Machine learning for screening and predicting the risk of anti-MDA5 antibody in juvenile dermatomyositis children.

ABSTRACT:

Objective

The anti-MDA5 (anti-melanoma differentiation associated gene 5) antibody is often associated with a poor prognosis in juvenile dermatomyositis (JDM) patients. In many developing countries, there is limited ability to access myositis- specific antibodies due to financial and technological issues, especially in remote regions. This study was performed to develop a prediction model for screening anti-MDA5 antibodies in JDM patients with commonly available clinical findings.

Methods

A cross-sectional study was undertaken with 152 patients enrolled from the inpatient wards of Beijing Children's Hospital between June 2018 and September 2021. Stepwise logistic regression, least absolute shrinkage and selection operator (LASSO) regression, and the random forest (RF) method

SUBMITTER: Xue Y 

PROVIDER: S-EPMC9872019 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature