2_use_case_name: DEXTR - automated data extraction tool
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: NIH
8_topic_area: Health & Medical
10_commercial_ai: None of the above.
11_purpose_benefits: Dextr/LaserAI does not use PII, PHI or demographic data. Dextr is FEDRAMP certified and ATO in the cloud. Data sources for Dextr are published PDFs accessible through open source publications (e.g. PubMed Central) or through our subscriptions (e.g., NLM).

The primary objective of the DEXTR/Laser AI use case is to streamline the process of data extraction from published studies for literature reviews, addressing the key problem of time and labor intensity involved in manual data identification and capture. DEXTR leverages natural language processing (NLP) models, regular expressions and large language models to automatically identify and predict pertinent entities of data extraction within scientific studies. By doing so, it reduces the burden of manual extraction while still supporting user verification, offering a semi-automated approach that ensures accuracy and quality control. One of the key tasks DEXTR enhances is the annotation of studies during the normal workflow, allowing the tool to generate annotated corpora for the development of future models. Dextr also provides automated extraction of information from tables in PDFs expanding the supported data extraction capabilities.  As a dynamic platform for multiple AI projects, DEXTR/LaserAI is continually being optimized and expanded to support a growing number of use cases. This automation not only accelerates data extraction but also aids in creating a robust, scalable system for handling the ever-increasing volume of published research. The anticipated positive outcomes include improved efficiency and accuracy in data extraction, which is critical for developing evidence-based conclusions, guiding future research directions, and informing policy decisions. By enabling faster and more comprehensive reviews of existing literature the tool has the potential to significantly enhance the ability to process large volumes of scientific data.
12_outputs: DEXTR leverages natural language processing (NLP) models, regular expressions and large language models to automatically identify and predict pertinent entities of data extraction within scientific studies.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 8/1/2020
19_date_acq_dev_began: 8/1/2020
22_dev_method: Developed with contracting resources.
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
33_agency_data: Data sources for Dextr are published PDFs that are accessible through open source publications (e.g. PubMed Central) or through our subscriptions (e.g., NLM).
34_data_docs: Documentation is missing or not available
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: Yes
41_system_name: Dextr is FEDRAMP certified and ATO in the cloud
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: No
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: No documentation is available: No documentation detailing model performance metrics, model architecture, features and intended use of the models have been created or are currently accessible to other data science teams within the agency.