Mining of Consumer Product Ingredient and Purchasing Data to Identify Potential Chemical Coexposures.

ABSTRACT:

Background

Chemicals in consumer products are a major contributor to human chemical coexposures. Consumers purchase and use a wide variety of products containing potentially thousands of chemicals. There is a need to identify potential real-world chemical coexposures to prioritize in vitro toxicity screening. However, due to the vast number of potential chemical combinations, this identification has been a major challenge.

Objectives

We aimed to develop and implement a data-driven procedure for identifying prevalent chemical combinations to which humans are exposed through purchase and use of consumer products.

Methods

We applied frequent itemset mining to an integrated data set linking consumer product chemical ingredient data with product purchasing data from

SUBMITTER: Stanfield Z 

PROVIDER: S-EPMC8221370 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature