MindGuard: Early Prediction of Post-Concussion Mental Health Sequelae in Youth with a Multimodal AI System
NICHD - Eunice Kennedy Shriver National Institute of Child Health and Human Development
About This Grant
PROJECT SUMMARY Concussion and mental health are two significant public health problems disproportionately affecting youth. Con- cussions can severely impact developing brains and are potentially linked to mental health issues like anxiety, depression, and suicidality. Early detection of at-risk youth using artificial intelligence and machine learning (AI/ML) techniques is crucial for timely referrals and treatment. However, current AI/ML models often rely solely on structured electronic health records (EHR) data, neglecting other data types like unstructured clinical notes or wearable sensor data from nonclinical settings. Additionally, many models lack human-centered AI design principles, resulting in rapid abandonment by end-users during deployment. To address these gaps, we have assembled an interdisciplinary team to develop a multimodal AI-based data collection system, MindGuard (Aim 1), use MindGuard to collect multimodal nonclinical data from youth concussion patients aged 11-17 in home settings (Aim 2), develop and evaluate a risk prediction model for post-concussion mental health sequelae using large-scale EHR and MindGuard-collected small-scale nonclinical data (Aim 3), and create and evaluate a hu- man-centered AI system with an explainable risk prediction dashboard to support clinicians decision-making (Aim 4). Our long-term goal is to prevent mental health sequelae and aid concussion recovery in youth. We will use a large EHR dataset of approximately 20,000 youth concussion patients aged 11 to 17 from Nationwide Children’s Hospital (2013-2025). This dataset will be linked to unstructured clinical notes, SDoH, and small-scale multimodal nonclinical data collected prospectively using MindGuard. MindGuard includes data from wearable and smart speaker devices used by 150 youth concussion patients aged 11 to 17. We will use the linked full data to develop and evaluate an AI/ML predictive model for mental health sequelae post-concussion and create an interactive dashboard for clinicians. The main study outcomes will be measured as diagnoses of mental health disorders and self-harm. This project is significant as it addresses two major public health issues affecting youth. It is innovative in its use of wearable sensors, large language model (LLM)-based voice interactions, prospec- tively collected multimodal patient data, advanced AI/ML techniques, and an interactive decision support dash- board. The findings will have a substantial impact by facilitating early detection and timely treatment for at-risk youth to mitigate the risk of mental health sequelae among youth with concussion.
Grant Summary
MindGuard: Early Prediction of Post-Concussion Mental Health Sequelae in Youth with a Multimodal AI System is a NICHD - Eunice Kennedy Shriver National Institute of Child Health and Human Development grant providing up to $726K for university, nonprofit, healthcare org. Applications are due 2031-03-31 (open). Check eligibility and apply with FindGrants.
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Up to $726K
2031-03-31
- 1Confirm your organization is eligible for MindGuard: Early Prediction of Post-Concussion Mental Health Sequelae in Youth with a Multimodal AI System from NICHD - Eunice Kennedy Shriver National Institute of Child Health and Human Development, 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 NICHD - Eunice Kennedy Shriver National Institute of Child Health and Human Development before the deadline.
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MindGuard: Early Prediction of Post-Concussion Mental Health Sequelae in Youth with a Multimodal AI System: Frequently Asked Questions
Who is eligible for the MindGuard: Early Prediction of Post-Concussion Mental Health Sequelae in Youth with a Multimodal AI System?
MindGuard: Early Prediction of Post-Concussion Mental Health Sequelae in Youth with a Multimodal AI System is offered by NICHD - Eunice Kennedy Shriver National Institute of Child Health and Human Development 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 MindGuard: Early Prediction of Post-Concussion Mental Health Sequelae in Youth with a Multimodal AI System provide?
MindGuard: Early Prediction of Post-Concussion Mental Health Sequelae in Youth with a Multimodal AI System provides up to $726K per award from NICHD - Eunice Kennedy Shriver National Institute of Child Health and Human Development. 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 MindGuard: Early Prediction of Post-Concussion Mental Health Sequelae in Youth with a Multimodal AI System deadline?
Applications for MindGuard: Early Prediction of Post-Concussion Mental Health Sequelae in Youth with a Multimodal AI System are due 2031-03-31 (open). Because deadlines can change, verify the date with the funder, NICHD - Eunice Kennedy Shriver National Institute of Child Health and Human Development, and give yourself enough time to prepare a complete, competitive application before the close date.
How do you apply for the MindGuard: Early Prediction of Post-Concussion Mental Health Sequelae in Youth with a Multimodal AI System?
To apply for MindGuard: Early Prediction of Post-Concussion Mental Health Sequelae in Youth with a Multimodal AI System, 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 NICHD - Eunice Kennedy Shriver National Institute of Child Health and Human Development.