NIH grant: Precision Rehab for Patients Post-Stroke Through Artificial Intelligence (1R01HD117897-01A1)

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NIH grant 1R01HD117897-01A1: Precision Rehab for Patients Post-Stroke Through Artificial Intelligence
Institute: {'code': 'HD', 'abbreviation': 'NICHD', 'name': 'Eunice Kennedy Shriver National Institute of Child Health and Human Development', 'admin_org_id': '12500030', 'admin_funding_url': ''}. PI: Allyn M Bove. Award: $676469.
Start: 2026-07-10T00:00:00.
Project Summary
Stroke stands as a leading contributor to disability, with an estimated 89.1 million adults worldwide, and 9.4
million adults in the United States. Rehabilitation interventions are effective in mitigating stroke-related
disability, greatly enhancing the quality of life, and reducing life-time health care expenses. While standardized
rehabilitation protocols are effective in some patients, the wide variety in recovery patterns seen post-stroke
make it extremely difficult to correctly identify the right rehabilitation intervention (i.e., which exercises) at the
right dosage (e.g., duration, sets, reps) at the right time for each patient. The integration of rehabilitation
activities and outcomes into electronic health records (EHRs) has offered considerable advantages. However,
there are notable gaps in the current research on the use of EHRs in rehabilitation research, particularly the
difficulty of using free-text EHR data for big data analytics and the lack of predictive modeling that integrates
both structured and unstructured EHR data. Our project recognizes this critical gap and aims to bridge it by
developing and implementing innovative natural language processing (NLP) algorithms to extract rehabilitation
information from free-text EHR data, alongside effective predictive models that utilize both structured and
unstructured EHR data. This approach is designed to prescribe optimal rehabilitation exercises tailored to
individual patients, thereby enhancing clinical outcomes. To address these gaps, this project outlines three
specific aims: Aim 1: Development of a Rehabilitation Ontology and Ontology-based Natural Language
Processing (NLP) Algorithms. Aim 2: Creation of Predictive Models for Personalized Rehabilitation
Recommendations to Maximize Function Outcomes. Aim 3: Validation of NLP and Predictive Models Using
the ENACT Network. By accomplishing these aims, our project seeks to revolutionize stroke rehabilitation,
making it more responsive to the individual needs of patients. Through the use of advanced AI, we are poised
to offer patients post-stroke a more personalized and effective path to recovery. By demonstrating success in
diverse healthcare settings in the large national network, we aim to overcome adoption barriers, ensuring
scalability and adaptability to various EHR systems and regional healthcare settings.

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