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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
The impact of criteria selection on prevalence rates
1Department of Psychiatry and Behavioral Science, University of Western Australia.
Abstract:
In spite of major advances in the past 10 years in the development of standardized instruments for reliable psychiatric case detection, many selection factors can influence prevalence rates, impairing the compilation of comparable epidemiological data from different settings. This is especially so in the aged. Such factors include relatively small variations in the wording of questionnaires, the sophistication and training of interviewers, availability of an informant history, duration of symptoms and diagnostic hierarchies. Many different measuring instruments and diagnostic criteria are currently used to diagnose depressive illness in the elderly. It is not clear how these compare in estimating prevalence rates. There are no agreed reliable criteria for dementia, especially mild dementia: different criteria systems give widely differing prevalence rates. Gender, education and social class can influence the scores on some tests of cognition. More uniform generally accepted measures and criteria of major psychiatric syndromes in the elderly, must be developed if epidemiological approaches are to contribute to further major advances in our understanding of psychiatric syndromes in the aged.
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