1. Screening for Mild Cognitive Impairment Using a Machine Learning Classifier and the Remote Speech Biomarker for Cognition
- Author
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Simona, Schäfer, Elisa, Mallick, Louisa, Schwed, Alexandra, König, Jian, Zhao, Nicklas, Linz, Timothy Hadarsson, Bodin, Johan, Skoog, Nina, Possemis, Daphne, Ter Huurne, Anna, Zettergren, Silke, Kern, Simona, Sacuiu, Inez, Ramakers, Ingmar, Skoog, Johannes, Tröger, RS: MHeNs - R3 - Neuroscience, Basic Neuroscience 2, RS: MHeNs - R1 - Cognitive Neuropsychiatry and Clinical Neuroscience, Psychology 1, and Psychology 2
- Subjects
Psychiatry and Mental health ,Clinical Psychology ,General Neuroscience ,General Medicine ,Geriatrics and Gerontology - Abstract
BACKGROUND: Modern prodromal Alzheimer's disease (AD) clinical trials might extend outreach to a general population, causing high screen-out rates and thereby increasing study time and costs. Thus, screening tools that cost-effectively detect mild cognitive impairment (MCI) at scale are needed.OBJECTIVE: Develop a screening algorithm that can differentiate between healthy and MCI participants in different clinically relevant populations.METHODS: Two screening algorithms based on the remote ki:e speech biomarker for cognition (ki:e SB-C) were designed on a Dutch memory clinic cohort (N = 121) and a Swedish birth cohort (N = 404). MCI classification was each evaluated on the training cohort as well as across on the unrelated validation cohort.RESULTS: The algorithms achieved a performance of AUC 0.73 and AUC 0.77 in the respective training cohorts and AUC 0.81 in the unseen validation cohort.CONCLUSION: The results indicate that a ki:e SB-C based algorithm robustly detects MCI across different cohorts and languages, which has the potential to make current trials more efficient and improve future primary health care.
- Published
- 2023
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