Broadening the capture of natural products mentioned in FAERS using fuzzy string-matching and a Siamese neural network.

ABSTRACT: Increased sales of natural products (NPs) in the US and growing safety concerns highlight the need for NP pharmacovigilance. A challenge for NP pharmacovigilance is ambiguity when referring to NPs in spontaneous reporting systems. We used a combination of fuzzy string-matching and a neural network to reduce this ambiguity. Our aim is to increase the capture of reports involving NPs in the US Food and Drug Administration Adverse Event Reporting System (FAERS). For this, we utilized Gestalt pattern-matching (GPM) and Siamese neural network (SM) to identify potential mentions of NPs of interest in 389,386 FAERS reports with unmapped drug names. A team of health professionals refined the candidates identified in the previous step through manual review and annotation. After candidate adjudicati

SUBMITTER: Dilan-Pantojas IO 

PROVIDER: S-EPMC10787736 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature