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Prediction of adverse drug reactions based on knowledge graph embedding
2021
BMC Medical Informatics and Decision Making
Background Adverse drug reactions (ADRs) are an important concern in the medication process and can pose a substantial economic burden for patients and hospitals. Because of the limitations of clinical trials, it is difficult to identify all possible ADRs of a drug before it is marketed. We developed a new model based on data mining technology to predict potential ADRs based on available drug data. Method Based on the Word2Vec model in Nature Language Processing, we propose a new knowledge
doi:10.1186/s12911-021-01402-3
pmid:33541342
fatcat:k5xmla2psvazbmxq7j6agsxo44