The DOI matches its Crossref record; 57 of the 59 references it cites were found in a registry or an index.
Machine learning to predict anti-tumor necrosis factor drug responses of rheumatoid arthritis patients by integrating clinical and genetic markers
Open accessAbstract
Objective: Accurate prediction of treatment responses in rheumatoid arthritis (RA) patients can provide valuable information on effective drug selection. Anti–tumor necrosis factor (anti‐TNF) drugs are an important second‐line treatment after methotrexate, the classic first‐line treatment for RA. However, patient heterogeneity hinders identification of predictive biomarkers and accurate modeling of anti‐TNF drug responses. This study was undertaken to investigate the usefulness of machine learning to assist in developing predictive models for treatment response. Methods: Using data on patient demographics, baseline disease assessment, treatment, and single‐nucleotide polymorphism (SNP) array from the Dialogue on Reverse Engineering Assessment and Methods (DREAM): Rheumatoid Arthritis Responder Challenge, we created a Gaussian process regression model to predict changes in the Disease Activity Score in 28 joints (DAS 28) for the patients and to classify them into either the responder or the nonresponder group. This model was developed and cross‐validated using data from 1,892 RA patients. It was evaluated using an independent data set from 680 patients. We examined the effectiveness of the similarity modeling and the contribution of individual features. Results: In the cross‐validation tests, our method predicted changes in DAS 28 (Δ DAS 28), with a correlation coefficient of 0.405. It correctly classified responses from 78% of patients. In the independent test, this method achieved a Pearson's correlation coefficient of 0.393 in predicting Δ DAS 28. Gaussian process regression effectively remapped the feature space and identified subpopulations that do not respond well to anti‐TNF treatments. Genetic SNP biomarkers showed small contributions in the prediction when added to the clinical models. This was the best‐performing model in the DREAM Challenge. Conclusion: The model described here shows promise in guiding treatment decisions in clinical practice, based primarily on clinical profiles with additional genetic information.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Despite several attempts, including machine learning utilisation,42–44 no single established biomarker has been reported in a real-life setting.45”
From Diurnal production of cortisol and prediction of treatment response in rheumatoid arthritis: a 6-month, real-life prospective cohort study (2024) · cites this paper as [42]
Citations
Cites
Arnett, F. C., Edworthy, S. M., Bloch, D. A., McShane, D. J., Fries, J. F., Cooper, N. S., Healey, L. A., Kaplan, S. R., Liang, M. H., Luthra, H. S., et al. (1988) · Arthritis & Rheumatism · doi:10.1002/art.1780310302 · Cited by 19,753
Gaussian Processes for Machine Learning
Rasmussen, C. E., & Williams, C. K. I. (2005) · doi:10.7551/mitpress/3206.001.0001 · Cited by 11,705
Revised Criteria for the Classification of Rheumatoid Arthritis
Arnett, F. C. (1990) · Orthopaedic Nursing · doi:10.1097/00006416-199003000-00011 · Cited by 7,742
Prevoo, M. L. L., Hof, M. A. V., Kuper, H. H., Leeuwen, M. A. V., van de Putte, L. B. A., & van Riel, P. L. C. M. (1995) · Arthritis & Rheumatism · doi:10.1002/art.1780380107 · Cited by 6,250
Infliximab and Methotrexate in the Treatment of Rheumatoid Arthritis
Lipsky, P. E., van der Heijde, D. M. F. M., Clair, E. W. S., Fürst, D. E., Breedveld, F. C., Kalden, J. R., Smolen, J. S., Weisman, M. H., Emery, P., Feldmann, M., et al. (2000) · New England Journal of Medicine · doi:10.1056/nejm200011303432202 · Cited by 3,059
Intelligible Models for HealthCare: Predicting Pneumonia Risk and Hospital 30-day Readmission
Caruana, R., Lou, Y., Gehrke, J., Koch, P., Sturm, M., & Elhadad, N. (2015) · Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2783258.2788613 · Cited by 1,685
van Gestel, A. M., Prevoo, M. L. L., Hof, M. A. V., van Rijswijk, M. H., van de Putte, L. B. A., & van Riel, P. L. C. M. (1996) · Arthritis & Rheumatism · doi:10.1002/art.1780390105 · Cited by 1,568
Applications of Machine Learning in Cancer Prediction and Prognosis
Cruz, J. A., & Wishart, D. S. (2006) · Cancer Informatics · doi:10.1177/117693510600200030 · Cited by 1,371
FaST linear mixed models for genome-wide association studies
Lippert, C., Listgarten, J., Liu, Y., Kadie, C. M., Davidson, R. I., & Heckerman, D. (2011) · Nature Methods · doi:10.1038/nmeth.1681 · Cited by 1,209
Aletaha, D., Nell, V. P. K., Stamm, T., Uffmann, M., Pflugbeil, S., Machold, K. P., & Smolen, J. S. (2005) · Arthritis Research & Therapy · doi:10.1186/ar1740 · Cited by 1,064
Flagged first, then by citations
2 of the 59 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- Rose NR, 2013, The autoimmune diseases
Cited by
Sorted by citations
Applied machine learning and artificial intelligence in rheumatology
Hügle, M., Omoumi, P., van Laar, J. M., Boedecker, J., & Hügle, T. (2020) · Rheumatology Advances in Practice · doi:10.1093/rap/rkaa005 · Cited by 187
Molecular and Cellular Heterogeneity in Rheumatoid Arthritis: Mechanisms and Clinical Implications
Zhao, J., Guo, S., Schrodi, S. J., & He, D. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.790122 · Cited by 167
Peng, J., Jury, E. C., Dönnes, P., & Ciurtin, C. (2021) · Frontiers in Pharmacology · doi:10.3389/fphar.2021.720694 · Cited by 162
Momtazmanesh, S., Nowroozi, A. A., & Rezaei, N. (2022) · Rheumatology and Therapy · doi:10.1007/s40744-022-00475-4 · Cited by 149
Adamichou, C., Genitsaridi, I., Nikolopoulos, D., Nikoloudaki, M., Repa, A., Bortoluzzi, A., Fanouriakis, A., Sidiropoulos, P. I., Boumpas, D. T., & Bertsias, G. K. (2021) · Annals of the Rheumatic Diseases · doi:10.1136/annrheumdis-2020-219069 · Cited by 123
Deep Learning Classification of Systemic Sclerosis Skin Using the MobileNetV2 Model
Akay, M., Du, Y., Sershen, C. L., Wu, M., Chen, T. Y., Assassi, S., Mohan, C., & Akay, Y. M. (2021) · IEEE Open Journal of Engineering in Medicine and Biology · doi:10.1109/ojemb.2021.3066097 · Cited by 128
An introduction to machine learning and analysis of its use in rheumatic diseases
Kingsmore, K. M., Puglisi, C., Grammer, A. C., & Lipsky, P. E. (2021) · Nature Reviews Rheumatology · doi:10.1038/s41584-021-00708-w · Cited by 86
Machine learning application in autoimmune diseases: State of art and future prospectives
Danieli, M. G., Brunetto, S., Gammeri, L., Palmeri, D., Claudi, I., Shoenfeld, Y., & Gangemi, S. (2023) · Autoimmunity Reviews · doi:10.1016/j.autrev.2023.103496 · Cited by 91
Synovial joint-on-a-chip for modeling arthritis: progress, pitfalls, and potential
Li, Z. A., Sant, S., Cho, S. K., Goodman, S. B., Bunnell, B. A., Tuan, R. S., Gold, M. S., & Lin, H. (2022) · Trends in biotechnology · doi:10.1016/j.tibtech.2022.07.011 · Cited by 80
Koo, B. S., Eun, S., Shin, K., Yoon, H., Hong, C., Kim, D., Hong, S., Kim, Y., Lee, C., Yoo, B., et al. (2021) · Arthritis Research & Therapy · doi:10.1186/s13075-021-02567-y · Cited by 69
Shi, Y., Zhou, M., Chang, C., Jiang, P., Wei, K.-S., Zhao, J.-N., Yu, S., Zheng, Y.-X., Zhao, F., Lv, X., et al. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1409555 · Cited by 59
Toward Overcoming Treatment Failure in Rheumatoid Arthritis
Wang, Z., Huang, J., Xie, D., He, D., Lü, A., & Liang, C. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.755844 · Cited by 57
Self-citationMachine learning in rheumatology approaches the clinic
Pandit, A., & Radstake, T. R. D. J. (2020) · Nature Reviews Rheumatology · doi:10.1038/s41584-019-0361-0 · Cited by 50
Lee, S., Kang, S., Eun, Y., Won, H., Kim, H., Lee, J., Koh, E., & Cha, H. (2021) · Arthritis Research & Therapy · doi:10.1186/s13075-021-02635-3 · Cited by 49
Bone and Joint‐on‐Chip Platforms: Construction Strategies and Applications
Du, C., Liu, J., Liu, S., Xiao, P., Chen, Z., Chen, H., Huang, W., & Lei, Y. (2024) · Small Methods · doi:10.1002/smtd.202400436 · Cited by 47
Machine Learning in Rheumatic Diseases
Jiang, M., Li, Y., Jiang, C., Zhao, L., Zhang, X., & Lipsky, P. E. (2020) · Clinical Reviews in Allergy & Immunology · doi:10.1007/s12016-020-08805-6 · Cited by 40
Verrou, K., Vlachogiannis, N. I., Ampatziadis-Michailidis, G., Moulos, P., Pavlopoulos, G. A., Hatzis, P., Kollias, G. A., & Sfikakis, P. P. (2021) · Rheumatology · doi:10.1093/rheumatology/keab052 · Cited by 40
Multi-omics approach to precision medicine for immune-mediated diseases
Ota, M., & Fujio, K. (2021) · Inflammation and Regeneration · doi:10.1186/s41232-021-00173-8 · Cited by 42
Gosselt, H. R., Verhoeven, M. M. A., Bulatović-Ćalasan, M., Welsing, P. M. J., de Rotte, M. C. F. J., Hazes, J. M. W., Lafeber, F. P. J. G., Hoogendoorn, M., & de Jonge, R. (2021) · Journal of Personalized Medicine · doi:10.3390/jpm11010044 · Cited by 37
Con, D., van Langenberg, D. R., & Vasudevan, A. (2021) · World Journal of Gastroenterology · doi:10.3748/wjg.v27.i38.6476 · Cited by 36
Biomarkers (mRNAs and non-coding RNAs) for the diagnosis and prognosis of rheumatoid arthritis
Jiang, Y., Zhong, S., He, S., Weng, J., Liu, L., Ye, Y., & Chen, H. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1087925 · Cited by 38
Benavent, D., Carmona, L., Llorente, J. F. G., Montoro, M., Ramirez, S., Otón, T., Loza, E., & Gómez-Centeno, A. (2025) · Rheumatology International · doi:10.1007/s00296-025-05825-3 · Cited by 39
Luque-Tévar, M., Pérez‐Sánchez, C., Patiño-Trives, A. M., Barbarroja, N., de la Rosa, I. A., Ábalos‐Aguilera, M. C., Marín-Sanz, J. A., Ruiz, D., Castro, R. O., Font, P., et al. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.631662 · Cited by 34
Duquesne, J., Bouget, V., Cournède, P.-H., Fautrel, B., Guillemin, F., de Jong, P. H. P., Heutz, J. W., Verstappen, M., Mil, A. H. M. V. D. H., Xavier Mariette, A., et al. (2022) · Lara D. Veeken · doi:10.1093/rheumatology/keac645 · Cited by 33
Artificial intelligence in rheumatoid arthritis: potential applications and future implications
Gilvaz, V. J., & Reginato, A. M. (2023) · Frontiers in Medicine · doi:10.3389/fmed.2023.1280312 · Cited by 40
Modi, K., Singh, I., & Kumar, Y. (2023) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-023-09957-2 · Cited by 34
The biomedical applications of artificial intelligence: an overview of decades of research
Naskar, S., Sharma, S., Kuotsu, K., Halder, S., Pal, G., Saha, S., Mondal, S., Biswas, U. K., Jana, M., & Bhattacharjee, S. (2025) · Journal of drug targeting · doi:10.1080/1061186x.2024.2448711 · Cited by 35
Venerito, V., Angelini, O., Fornaro, M., Cacciapaglia, F., Lopalco, G., & Iannone, F. (2021) · JCR Journal of Clinical Rheumatology · doi:10.1097/rhu.0000000000001720 · Cited by 29
An Efficient CNN for Hand X-Ray Classification of Rheumatoid Arthritis
Mate, G. S., Kureshi, A. K., & Singh, B. K. (2021) · Journal of Healthcare Engineering · doi:10.1155/2021/6712785 · Cited by 30
Salehi, F., González, L. I. L., Bayat, S., Kleyer, A., Zanca, D., Brost, A., Schett, G. A., & Eskofier, B. M. (2024) · Journal of Clinical Medicine · doi:10.3390/jcm13133890 · Cited by 29
Bouget, V., Duquesne, J., Hassler, S., Cournède, P.-H., Fautrel, B., Guillemin, F., Pallardy, M., Broët, P., Mariette, X., & Bitoun, S. (2022) · RMD Open · doi:10.1136/rmdopen-2022-002442 · Cited by 29
Madrid-García, A., Barbancho, B. M., Rodríguez‐González, A., Fernández‐Gutiérrez, B., Rodríguez‐Rodríguez, L., & Menasalvas, E. (2023) · Seminars in Arthritis and Rheumatism · doi:10.1016/j.semarthrit.2023.152213 · Cited by 29
Towards Personalized Medicine in Rheumatoid Arthritis
Sharma, S., & Bluett, J. (2024) · Open Access Rheumatology Research and Reviews · doi:10.2147/oarrr.s372610 · Cited by 26
Machine learning to predict early TNF inhibitor users in patients with ankylosing spondylitis
Lee, S., Eun, Y., Kim, H., Cha, H., Koh, E., & Lee, J. (2020) · Scientific Reports · doi:10.1038/s41598-020-75352-7 · Cited by 25
Rehberg, M., Giegerich, C., Praestgaard, A. H., van Hoogstraten, H., Iglesias‐Rodriguez, M., Curtis, J. R., Gottenberg, J., Schwarting, A., Castañeda, S., Rubbert‐Roth, A., et al. (2021) · Rheumatology and Therapy · doi:10.1007/s40744-021-00361-5 · Cited by 25
Yang, Y., Liu, Y., Chen, Y., Luo, D., Xu, K., & Zhang, L. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1477130 · Cited by 28
A survey of artificial intelligence in rheumatoid arthritis
Wang, J., Tian, Y., Zhou, T., Tong, D., Ma, J., & Li, J. (2023) · Rheumatology and Immunology Research · doi:10.2478/rir-2023-0011 · Cited by 26
Pinto, C. M., Sánchez-Tecuatl, M., Berra‐Romani, R., Castro, I. D. M., Etchegaray‐Morales, I., Munguía‐Realpozo, P., Cárdenas-García, M., Arellano-Avendaño, F. J., & García‐Carrasco, M. (2024) · Seminars in Arthritis and Rheumatism · doi:10.1016/j.semarthrit.2024.152501 · Cited by 24
Digital crowdsourcing: unleashing its power in rheumatology
Krusche, M., Burmester, G. R., & Knitza, J. (2020) · Annals of the Rheumatic Diseases · doi:10.1136/annrheumdis-2020-217697 · Cited by 22
Li, Y., Pan, J., Zhou, N., Fu, D., Lian, G., Yi, J., Peng, Y., & Liu, X. (2021) · Scandinavian Journal of Gastroenterology · doi:10.1080/00365521.2021.1939411 · Cited by 22
Lim, A. J. W., Lim, L. J., Ooi, B. N. S., Koh, E. T., Tan, J. W. L., Chong, S. S., Khor, C. C., Tucker‐Kellogg, L., Leong, K. P., & Lee, C. G. L. (2022) · EBioMedicine · doi:10.1016/j.ebiom.2021.103800 · Cited by 22
Westerlind, H., Maciejewski, M., Frisell, T., Jelinsky, S. A., Ziemek, D., & Askling, J. (2021) · ACR Open Rheumatology · doi:10.1002/acr2.11266 · Cited by 22
Wearable activity trackers and artificial intelligence in the management of rheumatic diseases
Davergne, T., Kedra, J., & Gossec, L. (2021) · Zeitschrift für Rheumatologie · doi:10.1007/s00393-021-01100-5 · Cited by 20
Munguía‐Realpozo, P., Etchegaray‐Morales, I., Pinto, C. M., Méndez‐Martínez, S., Osorio-Peña, Á. D., Ayón-Aguilar, J., & García‐Carrasco, M. (2023) · Autoimmunity Reviews · doi:10.1016/j.autrev.2023.103294 · Cited by 23
Kalweit, G., Burden, A. M., Boedecker, J., Hügle, T., & Burkard, T. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011073 · Cited by 20
Dudek, G., Sakowski, S., Brzezińska, O. E., Sarnik, J., Budlewski, T., Dragan, G., Popławska, M., Popławski, T., Bijak, M., & Makowska, J. S. (2024) · PLoS ONE · doi:10.1371/journal.pone.0300717 · Cited by 20
Prediction of treatment response: Personalized medicine in the management of rheumatoid arthritis
Wei, M., & Chu, C. (2022) · Best Practice & Research Clinical Rheumatology · doi:10.1016/j.berh.2021.101741 · Cited by 19
Pharmacogenomics of TNF inhibitors
Jan, Z., Assadi, F. E., Velayutham, D., Mifsud, B., & Jithesh, P. V. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1521794 · Cited by 19
Predictive analytics for step-up therapy: Supervised or semi-supervised learning?
Morid, M. A., Lau, M., & Fiol, G. D. (2021) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2021.103842 · Cited by 18
Cock, D. D., Myasoedova, E., Aletaha, D., & Studenic, P. (2022) · Therapeutic Advances in Musculoskeletal Disease · doi:10.1177/1759720x221105978 · Cited by 17
Lim, L. J., Lim, A. J. W., Ooi, B. N. S., Tan, J. W. L., Koh, E. T., TTSH Rheumatoid Arthritis Study Group, Ang, A. E. L., Chan, G. Y. L., Chan, M. T.-L., Chia, F. L., et al. (2022) · Lara D. Veeken · doi:10.1093/rheumatology/keac032 · Cited by 16
Joshi, I., Bhrdwaj, A., Khandelwal, R., Pande, A., Agarwal, A., Srija, C. D., Suresh, R. A., Mohan, M., Hazarika, L., Thakur, G., et al. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-85713-0.00021-9 · Cited by 17
Application of artificial intelligence in rheumatic disease: a bibliometric analysis
Zhao, J., Li, L., Li, J., & Zhang, L. (2024) · Clinical and Experimental Medicine · doi:10.1007/s10238-024-01453-6 · Cited by 14
Gilvaz, V. J., Sudheer, A., & Reginato, A. M. (2025) · Current Rheumatology Reports · doi:10.1007/s11926-025-01193-w · Cited by 15
Yavropoulou, M. P., Filippa, M. G., Vlachogiannis, N. I., Fragoulis, G. E., Laskari, K., Mantzou, A., Panopoulos, S., Fanouriakis, A., Bournia, V.-K., Evangelatos, G., et al. (2024) · RMD Open · doi:10.1136/rmdopen-2023-003575 · Cited by 13
“40 41 Despite several attempts, including machine learning utilisation, 42–44 no single established biomarker has been reported in a real-life setting.”
Cao, C., Zhao, W., Guo, J., Wang, Z., Lü, A., & Liang, C. C. (2025) · The Innovation Medicine · doi:10.59717/j.xinn-med.2025.100154 · Cited by 14
Self-citationApplications of AI in Predicting Drug Responses for Type 2 Diabetes
Garg, S., Kitchen, R. R., Gupta, R., & Pearson, E. R. (2025) · JMIR Diabetes · doi:10.2196/66831 · Cited by 15
Sutcliffe, M. A., Radley, G., & Barton, A. C. (2020) · Expert Review of Clinical Immunology · doi:10.1080/1744666x.2020.1740594 · Cited by 13
Digital Approaches for a Reliable Early Diagnosis of Psoriatic Arthritis
Fagni, F., Knitza, J., Krusche, M., Kleyer, A., Taşçılar, K., & Simón, D. (2021) · Frontiers in Medicine · doi:10.3389/fmed.2021.718922 · Cited by 13
Alsaber, A., Al-Herz, A. A., Alawadhi, B., Doush, I. A., Setiya, P., Al‐Sultan, A. T., Saleh, K., Al-Awadhi, A. M., Hasan, E., Al‐Kandari, W., et al. (2024) · Frontiers in Big Data · doi:10.3389/fdata.2024.1406365 · Cited by 12
Patterns and determinants of response to novel therapies in juvenile and adult-onset polyarthritis
Triaille, C., Quartier, P., Somer, L. D., Durez, P., Lauwerys, B. R., Verschueren, P., Taylor, P. C., & Wouters, C. H. (2023) · Rheumatology · doi:10.1093/rheumatology/kead490 · Cited by 10
Wang, Y., Wei, W., Ouyang, R., Chen, R., Wang, T., Xu, Y., Wang, F., Hou, H., & Wu, S. (2024) · Lupus Science & Medicine · doi:10.1136/lupus-2023-001125 · Cited by 12
Sonomoto, K., Fujino, Y., Tanaka, H., Nagayasu, A., Nakayamada, S., & Tanaka, Y. (2024) · Rheumatology and Therapy · doi:10.1007/s40744-024-00668-z · Cited by 10
GastricAITool: A Clinical Decision Support Tool for the Diagnosis and Prognosis of Gastric Cancer
Aznar-Gimeno, R., García-González, M. A., Sierra, R. M., Carrera‐Lasfuentes, P., Rodrigálvarez-Chamarro, M. D. L. V., González-Muñoz, C., Meléndez-Estrada, E., Lanas, Á., & Alonso, R. D. H. (2024) · Biomedicines · doi:10.3390/biomedicines12092162 · Cited by 11
Das, P., Weisenfeld, D., Dahal, K. P., De, D., Feathers, V. L., Coblyn, J. S., Weinblatt, M. E., Shadick, N. A., Cai, T., & Liao, K. P. (2023) · Arthritis Research & Therapy · doi:10.1186/s13075-023-03072-0 · Cited by 10
Artificial intelligence in autoimmune diseases: a bibliometric exploration of the past two decades
Liu, S., Liu, Y., Li, M., Shang, S., Cao, Y., Shen, X., & Huang, C. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1525462 · Cited by 9
Pharmacogenetics of Drug Therapies in Rheumatoid Arthritis
Aluko, A., & Ranganathan, P. (2022) · Methods in molecular biology · doi:10.1007/978-1-0716-2573-6_19 · Cited by 9
Ha, M. K., Bartholomeus, E., Os, L. V., Dandelooy, J., Leysen, J., Aerts, O., Siozopoulou, V., Smet, E. D., Gielen, J., Guerti, K., et al. (2022) · Pediatric Rheumatology · doi:10.1186/s12969-022-00747-x · Cited by 9
Chen, Y., Wang, Q., Liu, H., Jin, L., Feng, X., Dai, B., Chen, M., Xin, F., Wei, T., Bai, B., et al. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1173187 · Cited by 8
Nair, N., & Wilson, A. G. (2023) · Expert Review of Clinical Immunology · doi:10.1080/1744666x.2023.2193686 · Cited by 8
Novel DNA methylome biomarkers associated with adalimumab response in rheumatoid arthritis patients
Hageman, I., Mol, F., Atiqi, S., Joustra, V., Sengül, H., Henneman, P., Visman, I. M., Hakvoort, T. B. M., Nurmohamed, M. T., Wolbink, G., et al. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1303231 · Cited by 10
The differential diagnosis of IgG4-related disease based on machine learning
Yamamoto, M., Nojima, M., Kamekura, R., Souta-Kuribara, A., Uehara, M., Yamazaki, H., Yoshikawa, N., Aochi, S., Mizushima, I., Watanabe, T., et al. (2022) · Arthritis Research & Therapy · doi:10.1186/s13075-022-02752-7 · Cited by 8
Kose, A. H. P., & Mangaonkar, K. V. (2023) · Combinatorial Chemistry & High Throughput Screening · doi:10.2174/1386207326666230306114626 · Cited by 8
Can machine learning predict responses to TNF inhibitors?
Nair, N., & Wilson, A. G. (2019) · Nature Reviews Rheumatology · doi:10.1038/s41584-019-0320-9 · Cited by 8
Dara, A., Vlachogiannis, N. I., Fragoulis, G. E., Tektonidou, M. G., & Sfikakis, P. P. (2025) · Autoimmunity Reviews · doi:10.1016/j.autrev.2025.103914 · Cited by 7
Myasoedova, E. (2020) · Joint Bone Spine · doi:10.1016/j.jbspin.2020.08.001 · Cited by 7
Rehberg, M., Giegerich, C., Praestgaard, A. H., van Hoogstraten, H., Iglesias‐Rodriguez, M., Curtis, J. R., Gottenberg, J., Schwarting, A., Castañeda, S., Rubbert‐Roth, A., et al. (2021) · Rheumatology and Therapy · doi:10.1007/s40744-021-00389-7 · Cited by 7
Shim, J. V., Rehberg, M., Wagenhuber, B., van der Graaf, P. H., & Chung, D. W. (2025) · Frontiers in Pharmacology · doi:10.3389/fphar.2025.1479666 · Cited by 6
Eriakha, E., Yu, H., Li, M., Li, J., & Huang, Y. (2025) · BMC Rheumatology · doi:10.1186/s41927-025-00584-x · Cited by 6
Artificial intelligence in rheumatoid arthritis research: A bibliometric analysis from 2004 to 2023
Liu, Y., Su, Y., Wu, Z., Gao, J., Gong, X., & Zhang, L. (2024) · Rheumatology & autoimmunity · doi:10.1002/rai2.12142 · Cited by 6
Chen, S., Gupta, N., Galbraith, W. B., Shah, V., & Cirrone, J. (2022) · International Conference on Biomedical and Bioinformatics Engineering (ICBBE) · doi:10.1145/3574198.3574221 · Cited by 6
An Efficient CNN for Hand X-Ray Classification of Rheumatoid Arthritis
Subhash, M. G., & Kureshi, A. K. (2023) · Microprocessors and Microsystems · doi:10.1016/j.micpro.2023.104822 · Cited by 6
Curtis, J. R., Yun, H., Chen, L., Ford, S. S., van Hoogstraten, H., Fiore, S., Ford, K., Praestgaard, A. H., Rehberg, M., & Choy, E. (2023) · Rheumatology and Therapy · doi:10.1007/s40744-023-00568-8 · Cited by 6
Demyelination After Anti-TNF Therapy: Who is at Risk?
Williams, I., & Uhlig, H. H. (2020) · Journal of Crohn s and Colitis · doi:10.1093/ecco-jcc/jjaa144 · Cited by 6
Baloun, J., Cerezo, L. A., Kropáčková, T., Prokopcova, A., Maresova, K. B., Mann, H. F., Vencovský, J., Pavelká, K., & Šenolt, L. (2025) · Scientific Reports · doi:10.1038/s41598-025-18298-y · Cited by 6
White, I. R., Kleinstein, S. E., Praet, C. V., Chamberlain, C., McHale, D., Maia, J. M., Xie, P., Goldstein, D. B., Urban, T. J., & Shea, P. R. (2022) · PLoS ONE · doi:10.1371/journal.pone.0261165 · Cited by 5
Krasselt, M., Gruz, N., Pierer, M., Baerwald, C., & Wagner, U. G. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm12061003 · Cited by 5
Kurisu, K., Tsurutani, Y., Inoue, K., Hoshino, Y., Saiki, F., & Yoshiuchi, K. (2020) · Journal of Diabetes Investigation · doi:10.1111/jdi.13210 · Cited by 4
Hernandez, E. J. M., Tingzon, I., Ampil, L., & Tiu, J. (2021) · Journal of Medical Economics · doi:10.1080/13696998.2021.1999132 · Cited by 4
Predicting abatacept retention using machine learning
Alten, R., Behar, C., Merckaert, P., Afari, E. K., Vannier-Moreau, V., Ohayon, A., Connolly, S., Najm, A., Juge, P., Liu, G., et al. (2025) · Arthritis Research & Therapy · doi:10.1186/s13075-025-03484-0 · Cited by 4
Fahmi, A., MacBrayne, A., Humby, F., Curzon, P., & Marsh, W. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.109909 · Cited by 3
What will be the job of the rheumatologist in 2030?
Krusche, M., Mücke, J., & Burmester, G. R. (2020) · Joint Bone Spine · doi:10.1016/j.jbspin.2020.03.016 · Cited by 3
Künstliche Intelligenz-unterstützte Behandlung in der Rheumatologie
Hügle, T., & Kalweit, G. (2021) · Zeitschrift für Rheumatologie · doi:10.1007/s00393-021-01096-y · Cited by 10
Pena-Gralle, A. P. B., Schnitzer, M. E., Boureguaa, S.-N., Morin, F., Legault, M., Sirois, C., Dragomir, A., & Blais, L. (2025) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2025.103312 · Cited by 2
Gupta, D., Gupta, D., Bashir, U., Nnap, L. M., Rawat, A., Dolphin, J., Sultana, R., Cai, L. Y., Imam, B., Devkota, R. R., et al. (2025) · Cureus · doi:10.7759/cureus.99108 · Cited by 3
Ju, S., Hudson, M., Colmegna, I., Bernatsky, S., & Li, Y. (2021) · Annals of the Rheumatic Diseases · doi:10.1136/annrheumdis-2021-eular.1107 · Cited by 2
A New Hybrid Artificial Intelligence Model for Diseases Identification
Maiti, A., Abarda, A., & Hanini, M. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-26852-6_76 · Cited by 2
Identification of new hub- ferroptosis-related genes in Lupus Nephritis
Zheng, X., Chen, Y., Li, Y., Li, X.-L., Sun, D., & Li, Y.-Q. (2024) · Autoimmunity · doi:10.1080/08916934.2024.2319204 · Cited by 3
Messelink, M. A., Fadaei, S., Verhoef, L. M., Welsing, P. M. J., Nijhof, N., & Westland, H. (2024) · Rheumatology · doi:10.1093/rheumatology/keae202 · Cited by 2
Novel approaches to develop biomarkers predicting treatment responses to TNF-blockers
Mezghiche, I., Yahia‐Cherbal, H., Rogge, L., & Bianchi, E. (2021) · Expert Review of Clinical Immunology · doi:10.1080/1744666x.2021.1894926 · Cited by 2
Metadata
- Authors
- Guan, Y., Zhang, H., Quang, D., Wang, Z., Parker, S. C. J., Pappas, D. A., Kremer, J. M., & Zhu, F.
- Affiliations
- University of Michigan (US)
- Columbia University Irving Medical Center (US)
- Center for Rheumatology (US)
- Chinese Academy of Sciences (CN)
- Corresponding author
- Guan, Yuanfang
- ORCID
- Venue
- Arthritis & Rheumatology
- Publisher
- Wiley
- Published
- 2019-11-04
- Volume
- 71
- Issue
- 12
- Pages
- 1987-1996
- ISSN
- 2326-5191 (ISSN-L)
- 2326-5205
- OpenAlex
- W2964201896
- Cited by
- 137
- References
- 59
- Citations by year
- 2026: 8 · 2025: 20 · 2024: 18 · 2023: 20 · 2022: 23 · 2021: 36 · 2020: 11 · 2019: 1
- FWCI
- 13.47
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Arthritis & Rheumatology (publishedVersion)
- PubMed (publishedVersion)
- Deep Blue (University of Michigan) (submittedVersion)
- Funders
- National Natural Science Foundation of China (61802360)
- Topics
- Health Sciences › Medicine › Rheumatology › Rheumatoid Arthritis Research and Therapies
- Health Sciences › Medicine › Hematology › Autoimmune and Inflammatory Disorders Research
- Health Sciences › Medicine › Complementary and alternative medicine › Natural Compounds in Disease Treatment
- Keywords
- rheumatoid arthritis, treatment response prediction, DAS28, Gaussian process regression, single nucleotide polymorphisms, genetic biomarkers, clinical biomarkers, personalized treatment selection
- MeSH terms
- Certolizumab Pegol, Etanercept, Adalimumab, Infliximab, Machine Learning, Arthritis, Rheumatoid, Female, Genetic Markers, Humans, Male, Methotrexate, Middle Aged, Predictive Value of Tests, Regression Analysis, Severity of Illness Index, Tumor Necrosis Factor-alpha, Reproducibility of Results, Normal Distribution, Treatment Outcome, Outcome Assessment, Health Care, Antirheumatic Agents, Polymorphism, Single Nucleotide
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
