HEAL Portfolio Topic Analysis

Overview

Development stage

In development or acquisitionSource: a) Pre-deployment – The use case is in a development or acquisition status.

High-impact designation

Not high-impactSource: c) Not high-impact

Impact justification

Not reported

Start date

Not reported

Withheld from public reporting

NoSource: a) No

Mission

Topic area

Science

Operational functions

No function tag matched Derived by keyword rules; see methodology

Problem the AI is intended to solve

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.

Expected benefits

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.

System outputs

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.

Technology

AI classification

Classical / predictive MLSource: Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relatio…

System name(s)

Not reported

Custom-developed code

Not reported

Public source code

Not reported

Sourcing

How it was built

Not reported

Vendor (as reported)

Not reported

Vendors (standardized)

None on the standard list

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII

Not reported

Privacy Impact Assessment

Not reported

Authorization to Operate

Not reported

Demographic features

Not reported

Training and evaluation data

Not reported

Federal Data Catalog

Not reported

Governance

0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Potential impacts and how they were identified

Not reported