Using Machine-Learning to Facilitate Data Extraction for Human Health Chemical Assessments: A Proof-of-Concept Protocol
Using Machine-Learning to Facilitate Data Extraction for Human Health Chemical Assessments: A Proof-of-Concept Protocol
Artificial intelligence (AI) methods including natural language processing, active learning, and large language models are expected to provide workflow advances to reduce risk assessors' time and effort while maintaining the accuracy necessary to meet demand for chemical assessments. A growing suite of modular software applications that integrate AI methods and leverage semi-automated workflows are making operationalization of these advancements feasible. This manuscript prvides a protocol for a proof-of-concept case study with a new semi-automated data extraction tool (Dextr) incorporated into a chemical assessment workflow. The semi-automated data extraction tool will be used to create a literature inventory that will support the development of a Provisional Peer-Reviewed Toxicity Value (PPRTV) assessment for 1,3-dinitrobenzene (DNB).
- National Institutes of Health United States
- National Institute of Environmental Health Sciences United States
- Department of Climate, Energy and the Environment Ireland
- Environmental Protection Agency Ireland
- Environmental Protection Agency United States
standardization, systematic evidence map, machine learning, systematic review, FAIR principles, toxicity, open data, hazard identification, artificial intelligence
standardization, systematic evidence map, machine learning, systematic review, FAIR principles, toxicity, open data, hazard identification, artificial intelligence
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