Hepatotoxicidad por iopromida

Concejo, F. Bolado; Bastons, F. Capdevila; Urmeneta, José Manuel Zozaya; Higuera, B. González de la; Sanchotena, J. L. García

doi:10.4321/s1130-01082008000600017

Cited by 3 publications

(2 citation statements)

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“…On the other hand, only three previous adult reports describe an acute elevation of liver enzymes subsequent to the injection of a non-ionic contrast medium [6][7][8]. The time elapsed from exposure to iodinated contrast media in these reports ranged from a few hours (iopromide) to 19 days (iopamidol).…”

Section: Discussionmentioning

confidence: 85%

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Severe liver and renal injuries following cerebral angiography: late life-threatening complications of non-ionic contrast medium administration

2015

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Section: Discussionmentioning

confidence: 85%

“…Liver injury is another rare late complication of the administration of non-ionic contrast medium reported in only a few adult cases [6][7][8] but not described in the pediatric population. We hereby describe a rare case of a child presenting both liver and renal injuries following the injection of a non-ionic contrast medium.…”

Section: Introductionmentioning

confidence: 98%

Severe liver and renal injuries following cerebral angiography: late life-threatening complications of non-ionic contrast medium administration

2015

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Radiological contrast agents

Aronson¹

2010

Side Effects of Drugs Annual

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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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Hepatotoxicidad por iopromida

Cited by 3 publications

References 1 publication

Severe liver and renal injuries following cerebral angiography: late life-threatening complications of non-ionic contrast medium administration

Severe liver and renal injuries following cerebral angiography: late life-threatening complications of non-ionic contrast medium administration

Radiological contrast agents

Prediction of adverse drug reactions based on knowledge graph embedding

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