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Using semantics to scale up evidence-based chemical risk-assessments
Catherine Blake1, Jodi A Flaws2
1School of Information Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America.
Plos One
|December 15, 2021
Summary
Automated risk assessment tools can now extract and classify evidence supporting, refuting, or neutral to outcomes. This approach provides transparent, reproducible data for public policy decisions, overcoming limitations of manual methods.
Area of Science:
- Toxicology
- Computational Biology
- Public Health
Background:
- Manual risk assessment processes struggle to keep pace with the growing volume of available data.
- Automated methods offer a promising solution but require transparency for public policy applications.
Purpose of the Study:
- To develop an automated approach for evidence extraction and synthesis in risk assessment.
- To characterize evidence as supporting, refuting, or neutral regarding specific outcomes.
Main Methods:
- Combined knowledge resources and natural language processing to interpret scientific abstracts.
- Resolved coordinated ellipses and ontological concept differences for accurate evidence extraction.
- Employed explicit search, inclusion, and exclusion criteria, akin to systematic reviews.
Main Results:
- The system processed 482,000 abstracts across 27 chemicals.
- For cancer risk assessment endpoints, refuting evidence was highest for cell proliferation (45.9%) and general cell changes (37.7%).
- Cell death was the only endpoint with a majority of supporting evidence (61.3%).
Conclusions:
- Automated evidence extraction provides crucial directionality, preventing misleading conclusions from simple retrieval counts.
- Visualizing supporting, neutral, and refuting evidence ensures transparency and reproducibility for public policy.
- The approach enhances decision-making by offering a nuanced understanding of chemical risk evidence.
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