Machine Learning Approaches to Understand Cognitive Phenotypes in People With HIV

Shibani S. Mukerji, Kalen J. Petersen, Kilian M. Pohl, Raha M. Dastgheyb, Howard S. Fox, Robert M. Bilder, Marie Josee Brouillette, Alden L. Gross, Lori A.J. Scott-Sheldon, Robert H. Paul, Dana Gabuzda

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

Cognitive disorders are prevalent in people with HIV (PWH) despite antiretroviral therapy. Given the heterogeneity of cognitive disorders in PWH in the current era and evidence that these disorders have different etiologies and risk factors, scientific rationale is growing for using data-driven models to identify biologically defined subtypes (biotypes) of these disorders. Here, we discuss the state of science using machine learning to understand cognitive phenotypes in PWH and their associated comorbidities, biological mechanisms, and risk factors. We also discuss methods, example applications, challenges, and what will be required from the field to successfully incorporate machine learning in research on cognitive disorders in PWH. These topics were discussed at the National Institute of Mental Health meeting on "Biotypes of CNS Complications in People Living with HIV"held in October 2021. These ongoing research initiatives seek to explain the heterogeneity of cognitive phenotypes in PWH and their associated biological mechanisms to facilitate clinical management and tailored interventions.

Original languageEnglish (US)
Pages (from-to)S48-S57
JournalJournal of Infectious Diseases
Volume227
DOIs
StatePublished - Mar 15 2023

Keywords

  • HIV
  • HIV-associated neurocognitive disorders
  • cognitive impairment
  • deep learning
  • machine learning

ASJC Scopus subject areas

  • General Medicine

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