ArticleFrontiers in pharmacology2020
Early Prediction of Clinical Response to Etanercept Treatment in Juvenile Idiopathic Arthritis Using Machine Learning.
Article in Frontiers in pharmacology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 34 citations in OpenAlex.
- Digital health tools in juvenile idiopathic arthritis: a systematic literature review.Pediatric rheumatology online journal · 2025Pooled it
- Predictors of inactive disease and remission in children and young adults with juvenile idiopathic arthritis treated with etanercept.Rheumatology (Oxford, England) · 2026Trial
- Machine Learning-Integrated Metabolomics for Precision Pharmacotherapy: Advances, Challenges, and Clinical Translation.Metabolites · 2026Review
- Machine learning model for predicting hospital-acquired functional decline in older patients with postoperative cardiovascular surgery.World journal of clinical cases · 2026Article
- Construction of risk prediction model of sentinel lymph node metastasis in breast cancer patients based on machine learning algorithm.Discover oncology · 2025Article
- Machine learning-based prediction of response to Ustekinumab with Crohn's disease.Therapeutic advances in gastroenterology · 2025Article
- The emerging paradigm in pediatric rheumatology: harnessing the power of artificial intelligence.Rheumatology international · 2024Review
- Application of Machine Learning Algorithms to Analyze the Clinical Characteristics of NIHL Caused by Impulse Noise and Steady Noise.Iranian journal of public health · 2024Article
- Artificial intelligence for predicting treatment responses in autoimmune rheumatic diseases: advancements, challenges, and future perspectives.Frontiers in immunology · 2024Review
- Development and Validation of a Prediction Model on Adult Emergency Department Patients for Early Identification of Fulminant Myocarditis.Current medical science · 2023Article
- Comparison and development of machine learning for thalidomide-induced peripheral neuropathy prediction of refractory Crohn's disease in Chinese population.World journal of gastroenterology · 2023Article
- Exploring the Intersection of Artificial Intelligence and Clinical Healthcare: A Multidisciplinary Review.Diagnostics (Basel, Switzerland) · 2023Review
- Extending artificial intelligence research in the clinical domain: a theoretical perspective.Annals of operations research · 2022Article
- Association Between Patient-Reported Outcomes and Treatment Failure in Juvenile Idiopathic Arthritis.ACR open rheumatology · 2022Article
- Development and validation of a machine learning model to predict the risk of lymph node metastasis in renal carcinoma.Frontiers in endocrinology · 2022Article
- Development of a Machine Learning-Based Predictive Model for Lung Metastasis in Patients With Ewing Sarcoma.Frontiers in medicine · 2022Article
- Complex Machine-Learning Algorithms and Multivariable Logistic Regression on Par in the Prediction of Insufficient Clinical Response to Methotrexate in Rheumatoid Arthritis.Journal of personalized medicine · 2021Article
- Machine Learning Techniques for Personalised Medicine Approaches in Immune-Mediated Chronic Inflammatory Diseases: Applications and Challenges.Frontiers in pharmacology · 2021Review
- Biomarkers of Response to Biologic Therapy in Juvenile Idiopathic Arthritis.Frontiers in pharmacology · 2020Review
Corrections and comments
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Authors and funding
15 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
aimsAt present, there is a lack of simple and reliable model for early prediction of the efficacy of etanercept in the treatment of juvenile idiopathic arthritis (JIA). This study aimed to generate and validate prediction models of etanercept efficacy in patients with JIA before administration using machine learning algorithms based on electronic medical record (EMR). MATERIALS AND
methodsEMR data of 87 JIA patients treated with etanercept between January 2011 and December 2018 were collected retrospectively. The response of etanercept was evaluated by using DAS44/ESR-3 simplified standard. The stepwise forward and backward method based on information gain was applied to select features. Five machine learning algorithms, including Extreme Gradient Boosting (XGBoost), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Extremely Random Trees (ET) and Logistic Regression (LR) were used for model generation and validation with fifty-fold stratified cross-validation. EMR data of additional 14 patients were collected for external validation of the model.
resultsTender joint count (TJC), Time interval, Lymphocyte percentage (LYM), and Weight were screened out and included in the final model. The model generated by the XGBoost algorithm based on the above 4 features had the best predictive performance: sensitivity 75%, specificity 66.67%, accuracy 72.22%, AUC 79.17%, respectively.
conclusionA pre-administration model with good prediction performance for etanercept response in JIA was developed using advanced machine learning algorithms. Clinicians and pharmacists can use this simple and accurate model to predict etanercept response of JIA early and avoid treatment failure or adverse effects.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.