2_use_case_name: Automated Basic-Applied Categorization of extramural grants
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: NIH
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: None of the above.
11_purpose_benefits: This machine-learning algorithm is in development. Currently it uses information on NIMH-funded extramural research projects to categorize them as basic or applied research per the federal definitions for each. The data it is trained on is supplied from extramural grant applications and the same fields will be used for subsequent classification of new grants. For new grants, the algorithm will propose a categorization which can be reviewed, confirmed, or edited by an NIMH staff member. The resulting information will be used for internal, analytical purposes and annual reporting of NIMH-funded basic and applied research. The AI will NOT influence funding decisions, require or use PII, or use any other sensitive, personal, or health information.

The algorithm is intended to be consistent in identifying basic and applied research, reduce burden of review by NIMH staff, and provide a complementary perspective to human review.
12_outputs: The algorithm produces categorization of basic and applied research that is used for internal analysis and annual reporting. The algorithm is trained on the following data from extramural grants from fiscal years (FY) 2000-2022 and will use the same for grants in FY2025 and going forward to propose a categorization of basic or applied research: Program Class Code, NIMH Division, NIH Research, Condition, and Disease Categorization (RCDC), Human Subjects Codes, Animal Subjects Code, Clinical Trial NIMH Division, text of grant title, abstracts, specific aims. These data categories are all obtained from NIH IMPACII and will be applied to 100% of NIMH extramural grants, excluding L activity codes. This constitutes approximately 1,500 new grants per FY. Algorithm performance will be assessed each year, comparing the proposed classifications to a manually reviewed sample, and input received from NIMH staff regarding edits to proposed classifications. The frequency of processing is yet to be determined. No anonymization or de-identification processes are employed as personal information is used neither to train, nor classify grants using this algorithm.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 6/1/2022
19_date_acq_dev_began: 11/1/2022
22_dev_method: Developed in-house.
31_data_catalog: No
33_agency_data: The algorithm is trained on the following data from extramural grants from fiscal years (FY) 2000-2022 and will use the same for grants in FY2025 and going forward to propose a categorization of basic or applied research: Program Class Code, NIMH Division, NIH Research, Condition, and Disease Categorization (RCDC), Human Subjects Codes, Animal Subjects Code, Clinical Trial, NIMH Division, text of grant title, abstracts, and specific aims. These data are all obtained from NIH IMPACII and will be applied to 100% of NIMH extramural grants, excluding L activity codes. This constitutes approximately 1,500 new grants per FY. No anonymization or de-identification processes are employed as personal information is used neither to train nor classify grants using this algorithm.
34_data_docs: Documentation is complete
40_has_ato: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: No
49_existing_reuse: Re-use production level code and/or data products: This use case re-uses production level code as well as data products that have been published or made widely available for data science teams for within the agency.
50_internal_review: Limited documentation for review: Some information regarding the model’s performance on specific benchmarks are available or have been shared with specific stakeholders within the agency.