ArticleJournal of acquired immune deficiency syndromes (1999)2017
Toward Automating HIV Identification: Machine Learning for Rapid Identification of HIV-Related Social Media Data.
Article in Journal of acquired immune deficiency syndromes (1999), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- Artificial intelligence in HIV research, policy, and clinical care.The lancet. HIV · 2026Review
- Analytic Methods to Investigate HIV-Related Digital Communication: A Scoping Review.Current HIV/AIDS reports · 2025Article
- Identifying Substance Use and High-Risk Sexual Behavior Among Sexual and Gender Minority Youth by Using Mobile Phone Data: Development and Validation Study.Online journal of public health informatics · 2025Article
- Explainable artificial intelligence and domain adaptation for predicting HIV infection with graph neural networks.Annals of medicine · 2024Article
- Digital Epidemiological Approaches in HIV Research: a Scoping Methodological Review.Current HIV/AIDS reports · 2023Article
- Identification of Key Influencers for Secondary Distribution of HIV Self-Testing Kits Among Chinese Men Who Have Sex With Men: Development of an Ensemble Machine Learning Approach.Journal of medical Internet research · 2023Article
- Machine learning to predict virological failure among HIV patients on antiretroviral therapy in the University of Gondar Comprehensive and Specialized Hospital, in Amhara Region, Ethiopia, 2022.BMC medical informatics and decision making · 2023Article
- Predicting the HIV/AIDS Knowledge among the Adolescent and Young Adult Population in Peru: Application of Quasi-Binomial Logistic Regression and Machine Learning Algorithms.International journal of environmental research and public health · 2023Article
- Addressing Health Inequities in Digital Clinical Trials: A Review of Challenges and Solutions From the Field of HIV Research.Epidemiologic reviews · 2022Review
- Prediction of HIV status based on socio-behavioural characteristics in East and Southern Africa.PloS one · 2022Article
- Digital Public Health Surveillance Tools for Alcohol Use and HIV Risk Behaviors.AIDS and behavior · 2021Article
- Artificial intelligence and sexual health in the USA.The Lancet. Digital health · 2021Article
- Technology-Delivered Intervention Strategies to Bolster HIV Testing.Current HIV/AIDS reports · 2021Review
- Machine Learning and Clinical Informatics for Improving HIV Care Continuum Outcomes.Current HIV/AIDS reports · 2021Review
- Power of Big Data in ending HIV.AIDS (London, England) · 2021Article
- Identifying influential neighbors in social networks and venue affiliations among young MSM: a data science approach to predict HIV infection.AIDS (London, England) · 2021Article
- Identifying HIV-related digital social influencers using an iterative deep learning approach.AIDS (London, England) · 2021Article
- Monitoring HIV testing and pre-exposure prophylaxis information seeking by combining digital and traditional data.BMC infectious diseases · 2021Article
- Considerations for the Ethical Implementation of Psychological Assessment Through Social Media via Machine Learning.Ethics & behavior · 2021Article
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3 authors.
Funding
Abstract
introduction"Social big data" from technologies such as social media, wearable devices, and online searches continue to grow and can be used as tools for HIV research. Although researchers can uncover patterns and insights associated with HIV trends and transmission, the review process is time consuming and resource intensive. Machine learning methods derived from computer science might be used to assist HIV domain experts by learning how to rapidly and accurately identify patterns associated with HIV from a large set of social data.
methodsUsing an existing social media data set that was associated with HIV and coded by an HIV domain expert, we tested whether 4 commonly used machine learning methods could learn the patterns associated with HIV risk behavior. We used the 10-fold cross-validation method to examine the speed and accuracy of these models in applying that knowledge to detect HIV content in social media data. RESULTS AND DISCUSSION: Logistic regression and random forest resulted in the highest accuracy in detecting HIV-related social data (85.3%), whereas the Ridge Regression Classifier resulted in the lowest accuracy. Logistic regression yielded the fastest processing time (16.98 seconds).
conclusionsMachine learning can enable social big data to become a new and important tool in HIV research, helping to create a new field of "digital HIV epidemiology." If a domain expert can identify patterns in social data associated with HIV risk or HIV transmission, machine learning models could quickly and accurately learn those associations and identify potential HIV patterns in large social data sets.
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