Harnessing Multimodal AI to Reduce Virologic Failure in People Living with HIV
About This Grant
Artificial intelligence (AI) shows significant promise in optimizing HIV prevention and treatment efforts by identifying modifiable behavioral or environmental factors and enhancing intervention strategies to deliver more impactful interventions. This proposal focuses on identifying factors that predict virologic failure among persons living with HIV (PLWH) in the United States (US). We propose to design and integrate multimodal data from a mobile health system called Behavioral Engagement and Adherence Monitoring (BEAM), guided by human- centered design and AI ethical principles, to capture deeper contextual information surrounding HIV virologic failure in a two-year longitudinal cohort study of PLWH at a large US-based HIV clinic. Multimodal data will be collected to develop accurate, safe, efficient, and unbiased AI models that, in conjunction with knowledge graphs (KGs), will predict virologic failure among PLWH. Throughout this process, a Community Advisory Board (CAB) of PLWH will provide iterative feedback on all aspects of the study to ensure the models conform to AI ethical principles, minimizes bias, and are patient centered. We propose the following aims: Aim 1: With extensive input from the CAB, focus groups, and scientific literature, we will identify key predictors of HIV virologic failure, and their interconnections, and develop a knowledge graph to inform the design and implementation of BEAM (Behavioral Engagement and Adherence Monitoring). We will conduct four focus groups (n=8/group) of PLWH and integrate findings with scientific evidence to develop initial drafts of KGs. Aim 2: Implement BEAM in a 24-month longitudinal cohort of 200 patients at a large US-based HIV clinic to capture multimodal indicators of virologic failure risk allowing for real-world validation and adaptation of the knowledge graphs. We will collect participant data from wearables (i.e., Fitbits), medication event monitoring systems (MEMS), surveys, ecological momentary assessment (EMA) surveys, and a mobile app over a 12-month period, and clinical data from electronic health records (EHR) over a 24-month period. Aim 3: Using a human-centered and ethical approach, develop AI models to predict HIV virologic failure and iteratively refine knowledge graphs. AI models will be constructed through pre-processing the data, model training and evaluation, and integrating knowledge graphs. Aim 4: Leverage AI models and knowledge graphs to identify and co-develop, with the CAB, intervention use cases to deliver tailored behavioral supports to at-risk PLWH and evaluate them through theater testing. We will co-create with the CAB novel intervention strategies by leveraging AI-predicted risk factors and knowledge graph-derived insights and conduct theater testing with PLWH (n=8) and key stakeholders (n=8). The proposal is significant because rate of viral suppression among PLWH in the US is only at 65%, despite decades of efforts to engage PLWH in care. The public health impact of this proposal is strengthened by the application of the model to multiple lines of promising interventions to maximize their impact, to be tested more fully in future trials.
Grant Summary
Harnessing Multimodal AI to Reduce Virologic Failure in People Living with HIV is a NIMH - National Institute of Mental Health grant providing up to $1.1M for university, nonprofit, healthcare org. Applications are due 2031-04-30 (open). Check eligibility and apply with FindGrants.
Not quite the right fit?
Search 9,000+ open grants, or get matches ranked for your organization — free.
Focus Areas
Eligibility
How to Apply
Up to $1.1M
2031-04-30
- 1Confirm your organization is eligible for Harnessing Multimodal AI to Reduce Virologic Failure in People Living with HIV 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.
Don't want to draft it yourself?
We'll draft the complete application against NIMH - National Institute of Mental Health's requirements, run a quality review, and email you a submission-ready PDF plus an editable Word doc within 5 business days. Most orders deliver in 24-48 hours. Flat $399, any grant size.
AI Requirement Analysis
Detailed requirements not yet analyzed
Have the NOFO? Paste it below for AI-powered requirement analysis.
Harnessing Multimodal AI to Reduce Virologic Failure in People Living with HIV: Frequently Asked Questions
Who is eligible for the Harnessing Multimodal AI to Reduce Virologic Failure in People Living with HIV?
Harnessing Multimodal AI to Reduce Virologic Failure in People Living with HIV 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 Harnessing Multimodal AI to Reduce Virologic Failure in People Living with HIV provide?
Harnessing Multimodal AI to Reduce Virologic Failure in People Living with HIV provides up to $1.1M 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 Harnessing Multimodal AI to Reduce Virologic Failure in People Living with HIV deadline?
Applications for Harnessing Multimodal AI to Reduce Virologic Failure in People Living with HIV are due 2031-04-30 (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 Harnessing Multimodal AI to Reduce Virologic Failure in People Living with HIV?
To apply for Harnessing Multimodal AI to Reduce Virologic Failure in People Living with HIV, 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.