Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.
Sci Rep. 2020 Nov 20;10(1):20299. doi: 10.1038/s41598-020-75352-7.
We aim to generate an artificial neural network (ANN) model to predict early TNF inhibitor users in patients with ankylosing spondylitis. The baseline demographic and laboratory data of patients who visited Samsung Medical Center rheumatology clinic from Dec. 2003 to Sep. 2018 were analyzed. Patients were divided into two groups: early-TNF and non-early-TNF users. Machine learning models were formulated to predict the early-TNF users using the baseline data. Feature importance analysis was performed to delineate significant baseline characteristics. The numbers of early-TNF and non-early-TNF users were 90 and 505, respectively. The performance of the ANN model, based on the area under curve (AUC) for a receiver operating characteristic curve (ROC) of 0.783, was superior to logistic regression, support vector machine, random forest, and XGBoost models (for an ROC curve of 0.719, 0.699, 0.761, and 0.713, respectively) in predicting early-TNF users. Feature importance analysis revealed CRP and ESR as the top significant baseline characteristics for predicting early-TNF users. Our model displayed superior performance in predicting early-TNF users compared with logistic regression and other machine learning models. Machine learning can be a vital tool in predicting treatment response in various rheumatologic diseases.
我们旨在生成一个人工神经网络（ANN）模型，以预测强直性脊柱炎患者中早期使用 TNF 抑制剂的患者。分析了 2003 年 12 月至 2018 年 9 月期间在三星医疗中心风湿病诊所就诊的患者的基线人口统计学和实验室数据。患者分为两组：早期-TNF 和非早期-TNF 用户。使用基线数据制定了机器学习模型来预测早期-TNF 用户。进行特征重要性分析以描绘重要的基线特征。早期-TNF 和非早期-TNF 用户的数量分别为 90 和 505。基于接受者操作特征曲线（ROC）的曲线下面积（AUC）为 0.783 的 ANN 模型的性能优于逻辑回归、支持向量机、随机森林和 XGBoost 模型（ROC 曲线分别为 0.719、0.699、0.761 和 0.713），用于预测早期-TNF 用户。特征重要性分析表明 CRP 和 ESR 是预测早期-TNF 用户的最重要的基线特征。与逻辑回归和其他机器学习模型相比，我们的模型在预测早期-TNF 用户方面表现出更好的性能。机器学习可以成为预测各种风湿病治疗反应的重要工具。
