Proof‑of‑concept for using machine learning to facilitate data extraction for human health chemical assessments: a study protocol
Artificial intelligence (AI) and machine learning (ML) methods have rapidly advanced and show promise in achieving operational efficiencies in chemical assessment workflows by supporting automated or semi-automated SR methods, possibly improving the user experience. Yet incorporating advances into sustainable workflows has remained a challenge. Whether using a tool like Dextr improves operational efficiencies and the user experience remains to be determined.
Authors
- Kristina A. Thayer (ORCID: https://orcid.org/0000-0002-3616-618X)
- Elizabeth G. Radke (ORCID: https://orcid.org/0000-0002-2504-2405)
- Michelle Angrish (ORCID: https://orcid.org/0000-0002-4956-4806)
- Andrew A. Rooney (ORCID: https://orcid.org/0000-0002-1756-5185)
- Andrew J. Shapiro (ORCID: https://orcid.org/0000-0002-5233-8092)
- Artur Nowak (ORCID: https://orcid.org/0000-0001-7862-5797)
- Teresa Shannon
- Brittany Schulz (ORCID: https://orcid.org/0000-0002-3815-668X)
- Krista Christensen (ORCID: https://orcid.org/0000-0003-4431-336X)
- Glenn Rice
- Vickie R. Walker (ORCID: https://orcid.org/0009-0001-6711-071X)
- Sean Watford (ORCID: https://orcid.org/0000-0003-0888-5029)
- Michele Taylor (ORCID: https://orcid.org/0000-0001-5049-0499)
- Amanda S. Persad (ORCID: https://orcid.org/0000-0003-0137-1279)
- A. Amina Wilkins (ORCID: https://orcid.org/0000-0001-9292-833X)
- Allison L. Phillips
Institutions
- Oak Ridge Associated Universities (US)
- Environmental Protection Agency (US)
- National Institute of Environmental Health Sciences (US)
Publication Details
- Journal
- Evidence-Based Toxicology
- Published
- 2024-11-11
- DOI
- https://doi.org/10.1080/2833373x.2024.2421192
- Citations
- 1
- Primary Topic
- Geochemistry and Geologic Mapping
- Type
- article
- Field-Weighted Citation Impact
- 0.35
Funders
- U.S. Environmental Protection Agency