Early Prediction of Clinical Response to Etanercept Treatment in Juvenile Idiopathic Arthritis Using Machine Learning.

ABSTRACT:

Background and aims

At 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 methods

EMR 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 (

SUBMITTER: Mo X 

PROVIDER: S-EPMC7411125 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature