Immune-mediated Drug-induced Liver Injury Caused by Laninamivir Octanoate Hydrate

Kawaguchi, Toshihiro; Arinaga‐Hino, Teruko; Shimizu, Mitsuru; Tanikawa, Ken; Tokushige, Takahiko; Hirai, Setsuro; Nagamatsu, Hiroaki; Tateishi, Hideo; Takata, Akio; Ide, Tatsuya; Torimura, Takuji

doi:10.2169/internalmedicine.2740-19

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…Ten of the first 20 articles reported on liver injury caused by drugs. Two papers reported on related drugs that did not appear in our KG [18,19], so we were unable to predict the ADRs for these drugs. We used our model to score the liver injuryrelated ADRs for the drugs in the remaining seven articles.…”

Section: Literature Evidencementioning

confidence: 99%

Prediction of adverse drug reactions based on knowledge graph embedding

Zhang

Sun

Diao

et al. 2021

BMC Med Inform Decis Mak

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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 graph embedding method that embeds drugs and ADRs into their respective vectors and builds a logistic regression classification model to predict whether a given drug will have ADRs. Result First, a new knowledge graph embedding method was proposed, and comparison with similar studies showed that our model not only had high prediction accuracy but also was simpler in model structure. In our experiments, the AUC of the classification model reached a maximum of 0.87, and the mean AUC was 0.863. Conclusion In this paper, we introduce a new method to embed knowledge graph to vectorize drugs and ADRs, then use a logistic regression classification model to predict whether there is a causal relationship between them. The experiment showed that the use of knowledge graph embedding can effectively encode drugs and ADRs. And the proposed ADRs prediction system is also very effective.

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Section: Literature Evidencementioning

confidence: 99%