Warp Intelligent Learning Engine (WILEE)
Description
Identify emerging chemical signals and violative food substances by analyzing a large data set in a fraction of the time that it would have taken scientific reviewers to analyze the publications.
Detailed example
A prioritize list of emerging signals and an interactive view of supporting documentation/factors.
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
Expected benefit
By enhancing signal detection and chemical hazard forecasting capabilities, this tool can help anticipate and prioritize hazards, accelerate decision making and proactively mitigate risk to consumers.
Controls / human review
ATO: Yes; PIA: Not published
Data needed
Internally generated data during the premarket review process, web data collated from web crawls and a commercial data aggregator, scientific publications retrieved with API calls, grant data published by NIH.