Early and Accurate Prediction of Clinical Response to Methotrexate Treatment in Juvenile Idiopathic Arthritis Using Machine Learning

Early and Accurate Prediction of Clinical Response to Methotrexate Treatment in Juvenile Idiopathic Arthritis Using Machine Learning

Background and Aims: Accurately predicting the response to methotrexate (MTX) in juvenile idiopathic arthritis (JIA) patients before administration is the key point to improve the treatment outcome. However, no simple and reliable prediction model has been identified. Here, we aimed to develop and v...

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Bibliographic Details
Main Authors: Xiaolan Mo, Xiujuan Chen, Hongwei Li, Jiali Li, Fangling Zeng, Yilu Chen, Fan He, Song Zhang, Huixian Li, Liyan Pan, Ping Zeng, Ying Xie, Huiyi Li, Min Huang, Yanling He, Huiying Liang, Huasong Zeng
Format: Article
Language:English
Published: Frontiers Media S.A. 2019-10-01
Series:Frontiers in Pharmacology
Subjects:
methotrexate
juvenile idiopathic arthritis
prediction model
machine learning
clinical response
Online Access:https://www.frontiersin.org/article/10.3389/fphar.2019.01155/full

Internet

https://www.frontiersin.org/article/10.3389/fphar.2019.01155/full

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