scholarly journals A Modified Skip-Gram Algorithm for Extracting Drug-Drug Interactions from AERS Reports

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Li Wang ◽  
Wenjie Pan ◽  
QingHua Wang ◽  
Heming Bai ◽  
Wei Liu ◽  
...  
Keyword(s):  
Adverse Event ◽  
Drug Interactions ◽  
Large Scale ◽  
Characteristic Curve ◽  
Language Model ◽  
Adverse Event Reporting System ◽  
Adverse Event Reporting ◽  
Concept Representation ◽  
Average Accuracy ◽  
Tenfold Cross Validation

Drug-drug interactions (DDIs) are one of the indispensable factors leading to adverse event reactions. Considering the unique structure of AERS (Food and Drug Administration Adverse Event Reporting System (FDA AERS)) reports, we changed the scope of the window value in the original skip-gram algorithm, then propose a language concept representation model and extract features of drug name and reaction information from large-scale AERS reports. The validation of our scheme was tested and verified by comparing with vectors originated from the cooccurrence matrix in tenfold cross-validation. In the verification of description enrichment of the DrugBank DDI database, accuracy was calculated for measurement. The average area under the receiver operating characteristic curve of logistic regression classifiers based on the proposed language model is 6% higher than that of the cooccurrence matrix. At the same time, the average accuracy in five severe adverse event classes is 88%. These results indicate that our language model can be useful for extracting drug and reaction features from large-scale AERS reports.

scholarly journals Comparison of Rhabdomyolysis Associated With Sacubitril/valsartan-related Drug-drug Interactions by Individual Statins in an Adverse Event Reporting System

2021 ◽  
Author(s):  
Tomiko Sunaga ◽  
Ryo Yonezawa
Keyword(s):  
Adverse Event ◽  
Drug Interactions ◽  
Reporting System ◽  
Case Reports ◽  
Organic Anion ◽  
Adverse Event Reporting System ◽  
Adverse Event Reporting ◽  
Reporting Odds Ratio ◽  
Event Reporting ◽  
Structured Analysis

Abstract BackgroundSacubitril/valsartan was approved in Japan recently. Sacubitril is an inhibitor of organic anion-transporting polypeptide (OATP) 1B1 and 1B3. In Japan, sacubitril/valsartan product labeling indicates that it should be cautiously co-administered with atorvastatin due to drug-drug interactions (DDIs). However, all statins are the substrates of OATP1B1 and/or 1B3. Therefore, we should be cautious about DDIs between sacubitril/valsartan and all other statins.ObjectiveTo evaluate the association between rhabdomyolysis and concomitant association of sacubitril/valsartan with atorvastatin and all other statins.MethodsCase reports from the U.S. Food and Drug Administration’s Adverse Event Reporting System (FAERS) from 2015 to Q4/2020 were used. All FAERS reports on sacubitril/valsartan were captured through a structured analysis. We compared the proportion of cases reporting the adverse events associated with rhabdomyolysis and the concomitant use of sacubitril/valsartan and atorvastatin to those with sacubitril/valsartan and all other statins.ResultsAmong 10,940 case reports on sacubitril/valsartan, compared with all other drugs, statin users were associated with increased rhabdomyolysis (reporting odds ratio =4.54[2.62-7.87]). However, compared with all other statins, atorvastatin was not associated with increased rhabdomyolysis. ConclusionsWe suggest that the co-administration of sacubitril/valsartan with atorvastatin as well as other statins should be carefully managed as it may induce rhabdomyolysis.

scholarly journals Development of an automated assessment tool for MedWatch reports in the FDA adverse event reporting system

2017 ◽  
Vol 24 (5) ◽  
pp. 913-920 ◽  
Author(s):  
Lichy Han ◽  
Robert Ball ◽  
Carol A Pamer ◽  
Russ B Altman ◽  
Scott Proestel
Keyword(s):  
Adverse Event ◽  
Random Forest ◽  
Review Process ◽  
Assessment Tool ◽  
Characteristic Curve ◽  
Adverse Event Reporting System ◽  
Adverse Event Reporting ◽  
Support Vector ◽  
Related Adverse Event ◽  
Event Information

Abstract Objective: As the US Food and Drug Administration (FDA) receives over a million adverse event reports associated with medication use every year, a system is needed to aid FDA safety evaluators in identifying reports most likely to demonstrate causal relationships to the suspect medications. We combined text mining with machine learning to construct and evaluate such a system to identify medication-related adverse event reports. Methods: FDA safety evaluators assessed 326 reports for medication-related causality. We engineered features from these reports and constructed random forest, L1 regularized logistic regression, and support vector machine models. We evaluated model accuracy and further assessed utility by generating report rankings that represented a prioritized report review process. Results: Our random forest model showed the best performance in report ranking and accuracy, with an area under the receiver operating characteristic curve of 0.66. The generated report ordering assigns reports with a higher probability of medication-related causality a higher rank and is significantly correlated to a perfect report ordering, with a Kendall’s tau of 0.24 (P = .002). Conclusion: Our models produced prioritized report orderings that enable FDA safety evaluators to focus on reports that are more likely to contain valuable medication-related adverse event information. Applying our models to all FDA adverse event reports has the potential to streamline the manual review process and greatly reduce reviewer workload.

Myopathy with DPP-4 inhibitors and statins in the real world: investigating the likelihood of drug–drug interactions through the FDA adverse event reporting system

2019 ◽  
Vol 57 (1) ◽  
pp. 71-80 ◽  
Author(s):  
Ippazio Cosimo Antonazzo ◽  
Elisabetta Poluzzi ◽  
Emanuele Forcesi ◽  
Francesco Salvo ◽  
Antoine Pariente ◽  
...  
Keyword(s):  
Adverse Event ◽  
Drug Interactions ◽  
Real World ◽  
Reporting System ◽  
Adverse Event Reporting System ◽  
Adverse Event Reporting ◽  
Event Reporting ◽  
The Real

Novel Adverse Events of Iloperidone: A Disproportionality Analysis in US Food and Drug Administration Adverse Event Reporting System (FAERS) Database

2019 ◽  
Vol 14 (1) ◽  
pp. 21-26 ◽  
Author(s):  
Viswam Subeesh ◽  
Eswaran Maheswari ◽  
Hemendra Singh ◽  
Thomas Elsa Beulah ◽  
Ann Mary Swaroop
Keyword(s):  
Data Mining ◽  
Adverse Event ◽  
Adverse Events ◽  
Reporting System ◽  
Adverse Event Reporting System ◽  
Adverse Event Reporting ◽  
Disproportionality Analysis ◽  
Positive Signal ◽  
Data Mining Algorithms ◽  
Mining Algorithms

Background: The signal is defined as “reported information on a possible causal relationship between an adverse event and a drug, of which the relationship is unknown or incompletely documented previously”. Objective: To detect novel adverse events of iloperidone by disproportionality analysis in FDA database of Adverse Event Reporting System (FAERS) using Data Mining Algorithms (DMAs). Methodology: The US FAERS database consists of 1028 iloperidone associated Drug Event Combinations (DECs) which were reported from 2010 Q1 to 2016 Q3. We consider DECs for disproportionality analysis only if a minimum of ten reports are present in database for the given adverse event and which were not detected earlier (in clinical trials). Two data mining algorithms, namely, Reporting Odds Ratio (ROR) and Information Component (IC) were applied retrospectively in the aforementioned time period. A value of ROR-1.96SE>1 and IC- 2SD>0 were considered as the threshold for positive signal. Results: The mean age of the patients of iloperidone associated events was found to be 44years [95% CI: 36-51], nevertheless age was not mentioned in twenty-one reports. The data mining algorithms exhibited positive signal for akathisia (ROR-1.96SE=43.15, IC-2SD=2.99), dyskinesia (21.24, 3.06), peripheral oedema (6.67,1.08), priapism (425.7,9.09) and sexual dysfunction (26.6-1.5) upon analysis as those were well above the pre-set threshold. Conclusion: Iloperidone associated five potential signals were generated by data mining in the FDA AERS database. The result requires an integration of further clinical surveillance for the quantification and validation of possible risks for the adverse events reported of iloperidone.

scholarly journals Systematic analysis of safety profile for darunavir and its boosted agents using data mining in the FDA Adverse Event Reporting System database

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xiaojiang Tian ◽  
Yao Yao ◽  
Guanglin He ◽  
Yuntao Jia ◽  
Kejing Wang ◽  
...  
Keyword(s):  
Data Mining ◽  
Adverse Event ◽  
Reporting System ◽  
Proportional Reporting Ratio ◽  
Adverse Event Reporting System ◽  
Adverse Event Reporting ◽  
Reporting Odds Ratio ◽  
Systematic Analysis ◽  
Event Reporting ◽  
Exfoliative Dermatitis

AbstractThis current investigation was aimed to generate signals for adverse events (AEs) of darunavir-containing agents by data mining using the US Food and Drug Administration Adverse Event Reporting System (FAERS). All AE reports for darunavir, darunavir/ritonavir, or darunavir/cobicistat between July 2006 and December 2019 were identified. The reporting Odds Ratio (ROR), proportional reporting ratio (PRR), and Bayesian confidence propagation neural network (BCPNN) were used to detect the risk signals. A suspicious signal was generated only if the results of the three algorithms were all positive. A total of 10,756 reports were identified commonly observed in hepatobiliary, endocrine, cardiovascular, musculoskeletal, gastrointestinal, metabolic, and nutrition system. 40 suspicious signals were generated, and therein 20 signals were not included in the label. Severe high signals (i.e. progressive extraocular muscle paralysis, acute pancreatitis, exfoliative dermatitis, acquired lipodystrophy and mitochondrial toxicity) were identified. In pregnant women, umbilical cord abnormality, fetal growth restriction, low birth weight, stillbirth, premature rupture of membranes, premature birth and spontaneous abortion showed positive signals. Darunavir and its boosted agents induced AEs in various organs/tissues, and were shown to be possibly associated with multiple adverse pregnant conditions. This study highlighted some novel and severe AEs of darunavir which need to be monitored prospectively.

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