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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Screening and measurement of cognitive impairment in psychiatry
1Metis Cognition Ltd.,Kilmington Common,UK; Institute of Psychiatry,Psychology & Neuroscience,King's College London,London,UK;Alzheimer Center,VU Medical Center,Amsterdam,The Netherlands.
Abstract:
A key message from the review of cognitive dysfunction in psychiatry published by Millan et al (2012) was not just that cognitive skills are often compromised in patients with psychiatric disorders, but that deficits in specific domains are common to a number of conditions. The review also highlighted that the magnitude of the observed deficits varied across disorders. A helpful element of the Millan et al study was the inclusion of a table in which the authors sought to convey the domains of cognition and a categorization of the magnitude of the observed deficits.In previous articles, we have considered best practice for the assessment of cognition. In these contributions, we have argued not for the use of specific tests, but instead for measures that meet acceptable standards of reliability, validity, and sensitivity. In the course of our discussions, we have included reference to test validity in the context of considering whether selected measures index appropriate domains of cognition. In this article, we begin with a brief discussion of the requirements for good test selection, especially with respect to issues of sensitivity, reliability, and validity. Thereafter the focus of this article is on the issue of domain validity. We will critically review the specification of the cognitive domains proposed by Millan et al, as well as those selected by authors of meta-analyses characterizing cognitive deficits in major depressive disorders. This focus is solely to make the discussion tractable, though we propose that the issues raised will be applicable across all psychiatric and neurological disorders.
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