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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Detecting early stages of Alzheimer's disease using a web-based cognitive battery
Jordan R Hoffmeister1, Brady R Robison2, Christopher T Copeland2
1Department of Psychiatry and Behavioral Sciences, The University of Oklahoma Health Science Center, Oklahoma City, OK, USA. jordanhoffmeister@gmail.com.
None:
Portable and efficient cognitive screening measures are needed to address the growing need for effective early detection of Alzheimer's disease. The Neurotrack Cognitive Battery (NCB) offers an appealing, web-based application that may be sensitive to early cognitive changes associated with Alzheimer's disease. The NCB contains measures that were conceptually derived from animal lesion studies. The current study sought to investigate the construct and diagnostic validity of the NCB among those with and without mild cognitive impairment (MCI). Participants (n = 47) with and without MCI were administered the NCB and traditional cognitive tests. Three of six NCB measures assessing domains of memory, processing speed, and executive functioning demonstrated moderate to strong associations with well-established cognitive performance tests. In classifying those with and without MCI, sensitivities of these three NCB measures ranged from 0.47 to 0.74, and specificities ranged from 0.78 to 1.00. For traditional cognitive measures, sensitivities ranged from 0.74 to 0.84, and specificities ranged from 0.74 to 0.94. Overall, web-based cognitive test measures pertaining to cognitive domains of memory, processing speed, and executive functioning may serve as highly portable screening tools for detecting the early stages of Alzheimer's disease. Additionally, these cognitive domains may be valuable in informing back-translational research.
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