HEAL Portfolio Topic Analysis
Description
The HEAL Initiative supports hundreds of pain research studies. It is meant to support pain research that cannot be carried out by an individual IC. One challenge we have is describing what HEAL research is. This project is an attempt to give program staff a starting point and visualization that describes the topics covered by HEAL research so that staff can start to understand the HEAL portfolio and how the different programs in HEAL are related or different. It will also allow staff to be able to explain how the HEAL portfolio is different from portfolios of different ICs. It may be possible for staff to carry out this analysis but using a ML algorithm would require a fraction of the staff time needed for this and be more consistent. While it may be possible for staff to classify the topics in the relatively small HEAL portfolio without AI, having a ML method in place will allow a single staff member to compare the HEAL portfolio to the much larger NIH portfolio using consistent methods.
Detailed example
Network diagram showing different topics of research in the HEAL portfolio, how "related" these topics are to eachother and how many grants fall into each topic.
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
This will allow us to describe various portfolios that consist of 100s or 1000s of grants without requiring the time of dozens of staff members. The output will allow staff members to communicate summaries of their porfolios consistently and clearly.
Controls / human review
ATO: Not reported; PIA: Not published