[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120823-en":3,"doc-seo-120823-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120823,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Machine Learning Approaches to Understand Cognitive Phenotypes in People With HIV - Supplement Article","Cognitive disorders remain common in people with HIV (PWH) despite antiretroviral therapy, with marked heterogeneity in presentation, etiologies, and risk factors. Motivated by this variation and the lack of effective adjunctive treatments for HIV-associated neurocognitive disorders (HAND), the work reviews how machine learning can support data-driven identification of biologically defined subtypes (biotypes). It summarizes the current state of evidence, including associated comorbidities, biological mechanisms, and risk factors, and outlines methods, example applications, challenges, and field requirements for successful integration of machine learning into cognitive disorder research.","UCLA  \nUCLA Previously Published Works  \nTitle  \nMachine Learning Approaches to Understand Cognitive Phenotypes in People With HIV  \nPermalink  \n[https://escholarship.org/uc/item/1kh4z053](https://escholarship.org/uc/item/1kh4z053)  \nJournal  \nThe Journal of Infectious Diseases, 227(Suppl 1)  \nISSN  \n0022-1899  \nAuthors  \nMukerji, Shibani S  \nPetersen, Kalen J Pohl, Kilian Met al.  \nPublication Date  \n2023-03-17  \nDOI  \n10.1093/infdis/jiac293  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nThe Journal of Infectious Diseases  \nSUPPLEMENT ARTICLE  \nMachine Learning Approaches to Understand Cognitive Phenotypes in People With HIV  \nShibani S. Mukerji,1,2,3,a Kalen J. Petersen,4,a Kilian M. Pohl,5,6 Raha M. Dastgheyb,7 Howard S. Fox,8 Robert M. Bilder,9 Marie-Josée Brouillette,10 Alden L. Gross,11 Lori A. J. Scott-Sheldon,12 Robert H. Paul,13 and Dana Gabuzda2,3,  \n1Massachusetts General Hospital, Boston, Massachusetts, USA; 2Dana-Farber Cancer Institute, Boston, Massachusetts, USA; 3Harvard Medical School, Boston, Massachusetts, USA; 4Washington University in Saint Louis, Saint Louis, Missouri, USA; 5Stanford University, Stanford, California, USA; 6SRI International, Menlo Park, California, USA; 7Johns Hopkins University School of Medicine, Baltimore, Maryland, USA; 8University of Nebraska Medical Center, Omaha, Nebraska, USA; 9University of California, Los Angeles, California, USA; 10McGill University, Montreal, Quebec, Canada; 11Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA; 12Division of AIDS Research, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland, USA; and 13Missouri Institute of Mental Health, University of Missouri, Saint Louis, Missouri, USA  \nCognitive 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.  \nKeywords. HIV; cognitive impairment; HIV-associated neurocognitive disorders; machine learning; deep learning.  \nDespite the success of current antiretroviral therapies (ART) in achieving viral suppression and improving longevity of people with human immunodeficiency virus (PWH), the mechanisms underlying human immunodeficiency virus (HIV)-associated neurocognitive disorders (HAND) remain poorly understood and effective adjunctive therapies are still lacking [ 1] . While the prevalence of HIV-associated dementia has declined with viral suppression on ART, milder forms of HAND remain prevalent [ 1, 2]. The reported rates of HAND vary widely between studies of PWH who are virally suppressed, with estimates typically ranging from 20% to 50%[ 1–7]. Given the heterogeneity of cognitive disorders in PWH in the current ART era and evidence that these disorders have different etiologies and risk factors, scientific rationale is growing for using data-driven approaches to identify biologically defined subtypes (biotypes) .  \naS. S. M. and K. J. P. contribut","cbCaiqCmYmC5xBLR","https://ap.wps.com/l/cbCaiqCmYmC5xBLR","pdf",569666,1,11,"English","en",105,"# Machine learning and cognitive phenotypes in HIV\n## Scientific rationale for data-driven biotypes\n## HAND heterogeneity and current classification (Frascati criteria)\n## Evidence base, methods, and applications\n## Challenges and requirements for implementation","[{\"question\":\"Why is there interest in machine learning for cognitive disorders in people with HIV?\",\"answer\":\"Cognitive disorders in PWH show substantial heterogeneity in etiologies and risk factors, and effective adjunctive therapies for HAND are still limited. Data-driven models can help identify biologically defined subtypes (biotypes).\"},{\"question\":\"What criteria are used for diagnosing cognitive disorders in PWH?\",\"answer\":\"The HAND diagnostic scheme, commonly referred to as the Frascati criteria (published in 2007), relies on neurocognitive testing and functional ability assessments for activities of daily living.\"},{\"question\":\"What main topics does the article cover regarding machine learning?\",\"answer\":\"It discusses the state of science using machine learning to study cognitive phenotypes in PWH, including associated comorbidities, biological mechanisms, risk factors, and practical methods, example applications, challenges, and integration needs.\"}]","Machine Learning Approaches to Understand Cognitive Phenotypes in People With HIV - Supplement Article | PDF",1785732196,28,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-approaches-to-understand-cognitive-phenotypes-in-people-with-hiv-supplement-article","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-approaches-to-understand-cognitive-phenotypes-in-people-with-hiv-supplement-article/120823/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is there interest in machine learning for cognitive disorders in people with HIV?","Question",{"text":75,"@type":76},"Cognitive disorders in PWH show substantial heterogeneity in etiologies and risk factors, and effective adjunctive therapies for HAND are still limited. Data-driven models can help identify biologically defined subtypes (biotypes).","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What criteria are used for diagnosing cognitive disorders in PWH?",{"text":80,"@type":76},"The HAND diagnostic scheme, commonly referred to as the Frascati criteria (published in 2007), relies on neurocognitive testing and functional ability assessments for activities of daily living.",{"name":82,"@type":73,"acceptedAnswer":83},"What main topics does the article cover regarding machine learning?",{"text":84,"@type":76},"It discusses the state of science using machine learning to study cognitive phenotypes in PWH, including associated comorbidities, biological mechanisms, risk factors, and practical methods, example applications, challenges, and integration needs.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]