Machine learning-based remission prediction in rheumatoid arthritis patients treated with biologic disease-modifying anti-rheumatic drugs: findings from the Kuwait rheumatic disease registry.

IF 2.4 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Frontiers in Big Data Pub Date : 2024-10-03 eCollection Date: 2024-01-01 DOI:10.3389/fdata.2024.1406365
Ahmad R Alsaber, Adeeba Al-Herz, Balqees Alawadhi, Iyad Abu Doush, Parul Setiya, Ahmad T Al-Sultan, Khulood Saleh, Adel Al-Awadhi, Eman Hasan, Waleed Al-Kandari, Khalid Mokaddem, Aqeel A Ghanem, Yousef Attia, Mohammed Hussain, Naser AlHadhood, Yaser Ali, Hoda Tarakmeh, Ghaydaa Aldabie, Amjad AlKadi, Hebah Alhajeri
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Abstract

Background: Rheumatoid arthritis (RA) is a common condition treated with biological disease-modifying anti-rheumatic medicines (bDMARDs). However, many patients exhibit resistance, necessitating the use of machine learning models to predict remissions in patients treated with bDMARDs, thereby reducing healthcare costs and minimizing negative effects.

Objective: The study aims to develop machine learning models using data from the Kuwait Registry for Rheumatic Diseases (KRRD) to identify clinical characteristics predictive of remission in RA patients treated with biologics.

Methods: The study collected follow-up data from 1,968 patients treated with bDMARDs from four public hospitals in Kuwait from 2013 to 2022. Machine learning techniques like lasso, ridge, support vector machine, random forest, XGBoost, and Shapley additive explanation were used to predict remission at a 1-year follow-up.

Results: The study used the Shapley plot in explainable Artificial Intelligence (XAI) to analyze the effects of predictors on remission prognosis across different types of bDMARDs. Top clinical features were identified for patients treated with bDMARDs, each associated with specific mean SHAP values. The findings highlight the importance of clinical assessments and specific treatments in shaping treatment outcomes.

Conclusion: The proposed machine learning model system effectively identifies clinical features predicting remission in bDMARDs, potentially improving treatment efficacy in rheumatoid arthritis patients.

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基于机器学习的类风湿关节炎患者缓解预测:科威特风湿病登记处的研究结果。
背景:类风湿性关节炎(RA)是一种使用生物改良抗风湿药物(bDMARDs)治疗的常见疾病。然而,许多患者表现出抗药性,因此有必要使用机器学习模型来预测接受生物改良抗风湿药治疗的患者的病情缓解情况,从而降低医疗成本并将负面影响降至最低:该研究旨在利用科威特风湿病登记处(KRRD)的数据开发机器学习模型,以确定可预测接受生物制剂治疗的RA患者病情缓解的临床特征:该研究收集了2013年至2022年期间科威特四家公立医院1968名接受bDMARDs治疗的患者的随访数据。研究采用了拉索、脊、支持向量机、随机森林、XGBoost 和 Shapley 加性解释等机器学习技术来预测随访 1 年后的缓解情况:该研究利用可解释人工智能(XAI)中的夏普利图谱分析了不同类型 bDMARDs 的预测因素对缓解预后的影响。研究确定了接受 bDMARDs 治疗的患者的主要临床特征,每个特征都与特定的平均 SHAP 值相关。研究结果凸显了临床评估和特定治疗在影响治疗结果方面的重要性:结论:所提出的机器学习模型系统能有效识别预测bDMARDs缓解的临床特征,从而提高类风湿关节炎患者的治疗效果。
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CiteScore
5.20
自引率
3.20%
发文量
122
审稿时长
13 weeks
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