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Updated: Nov 19, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Longitudinal 5-year prediction of cognitive impairment among men with HIV disease
Natalia L Oliveira1,2, Edward H Kennedy1, Ryan Tibshirani1,2
1Department of Statistics and Data Science.
Background:
Although combination antiretroviral therapy reduced the prevalence of HIV-associated dementia, milder syndromes persist. Our goals were to predict cognitive impairment of the Multicenter AIDS Cohort Study (MACS) participants 5 years ahead and from a large pool of factors, select the ones that mostly contributed to our predictions.
Design:
Longitudinal, natural and treated history of HIV infection among MSM.
Methods:
The MACS is a longitudinal study of the natural and treated history of HIV disease in MSM; the neuropsychological substudy aims to characterize cognitive disorders in men with HIV disease.
Results:
We modeled on an annual basis the risk of cognitive impairment 5 years in the future. We were able to predict cognitive impairment at individual level with high precision and overperform default methods. We found that while a diagnosis of AIDS is a critical risk factor, HIV infection per se does not necessarily convey additional risk. Other infectious processes, most notably hepatitis B and C, are independently associated with increased risk of impairment. The relative importance of an AIDS diagnosis diminished across calendar time.
Conclusion:
Our prediction models are a powerful tool to help clinicians address dementia in early stages for MACS paticipants. The strongest predictors of future cognitive impairment included the presence of clinical AIDS and hepatitis B or C infection. The fact that the pattern of predictive power differs by calendar year suggests a clinically critical change to the face of the epidemic.
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