MeDIAR: Multi-Drug Adverse Reactions Analytics

Qin, Xiao; Kakar, Tabassum; Wunnava, Susmitha; MacCarthy, Brian; Schade, Andrew; Tran, Huy Quoc; Zylich, Brian; Rundensteiner, Elke A.; Harrison, Lane; Sahoo, Sanjay K.; De, Suranjan

doi:10.1109/icde.2018.00177

Cited by 8 publications

(14 citation statements)

References 7 publications

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“…First, each report is converted into a two-set tuple where the first set models the suspected drugs and the other the respective events. This structured information is then fed into the Signal Generator that uses a rule learning based DIAE inference algorithm [7]…”

Section: The Deves Systemmentioning

confidence: 99%

“…However, examining the huge number of potential DIAE signals generated by a standard rule mining algorithm can be prohibitively time-consuming. We thus developed an algorithm for not only generating non-spurious signals but also establishing a ranking for the signals based on their likelihood to be promising using the contrast measure published in KDD [7]. Non-spurious signals are true representative of the reported drugs and events in the reports and the mining algorithm avoids generation of misleading signals conveying partial associations that are not fully backed by actual reports.…”

Section: Non-spurious Signal Generation and Rankingmentioning

confidence: 99%

“…DIAE signals with higher scores have a higher chance of being true signals warranting action, therefore are recommended by DEVES with higher priority to the analysts. Details on non-spurious signal generation and the contrast measure can be found in MARAS [7].…”

Section: Non-spurious Signal Generation and Rankingmentioning

confidence: 99%

“…These reports, however, may not directly capture information about drug interactions. Hence, safety reviewers either engage in the highly demanding and error-prone task of manually sifting through these reports to attempt to put together repeated patterns in their head; or more recently, computational techniques have been applied to attempt to automatically derive potential DIAE signals [3,7]. These machine generated signals, which serve as a hypothesis of potential undiscovered drug interactions, then must be validated by domain experts to assess their severity and validity before regulatory action can be taken.…”

Section: Introductionmentioning

confidence: 99%

“…DEVES aims to bridge the gap between machine learning and user-friendly visual interfaces for sense-making of the mining results. DEVES first generates and then ranks DIAE signals using rule mining from FDA's spontaneous reports [7].…”

Section: Introductionmentioning

confidence: 99%

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Deves

Kakar

Qin

Schade

et al. 2018

Proceedings of the 27th ACM International Conference on Information and Knowledge Management

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Drug-drug interaction related adverse events (DIAE) signals are a major public health issue. Drug safety analysts must sift through thousands of adverse event reports submitted daily to U.S. Food and Drug Administration (FDA) to discover unexpected DIAE signals, which if addressed can lead to life-saving actions. To facilitate the DIAE discovery from these massive data sets, we design several technological innovations that together are integrated into an interactive visual analytics system called DEVES 1. First, our stateof-the-art DIAE mining algorithm efficiently infers potential DIAE signals from these reports, and then ranks them based on their significance score. For interpretability of these inferred DIAE signals, domain knowledge of adverse events and already known drug interactions is extracted from external authoritative data sources and then seamlessly integrated with the inferred signal set. Guided by this augmented signal model, DEVES supports advanced signal analytics-empowering the analyst to interact with linked visualizations offering complementary perspectives into the signal set and its supporting evidence in the form of reports. Our demonstration showcases the technological innovations of DEVES using real-world FDA datasets, demonstrating that DEVES effectively supports the core regulatory tasks from signal screening, signal prioritization to signal validation.

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Section: The Deves Systemmentioning

confidence: 99%

Section: Non-spurious Signal Generation and Rankingmentioning

confidence: 99%

Section: Non-spurious Signal Generation and Rankingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deves

Kakar

Qin

Schade

et al. 2018

Proceedings of the 27th ACM International Conference on Information and Knowledge Management

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Identifying Drug–Drug Interactions in Spontaneous Reports Utilizing Signal Detection and Biological Plausibility Aspects

Kontsioti,

Maskell,

Anderson

et al. 2024

Clin Pharma and Therapeutics

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Translational approaches can benefit post‐marketing drug safety surveillance through the growing availability of systems pharmacology data. Here, we propose a novel Bayesian framework for identifying drug–drug interaction (DDI) signals and differentiating between individual drug and drug combination signals. This framework is coupled with a systems pharmacology approach for automated biological plausibility assessment. Integrating statistical and biological evidence, our method achieves a 16.5% improvement (AUC: from 0.620 to 0.722) with drug‐target‐adverse event associations, 16.0% (AUC: from 0.580 to 0.673) with drug enzyme, and 15.0% (AUC: from 0.568 to 0.653) with drug transporter information. Applying this approach to detect potential DDI signals of QT prolongation and rhabdomyolysis within the FDA Adverse Event Reporting System (FAERS), we emphasize the significance of systems pharmacology in enhancing statistical signal detection in pharmacovigilance. Our study showcases the promise of data‐driven biological plausibility assessment in the context of challenging post‐marketing DDI surveillance.

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Clustering-Based Hybrid Approach for Identifying Quantitative Multidimensional Associations Between Patient Attributes, Drugs and Adverse Drug Reactions

Yogita

Sangma

Anal

et al. 2020

Interdiscip Sci Comput Life Sci

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The activity of post-marketing surveillance results in a collection of large amount of data. The analysis of data is very useful for raising early warnings on possible adverse reactions of drugs. Association rule mining techniques have been heavily explored by the research community for identifying binary association between drugs and their adverse effects. But these techniques perform poorly and miss out several interesting associations when it comes to analysis of multidimensional data which may include multiple patient attributes, drugs and adverse drug reactions. In the present work, a clustering-based hybrid approach has been presented for finding quantitative multidimensional association from the large amount of data. Firstly, it employs clustering technique for segmentation of data into semantically coherent clusters. Furthermore, disproportionality method called proportional reporting ratio is applied on clustered data for generating statistically strong associations. The performance of the proposed methodology has been examined on the data taken from the U.S. Food and Drug Administration Adverse Event Reporting System database corresponding to Aspirin and nine other drugs which are prescribed along with Aspirin. The experimental results show that the proposed approach discovered a number of association rules which are very comprehensive and informative regarding relationship of patient traits and drugs with adverse drug reactions. On comparing experimental results with LPMiner, it is observed that the quantitative association rules discovered by LPMiner are just 8.3% of what have been discovered by the proposed methodology.

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MeDIAR: Multi-Drug Adverse Reactions Analytics

Cited by 8 publications

References 7 publications

Deves

Deves

Identifying Drug–Drug Interactions in Spontaneous Reports Utilizing Signal Detection and Biological Plausibility Aspects

Clustering-Based Hybrid Approach for Identifying Quantitative Multidimensional Associations Between Patient Attributes, Drugs and Adverse Drug Reactions

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