Recurrence Biomarkers

Cross-source consensus on Recurrence Biomarkers from 1 sources and 5 claims.

1 sources · 5 claims

How it works

The recurrence biomarker model achieved a mean cross-validated AUC of 0.689. — Recurrence-Based Nonlinear Vocal Dynamics as Digital Biomarkers for Depression Detection from Conversational Speech
Recurrence biomarkers were the strongest-performing feature family tested in the study. — Recurrence-Based Nonlinear Vocal Dynamics as Digital Biomarkers for Depression Detection from Conversational Speech
Permutation testing found the recurrence model unlikely to perform that well under random label assignment. — Recurrence-Based Nonlinear Vocal Dynamics as Digital Biomarkers for Depression Detection from Conversational Speech
The pooled cross-validated recurrence model achieved AUC 0.665 with a 95% bootstrap confidence interval from 0.568 to 0.758. — Recurrence-Based Nonlinear Vocal Dynamics as Digital Biomarkers for Depression Detection from Conversational Speech
Recurrence rate is interpreted as a nonlinear biomarker because it quantifies how often vocal dynamics revisit similar states. — Recurrence-Based Nonlinear Vocal Dynamics as Digital Biomarkers for Depression Detection from Conversational Speech