Name
Abstract | ||
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In this paper1, we investigated the feasibility of applying deep learning methods to the detection of adverse drug reactions (ADRs) using spontaneous reporting systems (SRS) data. We adopted Convolutional Neural Network (CNN) to extract automatically appropriate features from the FAERS data with the help of a well-known ADR knowledge base, SIDER, to establish a model for future ADR detection for newly marketed drugs. Seven kinds of drugs not listed in SIDER that may cause myocardial infarction from FDA's safety report were considered. We use the instances that recorded these seven drugs as testing sets and detect them by our proposed CNN models. Our results show that if we consider adverse reactions in HLT level of MedDRA, the ADR signals detected by our models were far earlier than the FDA's alerts, also ahead of the time detected by conventional statistics-based approaches.
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Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3341105.3374068 | SAC '20: The 35th ACM/SIGAPP Symposium on Applied Computing
Brno
Czech Republic
March, 2020 |
Keywords | DocType | ISBN |
Adverse drug reaction, convolutional neural network, deep learning, pharmacovigilance, spontaneous reporting system | Conference | 978-1-4503-6866-7 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chenghao Wang | 1 | 17 | 2.44 |
| Wen-Yang Lin | 2 | 399 | 35.72 |