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Updated: Mar 7, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Data quality assurance and control in cognitive research: Lessons learned from the PREDICT-HD study
Holly James Westervelt1,2, Rachel A Bernier2,3, Melanie Faust2,4,5
1Department of Neurology, Vanderbilt Medical Center, Nashville, TN, USA.
Abstract:
We discuss the strategies employed in data quality control and quality assurance for the cognitive core of Neurobiological Predictors of Huntington's Disease (PREDICT-HD), a long-term observational study of over 1,000 participants with prodromal Huntington disease. In particular, we provide details regarding the training and continual evaluation of cognitive examiners, methods for error corrections, and strategies to minimize errors in the data. We present five important lessons learned to help other researchers avoid certain assumptions that could potentially lead to inaccuracies in their cognitive data.
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