Clinical Interpretability
Cross-source consensus on Clinical Interpretability from 1 sources and 5 claims.
1 sources · 5 claims
Uses
How it works
Benefits
Highlighted claims
- Anatomically informed whitening improved interpretation of linear neuroimaging classifiers without materially changing predictive accuracy. — Improving clinical interpretability of linear neuroimaging models through feature whitening
- Projecting coefficients back to original feature space preserved clinically recognizable ROI terms. — Improving clinical interpretability of linear neuroimaging models through feature whitening
- Pairwise whitening reduced left-right homologous coupling and GM-CSF inverse relationships that complicate coefficient interpretation. — Improving clinical interpretability of linear neuroimaging models through feature whitening
- The approach addressed interpretability rather than predictive performance. — Improving clinical interpretability of linear neuroimaging models through feature whitening
- After whitening, region-importance rankings were interpreted as more consistent with psychiatric neuroimaging literature. — Improving clinical interpretability of linear neuroimaging models through feature whitening