Machine Learning, After Fine-tuning, May Be Useful for SLE Classification, Study Suggests
Lupus News Today
By Marisa Wexler, MS
July 12, 2019
Machine learning analysis strategies can help predict disease status in people with systemic lupus erythematosus (SLE), a study shows. Still, technical variability inherent to each clinical analysis method can represent a roadblock in this process.
Fine-tuning of machine learning algorithms and parameter sets may help reduced technical “noise” caused by such variability, the researchers suggest. They said this would generate “sufficient accuracy to be informative as a standalone estimate of disease activity.”
Titled “Machine learning approaches to predict lupus disease activity from gene expression data,” the study was published in Nature Scientific Reports.
Diagnosing and classifying SLE is a challenge for clinicians because the disease is so varied in how it presents. One proposed way to classify this type of lupus is based on gene expression levels, which evaluate which genes are “turned on” and by how much.