# NIH grant: Improving predictive machine learning for retention in HIV care via synthetic data augmentation (1F31MH143657-01) — retained record

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NIH grant 1F31MH143657-01: Improving predictive machine learning for retention in HIV care via synthetic data augmentation
Institute: {'code': 'MH', 'abbreviation': 'NIMH', 'name': 'National Institute of Mental Health', 'admin_org_id': '12500050', 'admin_funding_url': ''}. PI: Nicholas James Jackson. Award: $35556.
Start: 2026-08-08T00:00:00.
PROJECT SUMMARY
This research proposal aims to enhance the effectiveness of machine learning (ML) in improving adherence to
antiretroviral therapy (ART) among people living with HIV (PWH). Adherence to ART is critical for viral
suppression, a key goal in the Ending the HIV Epidemic Plan for the United States. While modern ML methods
have been shown to identify individuals at risk of missing their visits with HIV providers and potentially
discontinuing ART, these ML models are only as effective as the data they are trained on. As a result, ML models
can be ineffective when applied to different patient populations or different healthcare settings,
potentially worsening health outcomes for PWH.
To address this challenge, this project will study synthetic data augmentation (SDA), a method that uses
generative artificial intelligence (AI) to create additional data to train ML models. This approach has shown
promise in various applications of ML, but has yet to be evaluated in, or adapted for, the domain of HIV care. My
hypothesis is that SDA can improve ML models' ability to predict retention in HIV care, ensuring the
benefits of these technologies extend to all PWH. However, there are limitations to SDA that must first be
addressed. First, SDA is a new method and can be performed with many different computational approaches.
Thus far, these varied methodologies for SDA have produced mixed results, making it unclear what the precise
mechanism is behind how SDA works and in what circumstances SDA will be most effective. Second, the
generative AI models that SDA utilizes do not have any external knowledge, learning only what they observe in
the data. This makes generative AI models prone to creating unrealistic or biomedically implausible synthetic
data. While a well-trained generative model does this infrequently, this can still occur and harm the performance
of ML models trained via SDA.
The proposal is structured around three specific aims. Aim 1: develop a foundational framework for
understanding the impact of SDA on ML performance, particularly in the presence of issues common to HIV
datasets. Aim 2: enhance the quality of synthetic HIV data by embedding biomedical knowledge into
generative AI models. Aim 3: apply SDA to a large cohort dataset of PWH in the United States and evaluate
the effectiveness of SDA in improving ML performance across different healthcare sites and populations.
Successful completion of this project will lead to more reliable and generalized ML systems, ensuring that
advancements in ML technology benefit all individuals living with HIV.
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