Machine Learning Approaches to Understand Cognitive Phenotypes in People With HIV.
Title | Machine Learning Approaches to Understand Cognitive Phenotypes in People With HIV. |
Publication Type | Journal Article |
Year of Publication | 2023 |
Authors | Mukerji, SS, Petersen, KJ, Pohl, KM, Dastgheyb, RM, Fox, HS, Bilder, RM, Brouillette, M-J, Gross, AL, Scott-Sheldon, LAJ, Paul, RH, Gabuzda, D |
Journal | J Infect Dis |
Volume | 227 |
Issue | Suppl 1 |
Pagination | S48-S57 |
Date Published | 2023 Mar 17 |
ISSN | 1537-6613 |
Keywords | External |
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. |
DOI | 10.1093/infdis/jiac293 |
Alternate Journal | J Infect Dis |
PubMed ID | 36930638 |
PubMed Central ID | PMC10022709 |
Grant List | U01 AA017347 / AA / NIAAA NIH HHS / United States U24 MH100925 / MH / NIMH NIH HHS / United States R01 MH113406 / MH / NIMH NIH HHS / United States R01 MH118514 / MH / NIMH NIH HHS / United States P30 AI094189 / AI / NIAID NIH HHS / United States P30 MH062261 / MH / NIMH NIH HHS / United States K23 MH115812 / MH / NIMH NIH HHS / United States R01 MH128868 / MH / NIMH NIH HHS / United States R01 MH113560 / MH / NIMH NIH HHS / United States R01 MH114152 / MH / NIMH NIH HHS / United States R56 MH115853 / MH / NIMH NIH HHS / United States R03 MH123290 / MH / NIMH NIH HHS / United States R01 MH118031 / MH / NIMH NIH HHS / United States 5R01MH110259 / NH / NIH HHS / United States R01 MH110259 / MH / NIMH NIH HHS / United States F32 MH129151 / MH / NIMH NIH HHS / United States |