2013, pp. 326-329.
[18]
Kramer, O. Dimensionality Reduction with Unsupervised Nearest Neighbors; Springer: Berlin, Heidelberg, Berlin, Heidelberg, 2013.
[http://dx.doi.org/10.1007/978-3-642-38652-7]
[19]
Ding, S.; Zhu, Z.; Zhang, X. An overview on semi-supervised support vector machine. Neural Comput. Appl., 2017, 28(5), 969-978.
[http://dx.doi.org/10.1007/s00521-015-2113-7]
[20]
Collobert, R.; Weston, J. A unified architecture for natural language processing: Deep neural networks with multitask learning. Proceedings of the 25th international conference on Machine learning - ICML ’08., 2008.
[21]
Aletaha, D.; Smolen, J.S. Diagnosis and management of rheumatoid arthritis: A review. JAMA, 2018, 320(13), 1360-1372.
[http://dx.doi.org/10.1001/jama.2018.13103] [PMID: 30285183]
[22]
Lin, Y.J.; Anzaghe, M.; Schülke, S. Update on the pathomechanism, diagnosis, and treatment options for rheumatoid arthritis. Cells, 2020, 9(4), 880.
[http://dx.doi.org/10.3390/cells9040880] [PMID: 32260219]
[23]
Kim, K.J.; Kim, M.; Adamopoulos, I.E.; Tagkopoulos, I. Compendium of synovial signatures identifies pathologic characteristics for predicting treatment response in rheumatoid arthritis patients. Clin. Immunol., 2019, 202, 1-10.
[http://dx.doi.org/10.1016/j.clim.2019.03.002] [PMID: 30831253]
[24]
Guan, Y.; Zhang, H.; Quang, D.; Wang, Z.; Parker, S.C.J.; Pappas, D.A.; Kremer, J.M.; Zhu, F. Machine learning to predict anti–tumor necrosis factor drug responses of rheumatoid arthritis patients by integrating clinical and genetic markers. Arthritis Rheumatol., 2019, 71(12), 1987-1996.
[http://dx.doi.org/10.1002/art.41056] [PMID: 31342661]
[25]
Eng, S.W.M.; Aeschlimann, F.A.; van Veenendaal, M.; Berard, R.A.; Rosenberg, A.M.; Morris, Q.; Yeung, R.S.M. Patterns of joint involvement in juvenile idiopathic arthritis and prediction of disease course: A prospective study with multilayer non-negative matrix factorization. PLoS Med., 2019, 16(2), e1002750.
[http://dx.doi.org/10.1371/journal.pmed.1002750] [PMID: 30807586]
[26]
Sebastiani, M.; Vacchi, C.; Manfredi, A.; Cassone, G. Personalized medicine and machine learning: A roadmap for the future. J. Clin. Med., 2022, 11(14), 4110.
[http://dx.doi.org/10.3390/jcm11144110] [PMID: 35887873]
[27]
Pandit, A.; Radstake, T.R.D.J. Machine learning in rheumatology approaches the clinic. Nat. Rev. Rheumatol., 2020, 16(2), 69-70.
[http://dx.doi.org/10.1038/s41584-019-0361-0] [PMID: 31908355]
[28]
Morita, K.; Tashita, A.; Nii, M.; Kobashi, S. Computer-aided diagnosis system for Rheumatoid Arthritis using machine learning.2017 International Conference on Machine Learning and Cybernetics (ICMLC); Ningbo, China, 2017, 99, pp. 357-360.
[http://dx.doi.org/10.1109/ICMLC.2017.8108947]
[29]
Norgeot, B.; Glicksberg, B.S.; Trupin, L.; Lituiev, D.; Gianfrancesco, M.; Oskotsky, B.; Schmajuk, G.; Yazdany, J.; Butte, A.J. Assessment of a deep learning model based on electronic health record data to forecast clinical outcomes in patients with rheumatoid arthritis. JAMA Netw. Open, 2019, 2(3), e190606.
[http://dx.doi.org/10.1001/jamanetworkopen.2019.0606] [PMID: 30874779]
[30]
Sundaramurthy, S.; Saravanabhavan; Kshirsagar, P. Prediction and classification of rheumatoid arthritis using ensemble machine learning approaches. 2020 International Conference on Decision Aid Sciences and Application (DASA), Bahrain, 2022, pp. 17-21.
[http://dx.doi.org/10.1109/DASA51403.2020.9317253]
[31]
Maarseveen, T.D.; Meinderink, T.; Reinders, M.J.T.; Knitza, J.; Huizinga, T.W.J.; Kleyer, A. Machine learning electronic health record identification of patients with rheumatoid arthritis: Algorithm pipeline development and validation study JMIR Med Inform, 2020, 8(11), e23930.
[http://dx.doi.org/10.2196/preprints.23930]
[32]
Maarseveen, T.D.; Maurits, M.P.; Niemantsverdriet, E.; van der Helm-van Mil, A.H.M.; Huizinga, T.W.J.; 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 Res. Ther., 2021, 23(1), 174.
[http://dx.doi.org/10.1186/s13075-021-02553-4] [PMID: 34158089]
[33]
Menni, C.; Zierer, J.; Valdes, A.M.; Spector, T.D. Mixing omics: Combining genetics and metabolomics to study rheumatic diseases. Nat. Rev. Rheumatol., 2017, 13(3), 174-181.
[http://dx.doi.org/10.1038/nrrheum.2017.5] [PMID: 28148918]
[34]
Caporali, R.; Caprioli, M.; Bobbio-Pallavicini, F.; Montecucco, C. DMARDS and infections in rheumatoid arthritis. Autoimmun. Rev., 2008, 8(2), 139-143.
[http://dx.doi.org/10.1016/j.autrev.2008.05.001] [PMID: 19014871]
[35]
Gavrilă, B.I.; Ciofu, C.; Stoica, V. Biomarkers in Rheumatoid Arthritis, what is new? J. Med. Life, 2016, 9(2), 144-148.
[PMID: 27453744]
[36]
Heard, B.J.; Rosvold, J.M.; Fritzler, M.J.; El-Gabalawy, H.; Wiley, J.P.; Krawetz, R.J. A computational method to differentiate normal individuals, osteoarthritis and rheumatoid arthritis patients using serum biomarkers. J. R. Soc. Interface, 2014, 11(97), 20140428.
[http://dx.doi.org/10.1098/rsif.2014.0428] [PMID: 24920114]
[37]
Ardle, A.M.; Kwasnik, A.; Szenpetery, A.; Jones, M.; Hernandez, B.; Meenagh, M. Identification and evaluation of serum protein biomarkers which differentiate psoriatic from rheumatoid arthritis. medRxiv, 2020, 6.
[http://dx.doi.org/10.1101/2020.06.23.20138552]
[38]
Liu, J.; Chen, N. A 9 mRNAs-based diagnostic signature for rheumatoid arthritis by integrating bioinformatic analysis and machine-learning. J. Orthop. Surg. Res., 2021, 16(1), 44.
[http://dx.doi.org/10.1186/s13018-020-02180-w] [PMID: 33430905]
[39]
Morris, M.A.; Saboury, B.; Burkett, B.; Gao, J.; Siegel, E.L. Reinventing radiology: Big data and the future of medical imaging. J. Thorac. Imaging, 2018, 33(1), 4-16.
[http://dx.doi.org/10.1097/RTI.0000000000000311] [PMID: 29252898]
[40]
Reed, M.; Le Souëf, T.; Rampono, E. Pilot study of a machine-learning tool to assist in the diagnosis of hand arthritis. Intern. Med. J., 2022, 52(6), 959-967.
[http://dx.doi.org/10.1111/imj.15173] [PMID: 33342022]
[41]
Hirano, T.; Nishide, M.; Nonaka, N.; Seita, J.; Ebina, K.; Sakurada, K.; Kumanogoh, A. Development and validation of a deep-learning model for scoring of radiographic finger joint destruction in rheumatoid arthritis. Rheumatol. Adv. Pract., 2019, 3(2), rkz047.
[http://dx.doi.org/10.1093/rap/rkz047] [PMID: 31872173]
[42]
Cupek, R.; Ziębiński, A. Automated assessment of joint synovitis activity from medical ultrasound and power doppler examinations using image processing and machine learning methods. Reumatologia, 2016, 5(5), 239-242.
[http://dx.doi.org/10.5114/reum.2016.63664] [PMID: 27994268]
[43]
Dougados, M. Comorbidities in rheumatoid arthritis. Curr. Opin. Rheumatol., 2016, 28(3), 282-288.
[http://dx.doi.org/10.1097/BOR.0000000000000267] [PMID: 27027814]
[44]
Khanna, N.N.; Jamthikar, A.D.; Gupta, D.; Piga, M.; Saba, L.; Carcassi, C.; Giannopoulos, A.A.; Nicolaides, A.; Laird, J.R.; Suri, H.S.; Mavrogeni, S.; Protogerou, A.D.; Sfikakis, P.; Kitas, G.D.; Suri, J.S. Rheumatoid arthritis: Atherosclerosis imaging and cardiovascular risk assessment using machine and deep learning–based tissue characterization. Curr. Atheroscler. Rep., 2019, 21(2), 7.
[http://dx.doi.org/10.1007/s11883-019-0766-x] [PMID: 30684090]
[45]
Wojciechowski, K.; Smolka, B.; Cupek, R.; Ziebinski, A.; Nurzynska, K.; Kulbacki, M. A machine-learning approach to the automated assessment of joint synovitis activity. Computational Collective Intelligence; Springer International Publishing: Cham, 2016, pp. 440-450.
[http://dx.doi.org/10.1007/978-3-319-45246-3_42]
[46]
Lekadir, K.; Galimzianova, A.; Betriu, A.; del Mar Vila, M.; Igual, L.; Rubin, D.L.; Fernandez, E.; Radeva, P.; Napel, S. A convolutional neural network for automatic characterization of plaque composition in carotid ultrasound. IEEE J. Biomed. Health Inform., 2017, 21(1), 48-55.
[http://dx.doi.org/10.1109/JBHI.2016.2631401] [PMID: 27893402]
[47]
Gosselt, H.R.; Verhoeven, M.M.A.; Bulatović-Ćalasan, M.; Welsing, P.M.; de Rotte, M.C.F.J.; Hazes, J.M.W.; Lafeber, F.P.J.G.; Hoogendoorn, M.; de Jonge, R. Complex machine-learning algorithms and multivariable logistic regression on par in the prediction of insufficient clinical response to methotrexate in rheumatoid arthritis. J. Pers. Med., 2021, 11(1), 44.
[http://dx.doi.org/10.3390/jpm11010044] [PMID: 33466633]
[48]
Tao, W.; Concepcion, A.N.; Vianen, M.; Marijnissen, A.C.A.; Lafeber, F.P.G.J.; Radstake, T.R.D.J.; Pandit, A. Multiomics and machine learning accurately predict clinical response to adalimumab and etanercept therapy in patients with rheumatoid arthritis. Arthritis Rheumatol., 2021, 73(2), 212-222.
[http://dx.doi.org/10.1002/art.41516] [PMID: 32909363]
[49]
Rehberg, M.; Giegerich, C.; Praestgaard, A.; van Hoogstraten, H.; Iglesias-Rodriguez, M.; Curtis, J.R.; Gottenberg, J.E.; Schwarting, A.; Castañeda, S.; Rubbert-Roth, A.; Choy, E.H.S. Identification of a rule to predict response to sarilumab in patients with rheumatoid arthritis using machine learning and clinical trial data. Rheumatol. Ther., 2021, 8(4), 1661-1675.
[http://dx.doi.org/10.1007/s40744-021-00361-5] [PMID: 34519964]
[50]
Davergne, T.; Kedra, J.; Gossec, L. Wearable activity trackers and artificial intelligence in the management of rheumatic diseases. Z. Rheumatol., 2021, 80(10), 928-935.
[http://dx.doi.org/10.1007/s00393-021-01100-5] [PMID: 34633504]
[51]
Kelly, C.J.; Karthikesalingam, A.; Suleyman, M.; Corrado, G.; King, D. Key challenges for delivering clinical impact with artificial intelligence. BMC Med., 2019, 17(1), 195.
[http://dx.doi.org/10.1186/s12916-019-1426-2] [PMID: 31665002]
[52]
Plant, D.; Barton, A. Machine learning in precision medicine: Lessons to learn. Nat. Rev. Rheumatol., 2021, 17(1), 5-6.
[http://dx.doi.org/10.1038/s41584-020-00538-2] [PMID: 33184488]
[53]
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 Rev. Clin. Immunol., 2021, 17(12), 1311-1321.
[http://dx.doi.org/10.1080/1744666X.2022.2017773] [PMID: 34890271]
[54]
Nair, S.S.; French, R.M.; Laroche, D.; Thomas, E. The application of machine learning algorithms to the analysis of electromyographic patterns from arthritic patients. IEEE Trans. Neural Syst. Rehabil. Eng., 2010, 18(2), 174-184.
[http://dx.doi.org/10.1109/TNSRE.2009.2032638] [PMID: 19775984]
[55]
Matsuo, H.; Kamada, M.; Imamura, A.; Shimizu, M.; Inagaki, M.; Tsuji, Y.; Hashimoto, M.; Tanaka, M.; Ito, H.; Fujii, Y. Machine learning-based prediction of relapse in rheumatoid arthritis patients using data on ultrasound examination and blood test. Sci. Rep., 2022, 12(1), 7224.
[http://dx.doi.org/10.1038/s41598-022-11361-y] [PMID: 35508670]
[56]
Üreten, K.; Erbay, H.; Maraş, H.H. Detection of rheumatoid arthritis from hand radiographs using a convolutional neural network. Clin. Rheumatol., 2020, 39(4), 969-974.
[http://dx.doi.org/10.1007/s10067-019-04487-4] [PMID: 30850962]
[57]
Tiulpin, A.; Klein, S.; Bierma-Zeinstra, S.M.A.; Thevenot, J.; Rahtu, E.; Meurs, J.; Oei, E.H.G.; Saarakkala, S. Multimodal machine learning-based knee osteoarthritis progression prediction from plain radiographs and clinical data Sci. Sci. Rep., 2019, 9(1), 20038.
[http://dx.doi.org/10.1038/s41598-019-56527-3] [PMID: 30626917]
[58]
Tang, J.; Jin, Z.; Zhou, X.; Zhang, W.; Wu, M.; Shen, Q.; Cheng, Q.; Wang, X.; Yuan, J. Enhancing convolutional neural network scheme for rheumatoid arthritis grading with limited clinical data. Chin. Phys. B, 2019, 28(3), 038701.
[http://dx.doi.org/10.1088/1674-1056/28/3/038701]
[59]
Sharon, H.; Elamvazuthi, I.; Lu, C.K.; Parasuraman, S.; Natarajan, E. Classification of rheumatoid arthritis using machine learning algorithms. 2019 IEEE Student Conference on Research and Development (SCOReD), Malaysia, 2019, pp. 245-250.
[http://dx.doi.org/10.1109/SCORED.2019.8896344]
[60]
Stoel, B.C. Artificial intelligence in detecting early RA. Semin. Arthritis Rheum., 2019, 49(3), S25-S28.
[http://dx.doi.org/10.1016/j.semarthrit.2019.09.020] [PMID: 31779846]
[61]
Prasad, B.; McGeough, C.; Eakin, A.; Ahmed, T.; Small, D.; Gardiner, P.; Pendleton, A.; Wright, G.; Bjourson, A.J.; Gibson, D.S.; Shukla, P. ATRPred: A machine learning based tool for clinical decision making of anti-TNF treatment in rheumatoid arthritis patients. PLOS Comput. Biol., 2022, 18(7), e1010204.
[http://dx.doi.org/10.1371/journal.pcbi.1010204] [PMID: 35788746]
[62]
Maheshwari, D.; Garcia-Zapirain, B.; Sierra-Sosa, D. Quantum machine learning applications in the biomedical domain: A systematic review. IEEE Access, 2022, 10, 80463-80484.
[http://dx.doi.org/10.1109/ACCESS.2022.3195044]
[63]
Dutt, V.; Chandrasekaran, S.; García-Díaz, V. Quantum neural networks for disease treatment identification. Eur. J. Mol. Clin. Med., 2020, 7(11), 57-67.
[64]
Batra, K.; Zorn, K.M.; Foil, D.H.; Minerali, E.; Gawriljuk, V.O.; Lane, T.R.; Ekins, S. Quantum machine learning algorithms for drug discovery applications. J. Chem. Inf. Model., 2021, 61(6), 2641-2647.
[http://dx.doi.org/10.1021/acs.jcim.1c00166] [PMID: 34032436]
[65]
Shehab, O.; Krunic, Z.; Floether, F.; Seegan, G.; Earnest-Noble, N. Quantum kernels for electronic health records classification. APS March Meeting Abstracts, 2022, 2022, pp. S37-43.
[66]
Chang, W.L.; Chen, J.C.; Chung, W.Y.; Hsiao, C.Y.; Wong, R.; Vasilakos, A.V. Quantum speedup and mathematical solutions of implementing bio-molecular solutions for the independent set problem on IBM quantum computers. IEEE Trans. Nanobiosci., 2021, 20(3), 354-376.
[http://dx.doi.org/10.1109/TNB.2021.3075733] [PMID: 33900920]

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