Determining how federal pain resarch has responded to the Federal Pain Research Strategy.
Description
The IPRCC has asked NIH to evaluate if all FPRS priorities are being addressed by NIH research. The task would require staff to categorize over 6000 grants into up to 13 FPRS priority areas. A single staff member can curate 5 grants per hour using these parameters, and agreement among staff members is approximately 30%. Therefore, we are carrying out this project using ML to be able to complete the project in a timely manner and without the need of significant staff time investment. It will identify areas of research that require staff to investigate further, instead of having staff curate the whole portfolio.
Detailed example
Expected outpus is a csv file that lists all Federally funded pain research grants and assigns them probabliltiies of addressing each of the 13 FPRS prioritites.
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
Allow staff to complete the analysis requested by the IPRCC without the need for grant by grant curation of the entire Federal Pain Research Portfolio
Controls / human review
ATO: Not reported; PIA: Not published