Tuesday, September 30, 2025 9/30/2025

Predicting Pain Recovery: A Multimodality Machine-learning Approach using Harmonized Electronic Health Record Data

Award Number: OT2OD037955
ORGANIZATION: OFFICE OF THE DIRECTOR, NIH
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: OTHER
PERIOD OF PERFORMANCE START DATE: 09/15/2025
PERIOD OF PERFORMANCE END DATE: 09/14/2026

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Predicting Pain Recovery: A Multimodality Machine-learning Approach using Harmonized Electronic Health Record Data - We will develop robust, ethical, and fair predictive models using harmonized electronic health record data including brain imaging from two institutions (Mayo Clinic and the VA Health Care System) from patients with headache pain diagnoses (migraine, post-traumatic headache, post-stroke headache) of various ages, genders, medical histories, ethnic backgrounds and sociodemographic statuses to identify generalizable factors that contribute to pain persistence and to develop prognostic models that accurately predict pain recovery using multimodal fused data. Model development will be first conducted on each dataset separately due to the ‘uniqueness’ of data available within each dataset. Subsequently, will we develop a novel contrastive learning model to fuse common and unique modalities from both databases to create a harmonized, clinically useful model for predicting pain recovery that can harness data incompleteness and is flexible to real-world settings where not all patients have all data available. Supervised learning and a vision-language model will be used to extract unique features and to connect data of different types across modalities. Testing and validation accuracies of the harmonized model will be compared to the prediction accuracies of the individual models. Code will be released using Huggingface model zoo and GitHub. Throughout our milestones, we will build in opportunities for stakeholder considerations of multimodal AI for chronic pain prediction to ensure that potential uses of the model are aligned with the needs and values of healthcare providers (the end users) and patients (whose data informs the AI) ensuring sensitivity of the impact of multimodal AI on healthcare delivery.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2025 ( Subtotal = $1,156,554 )
2025	2025	MAYO CLINIC ARIZONA	13400 E SHEA BLVD	SCOTTSDALE	AZ	85259	MARICOPA	USA	Trans-NIH Research Support	000	1	9/11/2025	NEW	$1,156,554
														Subtotal = $1,156,554

Grand Total All Awards = $1,156,554

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Predicting Pain Recovery: A Multimodality Machine-learning Approach using Harmonized Electronic Health Record Data

Award Number: OT2OD037955

ORGANIZATION: OFFICE OF THE DIRECTOR, NIH

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: OTHER

PERIOD OF PERFORMANCE START DATE: 09/15/2025

PERIOD OF PERFORMANCE END DATE: 09/14/2026

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