GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: Transcribing Cognitive Interviews with Whisper
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: CDC
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: Federal Health Survey Research. Specifically, Cognitive interviews are a form of qualitative research where interviewers ask probing questions to understand how the interviewee understands a concept which is being asked about in a federal survey. These interviews are a key part of survey design to identify any potential misconceptions and prevent lengthy and hard to answer questions which could create potentially wrong health statistics. These interviews are time consuming to perform and report as they are inherently individual and require extensive hours to transcribe. By using AI to transcribe the interviews, researchers can potentially cut down on the hours or qualitative review, increasing the speed of research publication and quality of survey questions used on federal surveys.

Without transcripts, these interviews have to be listened to repeatedly taking at least the same amount of time as the full length of the interview to find important pieces of information for their work. These transcripts can greatly reduce the burden on researchers by enabling immediate comparison of concepts and answers along with timestamps created for researchers to reference in review.
12_outputs: This AI generates transcripts from recorded interviews for staff to use when performing qualitative research to assist with Federal Health Survey research.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 10/1/2022
19_date_acq_dev_began: 7/1/2024
20_date_implemented:  
21_date_retired:  
22_dev_method: Developed in-house.
23_contract_piids:  
24_hisp_support:  
25_hisp_name:  
26_public_service:  
27_public_info:  
28_iqa_compliance:  
29_contains_pii:  
30_saop_review:  
31_data_catalog: No
33_agency_data: None, publicly available data was used to evaluate performance
34_data_docs: Documentation has been partially completed
37_custom_code:  
38_code_access:  
39_code_link:  
40_has_ato: No
41_system_name:  
42_dev_tools_wait: More than 12 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: No
49_existing_reuse: Re-use production level code from a different use-case: This use case re-uses production level code and/or internally developed code libraries for model development from existing AI systems or repositories inside 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.
2_use_case_name: Use of Natural Language Processing for Topic Modeling to Automate Review of Public Comments to Notice of Proposed Rulemaking
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: CDC
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: This use case is aimed to assist in the review of public comments for Notices of Proposed Rulemaking. All responses are manually reviewed following this tool usage.

By law, Public comments are required to be manually review for any notice of public rule making. These comments are highly labor intensive due to both the quantity and wide range of potential opinions shared by the public. These comments can range in both length of response and quantity serving as a challenge for properly ensuring the insights from the public are accurately gathered in the review process. By using an LLM based Topic Modeling approach, manual reviewers can organize responses based upon theme to enhance both the insights gained, the speed of manual review, and ensure all topics mentioned can be accurately reported in all review processes. This process can use open source python libraries and open-source LLMs to complete different actions. This AI methodology enables manual review processes to be more effective in gaining insights from the public, better organized in themes and categories present within text, and reduce the burden of manual review of comments from notices of proposed rulemaking required by law.
12_outputs: Generates clusters of similar public comments to a 'notice of proposed rulemaking' to aid in manual review.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 2/1/2023
19_date_acq_dev_began: 4/1/2023
20_date_implemented:  
21_date_retired:  
22_dev_method: Developed in-house.
23_contract_piids:  
24_hisp_support:  
25_hisp_name:  
26_public_service:  
27_public_info:  
28_iqa_compliance:  
29_contains_pii:  
30_saop_review:  
31_data_catalog: No
34_data_docs: Documentation has been partially completed
37_custom_code:  
38_code_access:  
39_code_link:  
40_has_ato: Yes
41_system_name: Enterprise Data, Analytics, and Visualization Platform
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: None: This use case does not re-use any internally developed tooling or managed infrastructure from any other AI development efforts 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.
2_use_case_name: Center for Forecasting and Outbreak Analytics Disease Modeling
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: CDC
8_topic_area: Health & Medical
10_commercial_ai: None of the above.
11_purpose_benefits: Federal and State, Tribal, Local, and Territorial health department responses to disease outbreaks depends on being able to predict the future behavior of a given outbreak as well as the potential impact of different responses to the outbreak. This project aims to build models of disease spread that allow for (1) determining if and where infection/impact rates are growing or increasing (2) forecasting future disease progression (3) modeling specific scenarios such as the impact of vaccination.

We use model output to provide information to Federal and SLTT decision makers to improve disease response. This allows those decision makers to determine where and how to direct resources in a disease out break as well as the impact of potential response actions. This kind of insight is critically dependent on mathematical modeling.
12_outputs: This project  models disease spread and impact based upon a set of models, a variety of data sources, and allows for the inclusion of the impact of potential public health actions such as vaccinations. These insights can be used by public health decision makers to inform current and future responses to public health emergencies
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 4/1/2022
19_date_acq_dev_began: 4/1/2022
20_date_implemented:  
21_date_retired:  
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: 75D30123F16628
24_hisp_support:  
25_hisp_name:  
26_public_service:  
27_public_info: No
28_iqa_compliance:  
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: A wide array of public health data including hospital admissions data from NHSN, emergency department visit data from NSSP, case reports provided to CDC from local jurisdictions, variant prevalence data sourced from public health laboratory samples, wastewater data from NWSS, and other related sources.
34_data_docs: Documentation is complete
37_custom_code: Yes
38_code_access: Yes – source code is publicly available.
39_code_link: https://github.com/cdcgov/dynode; https://github.com/cdcgov/ixa -- note, not all code is currently public
40_has_ato: Yes
41_system_name: Enterprise Data Analytics and Visualization (EDAV) Platform, EDAV-EXT, 1 CDC Data Platform (1CDP)
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: Re-use production level code from a different use-case: This use case re-uses production level code and/or internally developed code libraries for model development from existing AI systems or repositories inside the agency.
50_internal_review: Documentation has been published: Complete documentation has been published to a repository or data catalog within the agency and is made accessible to other data science teams for review and feedback.
2_use_case_name: Leveraging AI for Metadata Tagging for Enterprise Data Catalog of CDC
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: CDC
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: CDC’s Enterprise Data Catalog provides an internal single centralized inventory of available datasets within CDC staff. It provides data management and search tools to help users find what they need and aims to provide descriptions to aid in relevance determinations for users. This is from the appropriate metadata, e.g. project title, description, or other important information describing the dataset. Because of the previous manual metadata tagging process, many datasets catalogued do not have the appropriate tags to help staff determine the relevance of any particular dataset available within CDC for particular tasks.

Because of the lack of standardization and gaps in metadata fields, this AI's output will greatly increase the speed for applying the appropriate metadata fields for each dataset within the catalogue to increase the usability of the product. This reduces the time and effort required for manual tagging and improves the consistency and completeness of metadata. 
12_outputs: AI is used in this case to generate suggested metadata fields based upon existing metadata information provided by a dataset. These tags would then be used for staff accessing the enterprise data catalogue to discover datasets and find connections to support existing on-going work.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 6/1/2024
19_date_acq_dev_began: 6/1/2024
20_date_implemented:  
21_date_retired:  
22_dev_method: Developed in-house.
23_contract_piids:  
24_hisp_support:  
25_hisp_name:  
26_public_service:  
27_public_info: No
28_iqa_compliance:  
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: Enterprise Data Catalog Metadata fields
34_data_docs: Documentation has been partially completed
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
39_code_link:  
40_has_ato: Yes
41_system_name: Enterprise Data Analytics and Visualization (EDAV) Platform
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
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.
2_use_case_name: Malaria parasites DNA barcode geography classification
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: CDC
8_topic_area: Health & Medical
10_commercial_ai: None of the above.
11_purpose_benefits: Epidemiological investigations of Malaria

To complement epidemiologic investigations of domestic malaria cases, including cases that were domestically acquired. This information can help assess the origin of the malaria strain in question, which can help us understand how the strain entered the US. For more information, please see the following manuscript: https://journals.asm.org/doi/full/10.1128/aac.01203-24
12_outputs: Algorithm examines a sequence barcode/genotype (our use case is for malaria, but it can be applied to other contexts) and it assigns the malaria parasite genotype to a geographic origin, which may be a continent or some other geographic regional category. While this use case has the barcode as a set of genetic loci from malaria parasites, and the categories are geographic (i.e., continents, subregions), the algorithm could be trained to assign genetic barcodes from other pathogens to any number of different categories assuming the appropriate training dataset exists.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 7/1/2023
19_date_acq_dev_began: 7/1/2023
20_date_implemented:  
21_date_retired:  
22_dev_method: Developed in-house.
23_contract_piids:  
24_hisp_support:  
25_hisp_name:  
26_public_service:  
27_public_info: No
28_iqa_compliance:  
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
33_agency_data: Data used are a mixture of data generated at CDC and other data available publicly. All CDC data are accessible via the following links: https://www.ncbi.nlm.nih.gov/bioproject/PRJNA428490/, https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1092573/, https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA1110244. Travel histories from case patients were used to assess performance of the model to compare the generated output of the model and are available in the following manuscript: https://journals.asm.org/doi/full/10.1128/aac.01203-24. Non-CDC owned data are available through this link: https://apps.malariagen.net/apps/pf7/
34_data_docs: Documentation is widely available
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
39_code_link:  
40_has_ato: Yes
41_system_name: Advanced Molecular Detection Scientific Computing Platform
42_dev_tools_wait: 6-12 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: Use of existing data platforms: This use case is being developed on existing enterprise data and analytics platforms within the agency rather than procuring additional platforms or SaaS to operate.
50_internal_review: Documentation has been published: Complete documentation has been published to a repository or data catalog within the agency and is made accessible to other data science teams for review and feedback.
2_use_case_name: The School Closure Awareness System
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: CDC
8_topic_area: Emergency Management
10_commercial_ai: None of the above.
11_purpose_benefits: Identifying unplanned school closures. CDC is responsible for supporting schools before, during, and after and emergency. During COVID 19 this included monitoring school closure or virtual status and reporting this to federal partners, the White House, and the public. Due to cost the process used during COVID was no longer feasible and ceased in DEC 2022. We explored multiple ways to capture this data and our current system met the need best. 

This AI has allowed us to save nearly 2-million-dollars from contracting fees and decrease human work hours by 200 hours. It has made the capture of some of this data possible after cessation of the process used during COVID 19 which we could no longer support. The AI supported system is faster and captures more information about individual closures than the original human-powered process.
12_outputs: This system uses publicly available Facebook posts from approximately 40,000 school or district Facebook accounts. Then uses a large language model to categorize posts as unplanned school closure for several types of events (weather, health, facility issues, safety). It also will denote the type of status change (full closure, virtual, hybrid, or early / late dismissal). The data is checked and recoded (if needed) by a staff person every 24 hours.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 7/1/2022
19_date_acq_dev_began: 11/1/2022
20_date_implemented:  
21_date_retired:  
22_dev_method: Developed in-house.
23_contract_piids:  
24_hisp_support:  
25_hisp_name:  
26_public_service:  
27_public_info: No
28_iqa_compliance:  
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
34_data_docs: Documentation has been partially completed
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
39_code_link:  
40_has_ato: Yes
41_system_name: Enterprise Data, Analytics, and Visualization Platform
42_dev_tools_wait: More than 12 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: Use of existing data platforms: This use case is being developed on existing enterprise data and analytics platforms within the agency rather than procuring additional platforms or SaaS to operate.
50_internal_review: Documentation has been published: Complete documentation has been published to a repository or data catalog within the agency and is made accessible to other data science teams for review and feedback.
2_use_case_name: A reusable NLP pipeline for clinical narratives preprocessing and characterization
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: CDC
8_topic_area: Health & Medical
10_commercial_ai: None of the above.
11_purpose_benefits: Data cleaning of Clinical narratives from health records. At scale, clinical narratives are a dataset available to CDC currently underutilized as there is not a structured pipeline to identify structured information output. We are currently working on setting up a process to create a pipeline that cleans data markup (html, rtf, xml) and identifies signs and symptoms using pre-trained Named Entity Recognition (NER) tasks from relevant models published in peer review journals. 





The use of these pretrained models significantly reduces  the amount of time a human to spend tagging data and provides additional features and increased consistency from unstructured data for identification of more novel insights. 
12_outputs: The objective of this project is to take unstructured data, clean it, and use pretrained Named Entity Recognition (NER) tools to extract signs/symptoms in deidentified electronic health records that can be used to characterize a cohort or make further analysis.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 7/1/2024
20_date_implemented:  
21_date_retired:  
22_dev_method:  
23_contract_piids:  
24_hisp_support:  
25_hisp_name:  
26_public_service:  
27_public_info:  
28_iqa_compliance:  
29_contains_pii:  
30_saop_review:  
31_data_catalog:  
33_agency_data:  
34_data_docs:  
37_custom_code:  
38_code_access:  
39_code_link:  
40_has_ato:  
41_system_name:  
43_infra_provisioned:  
45_compute_request:  
49_existing_reuse:  
50_internal_review:
2_use_case_name: Autocoding to Support Adverse Drug Event Surveillance
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: CDC
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: The model will help surveillance epidemiologists working at CDC to determine whether reported potential adverse drug events in the National Electronic Injury Surveillance System Cooperative Adverse Drug event Surveillance meet the agency's surveillance case definition for the same.

It will speed up coding of the drug event reports, helping epidemiologists produce prevalence estimates for the surveillance system.
12_outputs: The model will take a de-identified free-text description of the patient's emergency department visit encounter, along with a handful of other pre-coded variables, and output the probability that the encounter meets the case definition.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 5/1/2024
20_date_implemented:  
21_date_retired:  
22_dev_method:  
23_contract_piids:  
24_hisp_support:  
25_hisp_name:  
26_public_service:  
27_public_info:  
28_iqa_compliance:  
29_contains_pii:  
30_saop_review:  
31_data_catalog:  
33_agency_data:  
34_data_docs:  
37_custom_code:  
38_code_access:  
39_code_link:  
40_has_ato:  
41_system_name:  
43_infra_provisioned:  
45_compute_request:  
49_existing_reuse:  
50_internal_review:
2_use_case_name: Automating LIMS Bioinformatics Workflow Configuration and Enhancing Lab Quality Management with AI
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: CDC
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: The tool proposed will impact the laboratory bioinformatics workflow configuration within the Core Laboratory's Genomics Sequencing Lab (GSL). Specifically, it will improve the efficacy with Clarity LIMS for tracking and processing Next Generation Sequencing (NGS) samples. This includes the creation and customization of bioinformatics workflows in the Laboratory Information Management System, which is currently a time-consuming and expertise-intensive task for bioinformaticians and laboratory scientists.

The RAG system can potentially reduce the time required to configure Clarity LIMS workflows, enabling rapid deployment which is particularly crucial during pandemics or outbreak responses. By simplifying the workflow customization process through natural language inputs, it lowers the barrier to entry for laboratory scientists and bioinformaticians who may not be familiar with XML scripting. The system also enhances team learning and training by providing easy access to relevant documents on quality management and regulations, ensuring that all team members are well-informed about best practices.
12_outputs: We propose to use a Retrieval-Augmented Generation (RAG) framework for two primary functions: It converts natural language lab protocols into precise XML workflows that are compatible with Clarity LIMS. This conversion process involves understanding technical documentations and generating corresponding script code. In addition, it serves as an interactive knowledge base for documentation of Clarity LIMS and Laboratory Quality Management and Regulations. 
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 1/1/2024
20_date_implemented:  
21_date_retired:  
22_dev_method:  
23_contract_piids:  
24_hisp_support:  
25_hisp_name:  
26_public_service:  
27_public_info:  
28_iqa_compliance:  
29_contains_pii:  
30_saop_review:  
31_data_catalog:  
33_agency_data:  
34_data_docs:  
37_custom_code:  
38_code_access:  
39_code_link:  
40_has_ato:  
41_system_name:  
43_infra_provisioned:  
45_compute_request:  
49_existing_reuse:  
50_internal_review: