Explainable AI for Predicting Incidents of Depression in People with HIV
About This Grant
ABSTRACT The prevalence of Major Depressive Disorder (MDD) among people with HIV (PWH) is 20-50%, which is 3 times higher than the prevalence of MDD in the general population. Depression is a leading risk factor for continuity of care among PWH, including adherence to anti-retroviral therapy (ART). Complicating clinical decision making and patient care are the overlapping symptoms of depression with those of HIV and side effects of ART (e.g., psychomotor slowing, difficulties thinking, sleep disruption, fatigue, apathy, and other comorbidities (e.g., anxiety). In addition to depression being underdiagnosed, our preliminary machine learning analyses indicate bidirectional associations between HIV and depression: (1) depressive symptoms predict chronic immune dysregulation (e.g., CD4/CD8 inversion) among virally suppressed PWH; and (2) elevated levels of peripheral inflammatory markers despite sustained viral control predict depression incidence and persistence. These findings suggest an urgent need for novel approaches to accurately identify and manage depression in PWH. Aligned with the NIMH’s Research Domain Criteria (RDoC), we propose an innovative paradigm shift to elucidate the interplay between these overlapping constructs by moving away from conventional a priori measurement selection and small single-study, group-level analyses to integrative, multimodal analyses leveraging existing data acquired prospectively across multiple longitudinal studies from 3962 PWH. To maximize research efficiencies, we will harmonize the datasets to the repository of the NIH-sponsored National NeuroHIV Tissue Consortium (NNTC). We will then create a pioneering, multimodal, explainable AI (xAI) framework that uses knowledge graphs to holistically capture individual-level neurobiological, cognitive, sociodemographic, and environmental determinants of depression in PWH. We will prioritize interpretability, scientific rigor (including reproducibility), and clinical relevance. We will ensure ethical and stakeholder-driven development in order to support the vision of this team in translating results into actionable clinical strategies capable of improving treatment outcomes for PWH. Specifically, in Aim 1, we will develop and validate HIV- specific data-driven models that will use knowledge graphs to predict depression from self-report questionnaires (e.g., Beck Depression Inventory-II), neuropsychological testing, and MR images. In Aim 2, we will create xAI capable of forecasting future incidents and non-remitting cases of depression based on longitudinal data in virally suppressed individuals. By sharing our validated explainable AI tools publicly, this research promises immediate, tangible benefits to HIV clinical care, with the potential of enabling precise identification and management of depression risk. Doing so will help refine clinical care strategies to reduce the clinical burden of depression in people with HIV.
Grant Summary
Explainable AI for Predicting Incidents of Depression in People with HIV is a NIMH - National Institute of Mental Health grant providing up to $723K for university, nonprofit, healthcare org. Applications are due 2031-03-31 (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 $723K
2031-03-31
- 1Confirm your organization is eligible for Explainable AI for Predicting Incidents of Depression in People 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.
Explainable AI for Predicting Incidents of Depression in People with HIV: Frequently Asked Questions
Who is eligible for the Explainable AI for Predicting Incidents of Depression in People with HIV?
Explainable AI for Predicting Incidents of Depression in People 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 Explainable AI for Predicting Incidents of Depression in People with HIV provide?
Explainable AI for Predicting Incidents of Depression in People with HIV provides up to $723K 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 Explainable AI for Predicting Incidents of Depression in People with HIV deadline?
Applications for Explainable AI for Predicting Incidents of Depression in People with HIV are due 2031-03-31 (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 Explainable AI for Predicting Incidents of Depression in People with HIV?
To apply for Explainable AI for Predicting Incidents of Depression in People 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.