HHS · no agency ID · record R1007

Identifying pain vs non-pain research

Department of Health and Human Services · HHS/NIH

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

Identifying pain research studies is complicated because of the ubiquity of pain in language. NIH program staff working in the pain research field need to be able to distinguish pain research from opioid research, both of which get classified as "Pain Research" by RCDC. This usually requires significant investment of staff time, however, since a large enough training dataset has been developed we are trying to train various ML algorithms to be able to identify grants that are truly researching pain compared to grants that merely mention pain in their text.

Expected benefits

It is expected that this will decrease the amount of staff time needed to curate a portfolio as starting point of analyses and that it will speed up the time it takes to carryout an analysis.

System outputs

Expected output is a file with a list of grants that were identified as pain related.

Technology

AI classification: Natural language processingSource: Natural Language Processing: AI that processes, interprets, and shares information in human language.
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.

Pre-deployment testingNo answer reported

AI impact assessmentNo answer reported

Independent reviewNo answer reported

Ongoing monitoringNo answer reported

Operator trainingNo answer reported

Fail-safeNo answer reported

Appeal processNo answer reported

User and public consultationNo answer reported

Potential impacts and how they were identified

Not reported