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Improving predictive machine learning for retention in HIV care via synthetic data augmentation

Award Number: F31MH143657
ORGANIZATION: NATIONAL INSTITUTE OF MENTAL HEALTH
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: FELLOWSHIP/SCHOLARSHIP/STUDENT LOANS
PERIOD OF PERFORMANCE START DATE: 08/11/2026
PERIOD OF PERFORMANCE END DATE: 08/10/2027

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Improving predictive machine learning for retention in HIV care via synthetic data augmentation - 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.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2026 ( Subtotal = $35,556 )
2026	2026	VANDERBILT UNIVERSITY	110 21ST AVE S	NASHVILLE	TN	37203	DAVIDSON	USA	Mental Health Research Grants	000	1	2/11/2026	NEW	$34,980
2026	2026	VANDERBILT UNIVERSITY	110 21ST AVE S	NASHVILLE	TN	37203	DAVIDSON	USA	Mental Health Research Grants	001	1	3/26/2026	NEW	$576
														Subtotal = $35,556

Grand Total All Awards = $35,556

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Improving predictive machine learning for retention in HIV care via synthetic data augmentation

Award Number: F31MH143657

ORGANIZATION: NATIONAL INSTITUTE OF MENTAL HEALTH

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: FELLOWSHIP/SCHOLARSHIP/STUDENT LOANS

PERIOD OF PERFORMANCE START DATE: 08/11/2026

PERIOD OF PERFORMANCE END DATE: 08/10/2027

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