Proof-of-concept for using machine learning to facilitate data extraction for human health chemical assessments: a study protocol.

ABSTRACT:

Background

Systematic review (SR) methods are relied upon to develop transparent, unbiased, and standardized human health chemical assessments. The expectation is that these assessments will have discovered and evaluated all of the available information in a trackable, transparent, and reproducible manner inherent to SR principles. The challenge is that chemical assessment development relies on mostly literature-based data using manual approaches that are not scalable. Various SR tools have increased the efficiency of assessment development by implementing semi-automated approaches (human in the loop) for data discovery (literature search and screening) and enhanced data repositories with standardized data collection and curation frameworks. Yet filling these repositories with data

SUBMITTER: Angrish M 

PROVIDER: S-EPMC12004489 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature