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-Josée Brouillette; Alden L Gross; Lori A J Scott-Sheldon; Robert H Paul; Dana Gabuzda

doi:10.1093/infdis/jiac293

Machine Learning Approaches to Understand Cognitive Phenotypes in People With HIV

J Infect Dis. 2023 Mar 17;227(Suppl 1):S48-S57. doi: 10.1093/infdis/jiac293.

Authors

Shibani S Mukerji^{1

2

3}, Kalen J Petersen⁴, Kilian M Pohl^{5

6}, Raha M Dastgheyb⁷, Howard S Fox⁸, Robert M Bilder⁹, Marie-Josée Brouillette¹⁰, Alden L Gross¹¹, Lori A J Scott-Sheldon¹², Robert H Paul¹³, Dana Gabuzda^{2

3}

Affiliations

¹ Massachusetts General Hospital, Boston, Massachusetts, USA.
² Dana-Farber Cancer Institute, Boston, Massachusetts, USA.
³ Harvard Medical School, Boston, Massachusetts, USA.
⁴ Washington University in Saint Louis, Saint Louis, Missouri, USA.
⁵ Stanford University, Stanford, California, USA.
⁶ SRI International, Menlo Park, California, USA.
⁷ Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
⁸ University of Nebraska Medical Center, Omaha, Nebraska, USA.
⁹ University of California, Los Angeles, California, USA.
¹⁰ McGill University, Montreal, Quebec, Canada.
¹¹ Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.
¹² Division of AIDS Research, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland, USA.
¹³ Missouri Institute of Mental Health, University of Missouri, Saint Louis, Missouri, USA.

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.

Keywords: HIV; HIV-associated neurocognitive disorders; cognitive impairment; deep learning; machine learning.

Publication types

Research Support, N.I.H., Extramural

MeSH terms

Cognition
Cognition Disorders*
Cognitive Dysfunction* / etiology
HIV Infections* / complications
HIV Infections* / drug therapy
Humans
Machine Learning
Phenotype

Abstract

Publication types

MeSH terms

Grants and funding