| アブストラクト | 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 adjudication, GPM identified 595 unique NP names and SM 504. There was little overlap between candidates identified by each (Non-overlapping: GPM 347, SM 248). We identified a total of 686 novel NP names from FAERS reports. Including these names in the FAERS collection yielded 3,486 additional reports mentioning NPs. |
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| ジャーナル名 | Scientific reports |
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| Pubmed追加日 | 2024/1/14 |
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| 投稿者 | Dilan-Pantojas, Israel O; Boonchalermvichien, Tanupat; Taneja, Sanya B; Li, Xiaotong; Chapin, Maryann R; Karcher, Sandra; Boyce, Richard D |
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| 組織名 | Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, USA.;iod4@pitt.edu.;Intelligent Systems Program, University of Pittsburgh, Pittsburgh, USA.;School of Pharmacy, University of Pittsburgh, Pittsburgh, USA. |
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| Pubmed リンク | https://www.ncbi.nlm.nih.gov/pubmed/38218987/ |
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