Improving predictive machine learning for retention in HIV care via synthetic data augmentation
About This Grant
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.
Grant Summary
Improving predictive machine learning for retention in HIV care via synthetic data augmentation is a NIMH - National Institute of Mental Health grant providing up to $36K for university, nonprofit, healthcare org. Applications are due 2029-08-07 (open). Check eligibility and apply with FindGrants.
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Eligibility
How to Apply
Up to $36K
2029-08-07
- 1Confirm your organization is eligible for Improving predictive machine learning for retention in HIV care via synthetic data augmentation from NIMH - National Institute of Mental Health, checking organization type, location, and any population or project requirements.
- 2Gather the required documents and information, including your organization details, project plan, and budget figures.
- 3Draft your application narrative and budget addressing the funder's priorities and review criteria. FindGrants can draft each section for you to review and edit.
- 4Review every section against the requirements checklist, then export a submission-ready application pack and submit it to NIMH - National Institute of Mental Health before the deadline.
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Improving predictive machine learning for retention in HIV care via synthetic data augmentation: Frequently Asked Questions
Who is eligible for the Improving predictive machine learning for retention in HIV care via synthetic data augmentation?
Improving predictive machine learning for retention in HIV care via synthetic data augmentation is offered by NIMH - National Institute of Mental Health and is generally open to university, nonprofit, healthcare org. It is open to organizations nationwide unless the funder specifies otherwise. Review the specific eligibility terms before applying, since funders set their own requirements around organization type, location, and the population or project being served.
How much funding does the Improving predictive machine learning for retention in HIV care via synthetic data augmentation provide?
Improving predictive machine learning for retention in HIV care via synthetic data augmentation provides up to $36K per award from NIMH - National Institute of Mental Health. Actual award sizes depend on the scope of your project, available program funds, and the number of applicants, so build a budget that reflects realistic, allowable costs rather than the maximum figure.
When is the Improving predictive machine learning for retention in HIV care via synthetic data augmentation deadline?
Applications for Improving predictive machine learning for retention in HIV care via synthetic data augmentation are due 2029-08-07 (open). Because deadlines can change, verify the date with the funder, NIMH - National Institute of Mental Health, and give yourself enough time to prepare a complete, competitive application before the close date.
How do you apply for the Improving predictive machine learning for retention in HIV care via synthetic data augmentation?
To apply for Improving predictive machine learning for retention in HIV care via synthetic data augmentation, confirm your eligibility, gather the required documents, and prepare a narrative and budget that address the funder's priorities. FindGrants guides you step by step and can draft each section, then exports a submission-ready application pack for this grant from NIMH - National Institute of Mental Health.