| 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. We aim to increase the capture of reports involving NPs in the US Food and Drug Administration Adverse Event Reporting System (FAERS). Gestalt pattern-matching (GPM) and Siamese neural network (SM) were used to identify potential mentions of NPs of interest in 389,386 FAERS reports with unmapped drug names. We refined the identified candidates through manual review and annotation by health professionals. After adjudication, GPM identified 595 unique NP names and SM 504. There was little overlap between candidates identified by the approaches (Non-overlapping: GPM 347, SM 248). In total, 686 novel NP names were identified in the unmapped 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 | Research square |
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| Pubmed追加日 | 2023/9/7 |
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| 投稿者 | Dilan-Pantojas, Israel; Boonchalermvichien, Tanupat; Taneja, Sanya; Li, Xiaotong; Chapin, Maryann; Karcher, Sandra; Boyce, Richard D |
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| 組織名 | University of Pittsburgh School of Medicine.;University of Pittsburgh School of Computing and Information.;University of Pittsburgh School of Pharmacy.;University of Pittsburgh. |
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| Pubmed リンク | https://www.ncbi.nlm.nih.gov/pubmed/37674723/ |
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