Machine Learning Approaches to Understand Cognitive Phenotypes in People With HIV

Area of Science:

• Neuroscience
• Infectious Diseases
• Computational Biology

Background:

• Cognitive disorders remain common in people with HIV (PWH) even with antiretroviral therapy.
• The diverse nature of these disorders suggests multiple underlying causes and risk factors.
• A need exists for data-driven approaches to classify cognitive issues in PWH.

Purpose of the Study:

• To review the application of machine learning in understanding cognitive phenotypes in PWH.
• To explore associated comorbidities, biological mechanisms, and risk factors.
• To discuss the future integration of machine learning in HIV-associated cognitive disorder research.

Main Methods:

• Review of current scientific literature on machine learning and cognitive disorders in PWH.
• Discussion of methodologies for identifying biologically defined subtypes (biotypes).
• Examination of challenges and requirements for implementing machine learning in this field.

Main Results:

• Machine learning offers a promising framework for dissecting the heterogeneity of cognitive disorders in PWH.
• Identified potential for linking specific cognitive profiles to distinct biological mechanisms and risk factors.
• Highlighted the need for collaborative efforts and standardized methods.

Conclusions:

• Machine learning can facilitate the identification of distinct biotypes of cognitive disorders in PWH.
• Understanding these biotypes is crucial for developing targeted clinical management strategies.
• Further research is needed to fully integrate machine learning for personalized interventions in PWH.