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Updated: May 6, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Cognitive diagnostic assessment via Bayesian evaluation of informative diagnostic hypotheses
Herbert Hoijtink1, Sébastien Béland2, Jorine A Vermeulen3
1CITO Institute for Educational Measurement.
Abstract:
There exist diverse approaches that can be used for cognitive diagnostic assessment, such as mastery testing, constrained latent class analysis, rule space methodology, diagnostic cognitive modeling, and person-fit analysis. Each of these approaches can be used within 1 of the 4 psychometric perspectives on diagnostic testing discussed by Borsboom (2008), that is, the dimensional, diagnostic, constructivist, and causal system perspectives. Bayesian evaluation of informative diagnostic hypotheses is an alternative for each of the other approaches that is more flexible in the diagnostic hypotheses that can be evaluated, and it can be used in each of the 4 psychometric perspectives on diagnostic testing. After being formulated, informative diagnostic hypotheses are evaluated by means of the Bayes factor using only the data from the person to be diagnosed. Already, relatively small diagnostic tests render Bayes factors that provide convincing evidence in favor of 1 of the diagnostic hypotheses under consideration.
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