ArticleEClinicalMedicine2019
Algorithmic prediction of HIV status using nation-wide electronic registry data.
Article in EClinicalMedicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 4 of them syntheses that pooled it.
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Who cites it
23 citing papers in PubMed, 4 syntheses or guidelines pooled it, 49 citations in OpenAlex.
- Machine Learning in HIV Care and Antiretroviral Therapy: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Risk prediction models for targeted testing of HIV, hepatitis B and hepatitis C: a systematic review and meta-analysis.BMC infectious diseases · 2025Pooled it
- Machine learning for personalized risk assessment of HIV, syphilis, gonorrhoea and chlamydia: A systematic review and meta-analysis.International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases · 2025Pooled it
- Current ARTs, Virologic Failure, and Implications for AIDS Management: A Systematic Review.Viruses · 2023Pooled it
- Evaluating machine learning algorithms for predicting HIV status among young Thai men who have sex with men.BMJ health & care informatics · 2025Article
- Building models, building capacity: A review of participatory machine learning for HIV prevention.PLOS global public health · 2025Review
- The status of machine learning in HIV testing in South Africa: a qualitative inquiry with stakeholders in Gauteng province.Frontiers in digital health · 2025Article
- Using Machine Learning to Identify Patients at Risk of Acquiring HIV in an Urban Health System.Journal of acquired immune deficiency syndromes (1999) · 2024Article
- Lightweight federated learning for STIs/HIV prediction.Scientific reports · 2024Article
- Generalizable pipeline for constructing HIV risk prediction models across electronic health record systems.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Testing the Feasibility and Acceptability of Using an Artificial Intelligence Chatbot to Promote HIV Testing and Pre-Exposure Prophylaxis in Malaysia: Mixed Methods Study.JMIR human factors · 2024Article
- STI/HIV risk prediction model development-A novel use of public data to forecast STIs/HIV risk for men who have sex with men.Frontiers in public health · 2024Article
- Development of a predictive model for identifying women vulnerable to HIV in Chicago.BMC women's health · 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
- Supervised machine learning algorithms to predict the duration and risk of long-term hospitalization in HIV-infected individuals: a retrospective study.Frontiers in public health · 2023Article
- Predicting HIV Status among Men Who Have Sex with Men in Bulawayo & Harare, Zimbabwe Using Bio-Behavioural Data, Recurrent Neural Networks, and Machine Learning Techniques.Tropical medicine and infectious disease · 2022Article
- Prediction of HIV status based on socio-behavioural characteristics in East and Southern Africa.PloS one · 2022Article
- Application of machine learning algorithms in predicting HIV infection among men who have sex with men: Model development and validation.Frontiers in public health · 2022Article
- 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
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLate HIV diagnosis is detrimental both to the individual and to society. Strategies to improve early diagnosis of HIV must be a key health care priority. We examined whether nation-wide electronic registry data could be used to predict HIV status using machine learning algorithms.
methodsWe extracted individual level data from Danish registries and used algorithms to predict HIV status. We used various algorithms to train prediction models and validated these models. We calibrated the models to mimic different clinical scenarios and created confusion matrices based on the calibrated models.
findingsA total 4,384,178 individuals, including 4,350 with incident HIV, were included in the analyses. The full model that included all variables that included demographic variables and information on past medical history had the highest area under the receiver operating characteristics curves of 88·4% (95%CI: 87·5% - 89·4%) in the validation dataset. Performance measures did not differ substantially with regards to which machine learning algorithm was used. When we calibrated the models to a specificity of 99·9% (pre-exposure prophylaxis (PrEP) scenario), we found a positive predictive value (PPV) of 8·3% in the full model. When we calibrated the models to a sensitivity of 90% (screening scenario), 384 individuals would have to be tested to find one undiagnosed person with HIV.
interpretationMachine learning algorithms can learn from electronic registry data and help to predict HIV status with a fairly high level of accuracy. Integration of prediction models into clinical software systems may complement existing strategies such as indicator condition-guided HIV testing and prove useful for identifying individuals suitable for PrEP.
fundingThe study was supported by funds from the Preben and Anne Simonsens Foundation, the Novo Nordisk Foundation, Rigshospitalet, Copenhagen University, the Danish AIDS Foundation, the Augustinus Foundation and the Danish Health Foundation.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.