This paper is central to the computational phenotyping arc because it focuses specifically on women with HIV, a group often underrepresented in mixed-sex neuroHIV analyses. It shows that viral suppression does not eliminate cognitive heterogeneity, and that women’s cognitive profiles can be organized into interpretable patterns rather than treated as a single impairment category. The work also demonstrates how machine learning can surface modifiable or clinically meaningful correlates of profile membership.
- The study analyzed neuropsychological data from 929 virally suppressed women with HIV and 717 women without HIV from the Women’s Interagency HIV Study.
- Seventeen neuropsychological performance metrics were used to identify high-dimensional cognitive patterns.
- Among virally suppressed women with HIV, the analysis identified an unimpaired group plus profiles involving sequencing, processing speed, learning and recognition, and learning and memory weaknesses.
- Random forest models were used to identify sociodemographic, behavioral, clinical, and female-specific factors associated with cognitive profile membership.
- The findings emphasize profile heterogeneity and point toward potentially modifiable factors that may contribute to impaired cognitive patterns.
This study looked for patterns in cognitive test performance among women with HIV whose virus was suppressed. Instead of asking only whether cognition was impaired, the analysis identified different kinds of cognitive profiles. That makes the findings more useful for understanding why cognitive difficulties vary across individuals and which risk factors may matter for each pattern.