SGC-RML creates an 8D symptom atlas from multimodal PD data and integrates conformal calibration to deliver reliable, rejectable longitudinal assessments.
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For 5-item subsets of the MDS-UPDRS, coordinate descent item selection cuts expected standard deviation of severity estimates by 26% and adaptive selection by 34% versus random choice, outperforming Fisher-information ranking by 12 percentage points.
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SGC-RML: A reliable and interpretable longitudinal assessment for PD in real-world DNS
SGC-RML creates an 8D symptom atlas from multimodal PD data and integrates conformal calibration to deliver reliable, rejectable longitudinal assessments.
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Optimized questionnaire item selection for tracking the progression of motor symptoms in Parkinson's disease
For 5-item subsets of the MDS-UPDRS, coordinate descent item selection cuts expected standard deviation of severity estimates by 26% and adaptive selection by 34% versus random choice, outperforming Fisher-information ranking by 12 percentage points.