AIMC Topic: HIV Infections
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A reinforcement learning model to inform optimal decision paths for HIV elimination.
Mathematical biosciences and engineering : MBE
Sep 6, 2021
The 'Ending the HIV Epidemic (EHE)' national plan aims to reduce annual HIV incidence in the United States from 38,000 in 2015 to 9300 by 2025 and 3300 by 2030. Diagnosis and treatment are two most effective interventions, and thus, identifying corre...
Identifying HIV-related digital social influencers using an iterative deep learning approach.
AIDS (London, England)
May 1, 2021
OBJECTIVES: Community popular opinion leaders have played a critical role in HIV prevention interventions. However, it is often difficult to identify these 'HIV influencers' who are qualified and willing to promote HIV campaigns, especially online, b...
Development of a predictive model for retention in HIV care using natural language processing of clinical notes.
Journal of the American Medical Informatics Association : JAMIA
Jan 15, 2021
OBJECTIVE: Adherence to a treatment plan from HIV-positive patients is necessary to decrease their mortality and improve their quality of life, however some patients display poor appointment adherence and become lost to follow-up (LTFU). We applied n...
[Prevalence of transmitted drug resistance in HIV-infected treatment-naive patients in Chile].
Revista medica de Chile
Nov 1, 2020
BACKGROUND: Transmitted drug resistance (TDR) occurs in patients with HIV infection who are not exposed to antiretroviral drugs but who are infected with a virus with mutations associated with resistance.
Machine Learning Analysis Reveals Novel Neuroimaging and Clinical Signatures of Frailty in HIV.
Journal of acquired immune deficiency syndromes (1999)
Aug 1, 2020
BACKGROUND: Frailty is an important clinical concern for the aging population of people living with HIV (PLWH). The objective of this study was to identify the combination of risk features that distinguish frail from nonfrail individuals.
Machine-learning classification of neurocognitive performance in children with perinatal HIV initiating de novo antiretroviral therapy.
AIDS (London, England)
Apr 1, 2020
OBJECTIVE: To develop a predictive model of neurocognitive trajectories in children with perinatal HIV (pHIV).
Super learner analysis of real-time electronically monitored adherence to antiretroviral therapy under constrained optimization and comparison to non-differentiated care approaches for persons living with HIV in rural Uganda.
Journal of the International AIDS Society
Mar 1, 2020
INTRODUCTION: Real-time electronic adherence monitoring (EAM) systems could inform on-going risk assessment for HIV viraemia and be used to personalize viral load testing schedules. We evaluated the potential of real-time EAM (transferred via cellula...
Deep Learning Analysis of Cerebral Blood Flow to Identify Cognitive Impairment and Frailty in Persons Living With HIV.
Journal of acquired immune deficiency syndromes (1999)
Dec 15, 2019
BACKGROUND: Deep learning algorithms of cerebral blood flow were used to classify cognitive impairment and frailty in people living with HIV (PLWH). Feature extraction techniques identified brain regions that were the strongest predictors.
Network context matters: graph convolutional network model over social networks improves the detection of unknown HIV infections among young men who have sex with men.
Journal of the American Medical Informatics Association : JAMIA
Nov 1, 2019
OBJECTIVE: HIV infection risk can be estimated based on not only individual features but also social network information. However, there have been insufficient studies using n machine learning methods that can maximize the utility of such information...
Using Smartphone Survey Data and Machine Learning to Identify Situational and Contextual Risk Factors for HIV Risk Behavior Among Men Who Have Sex with Men Who Are Not on PrEP.
Prevention science : the official journal of the Society for Prevention Research
Aug 1, 2019
"Just-in-time" interventions (JITs) delivered via smartphones have considerable potential for reducing HIV risk behavior by providing pivotal support at key times prior to sex. However, these programs depend on a thorough understanding of when risk b...
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