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Updated: Jan 3, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Constructing classification boundaries in the memory clinic: negotiating risk and uncertainty in constituting mild
1School of Sociology and Social Policy, University of Leeds, Leeds, UK.
Abstract:
Determining the boundaries around processes of 'normal' ageing and pathological cognitive deterioration associated with Alzheimer's disease (AD) is a difficult process, complicated further by the expansion of the disease category to include mild cognitive impairment (MCI). MCI is a label used to identify individuals with the symptoms of cognitive deterioration not attributable to 'normal ageing' but deemed to be 'at risk' of developing AD despite clinical uncertainty around whether individuals will go on to develop the condition in the future. Drawing on qualitative data gathered across an out-patient memory service, this article examines practitioners' accounts of the complexity associated with constructing the boundaries around MCI, AD and age in the clinic. Practitioners utilise uncertainty by classifying patients with MCI to keep them on for review to account for the possibility that patients may go on to develop AD but they also recognise the difficulty in predicting future progression to AD. Negotiating classification boundaries in the clinic is, however, not only about managing uncertainty regarding potential future risk but also about navigating the wider social and political context in which ageing and cognitive deterioration intersect, and are constructed and managed.
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