Feature Engineering for the Prediction of Scoliosis in 5q-Spinal Muscular Atrophy.

ABSTRACT:

Background

5q-Spinal muscular atrophy (SMA) is now one of the 5% treatable rare diseases worldwide. As disease-modifying therapies alter disease progression and patient phenotypes, paediatricians and consulting disciplines face new unknowns in their treatment decisions. Conclusions made from historical patient data sets are now mostly limited, and new approaches are needed to ensure our continued best standard-of-care practices for this exceptional patient group. Here, we present a data-driven machine learning approach to a rare disease data set to predict spinal muscular atrophy (SMA)-associated scoliosis.

Methods

We collected data from 84 genetically confirmed 5q-SMA patients who have received novel SMA therapies. We performed expert domain knowledge-directed feature engin

SUBMITTER: Vu-Han TL 

PROVIDER: S-EPMC11670177 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature