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Updated: Jun 6, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Operationalization of the updated diagnostic algorithm for classifying HIV-related cognitive impairment and dementia
J M Foley1, M J Wright, A L Gooding
1UCLA Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and Biobehavioral Sciences, Los Angeles, California, U.S.A.
Background:
This study applies the updated HIV-Associated Neurocognitive Disorders (HAND) diagnostic algorithm.
Methods:
Participants were 210 HIV-infected-adults, classified using proposed HAND criteria: HIV-Associated Dementia (HAD), Mild Neurocognitive Disorder (MND), Asymptomatic Neurocognitive Impairment (ANI).
Results:
The algorithm yielded: normal = 32.8%, ANI = 21.4%, MND = 34.3%, and HAD = 11.4%. Normal participants performed superior to HAND-defined participants on cognition, and HAD participants performed more poorly on global cognition and executive functioning. Two distinct subgroups of interest emerged: (1) functional decline without cognitive impairment; (2) severe cognitive impairment and minimal functional compromise.
Conclusions:
The algorithm discriminates between HIV-infected cognitively impaired individuals. Diagnosis yields two unique profiles requiring further investigation. Findings largely support the algorithm's utility for diagnosing HIV-cognitive-impairment, but suggest distinct subsets of individuals with discrepant cognitive/functional performances that may not be readily apparent by conventional application of HAND diagnosis.
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