[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123956-en":3,"doc-seo-123956-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123956,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Machine Learning Prediction of Treatment Response to Biological Disease-Modifying Antirheumatic Drugs in Rheumatoid Arthritis","Disease-modifying antirheumatic drugs (bDMARDs) show benefit in rheumatoid arthritis (RA), yet around 30% of patients do not respond and only about half reach sustained response. This study develops machine learning models to predict initial response at 6 months and sustained response at 12 months using baseline clinical data from 154 RA patients. Five algorithms are compared with nested crossvalidation and hyperparameter tuning. XGBoost leads for initial response prediction (AUC-ROC 0.91) and AdaBoost for sustained response (AUC-ROC 0.84). DAS28-ESR and SHAP explain key predictors and directionality, supporting treatment planning before medication.","Journal of  \nClinical Medicine  \nArticle  \nMachine Learning Prediction of Treatment Response to Biological Disease-Modifying Antirheumatic Drugs in Rheumatoid Arthritis  \nFatemeh Salehi 1,*, Luis I. Lopera Gonzalez 2, Sara Bayat 3,4, Arnd Kleyer 5, Dario Zanca 1, Alexander Brost 6, Georg Schett 3,4 and Bjoern M. Eskofier 1,7  \nCitation: Salehi, F.; Lopera Gonzalez, L.I.; Bayat, S.; Kleyer, A.; Zanca, D.; Brost, A.; Schett, G.; Eskofier, B.M. Machine Learning Prediction of Treatment Response to Biological Disease-Modifying Antirheumatic Drugs in Rheumatoid Arthritis. J. Clin. Med. 2024, 13, 3890. [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.3390/jcm13133890](10.3390/jcm13133890)  \nAcademic Editor: Chang-Hee Suh  \nReceived: 31 May 2024  \nRevised: 25 June 2024  \nAccepted: 27 June 2024  \nPublished: 2 July 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Machine Learning and Data Analytics Laboratory, Department Artificial Intelligence in Biomedical Engineering, Friedrich Alexander University Erlangen-Nuremberg, 91052 Erlangen, Germany; [dario.zanca@fau.de](dario.zanca@fau.de) (D.Z.); [bjoern.eskofier@fau.de](bjoern.eskofier@fau.de) (B.M.E.)  \n2 Instutue of Digital Health, Friedrich Alexander University Erlangen-Nuremberg, 91052 Erlangen, Germany; [luis.i.lopera@fau.de](luis.i.lopera@fau.de)  \n3 Department of Internal Medicine 3, Rheumatology and Immunology, University Hospital Erlangen,  \n91054 Erlangen, Germany; [sara.bayat@uk-erlangen.de](sara.bayat@uk-erlangen.de) (S.B.); [georg.schett@uk-erlangen.de](georg.schett@uk-erlangen.de) (G.S.)  \n4 Deutsches Zentrum Immuntherapie (DZI), 91054 Erlangen, Germany  \n5 Department of Rheumatology and Clinical Immunology, Charité—University Medicine Berlin,  \n10117 Berlin, Germany; [arnd.kleyer@extern.uk-erlangen.de](arnd.kleyer@extern.uk-erlangen.de)  \n6 Siemens Healthcare GmbH, 91301 Forchheim, Germany; [alexander.brost@siemens-healthineers.com](alexander.brost@siemens-healthineers.com)  \n7 Translational Digital Health Group, Institute of AI for Health, Helmholtz Center Munich—German Research Center for Environmental Health, 85764 Neuherberg, Germany  \n* Correspondence: [fatemeh.salehihafshejani@fau.de](fatemeh.salehihafshejani@fau.de)  \nAbstract: Background: Disease-modifying antirheumatic drugs (bDMARDs) have shown efficacy in treating Rheumatoid Arthritis (RA). Predicting treatment outcomes for RA is crucial as approximately 30% of patients do not respond to bDMARDs and only half achieve a sustained response. This study aims to leverage machine learning to predict both initial response at 6 months and sustained response at 12 months using baseline clinical data. Methods: Baseline clinical data were collected from 154 RA patients treated at the University Hospital in Erlangen, Germany. Five machine learning models were compared: Extreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), K-nearest neighbors (KNN), Support Vector Machines (SVM), and Random Forest. Nested crossvalidation was employed to ensure robustness and avoid overfitting, integrating hyperparameter tuning within its process. Results: XGBoost achieved the highest accuracy for predicting initial response (AUC-ROC of 0.91), while AdaBoost was the most effective for sustained response (AUCROC of 0.84) . Key predictors included the Disease Activity Score-28 using erythrocyte sedimentation rate (DAS28-ESR), with higher scores at baseline associated with lower response chances at 6 and 12 months. Shapley additive explanations (SHAP) identified the most important baseline features and visualized their directional effects on treatment response and sustained response. Conclusions: These findi","cbCait12eg7RNg5I","https://ap.wps.com/l/cbCait12eg7RNg5I","pdf",3332683,1,16,"English","en",105,"# Abstract\n# Introduction\n# Methods\n## Models and validation\n# Results\n## Predictive performance\n## Feature importance and explanations\n# Conclusions","[{\"question\":\"What outcomes does the study aim to predict in rheumatoid arthritis patients?\",\"answer\":\"It predicts both the initial treatment response at 6 months and the sustained response at 12 months using baseline clinical information.\"},{\"question\":\"Which machine learning models were compared in the analysis?\",\"answer\":\"Extreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), K-nearest neighbors (KNN), Support Vector Machines (SVM), and Random Forest were evaluated.\"},{\"question\":\"What baseline predictors were identified as important for treatment response?\",\"answer\":\"The Disease Activity Score-28 with ESR (DAS28-ESR) emerged as a key predictor, where higher baseline scores were associated with lower response chances at both 6 and 12 months, supported by SHAP-based interpretation.\"}]","Machine Learning Prediction of Treatment Response to Biological Disease-Modifying Antirheumatic Drugs in Rheumatoid Arthritis | 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