Machine learning coupled with causal inference to identify COVID-19 related chemicals that pose a high concern to drinking water.

ABSTRACT: Various synthetic substances were utilized in large quantities during the recent coronavirus pandemic, COVID-19. Some of these chemicals could potentially enter drinking water sources. Persistent, mobile, and toxic (PMT) substances have been recognized as a threat to drinking water resources. It has not yet been assessed how many COVID-19 related substances could be considered PMT substances. One reason is the lack of high-quality experimental data for the identification of PMT substances. To solve this problem, we applied a machine learning model to identify the PMT substances among COVID-19 related chemicals. The optimal model achieved an accuracy of 90.6% based on external test data. The model interpretation and causal inference indicated that our approach understood causation between P

SUBMITTER: Han M 

PROVIDER: S-EPMC10863329 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature