ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2005
Neural network-longitudinal assessment of the Electronic Anti-Retroviral THerapy (EARTH) cohort to follow response to HIV-treatment.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2005. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed, 10 citations in OpenAlex.
- Studying patterns and predictors of HIV viral suppression using A Big Data approach: a research protocol.BMC infectious diseases · 2022Article
- Fuzzy-multidimensional deep learning for efficient prediction of patient response to antiretroviral therapy.Heliyon · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
HIV infection is for the most part a chronic and asymptomatic disease. To properly monitor the health status of infected individuals it is important to use host and viral surrogate markers as well as pharmacokinetic parameters. Disease progression, assessment of the antiviral potency of the drugs and response to therapy can only be monitored by repetitive measures of viral and host parameters. To prevent the emergence of antiviral drug-resistance, long term side effects and to decide on the appropriate treatment choices, a comprehensive assessment of all contributing factors, medical and non-medical, is necessary. However, the relationship between treatment outcomes with disease markers and other contributing factors is not simple. To date, a model that accurately predicts the likelihood of disease progression or treatment failure in HIV infected patients does not exist. Extending our previous work in this area, we developed temporal Artificial Intelligence models based on Jordan-Elman networks to longitudinally follow viral surrogate markers together with demographics, biochemical and laboratory data to describe the drug-virus-host interactions in over 4000 HIV adult patients. In an international (multi-continent) study of HIV clinical and laboratory data, the profiles of drug-naïve as well as treated patients were evaluated during a 20 year follow-up. Validation of models on a subset of this cohort (n=595) estimated the sensitivity and specificity of treatment success/failure, under different management modalities for individual patients. ROC-curves predicted: virologic success from baseline (ROC=0.871) in drug-naïve previously non-treated patients, switch from virologic success/ failure to failure/success if ever and when (ROC=0.625), switch to virologic success/failure from failure/success within 6 months (ROC=0.722) following a previous switch. This tool may be helpful in the design of longitudinal clinical trials.
Indexed as
Identifiers
16779050PMC1560514W1645224590What OpenQuestion holds
Registered trials
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.