Mining multi-item drug adverse effect associations in spontaneous reporting systems

Harpaz, Rave; Chase, Herbert; Friedman, Carol

doi:10.1186/1471-2105-11-s9-s7

Cited by 142 publications

(135 citation statements)

References 25 publications

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“…The safety and efficacy profile of a drug can be altered significantly by the co-administration of other drugs. Multiple drug combinations in therapy are common 11 and increase the risk of adverse events since concomitant drugs can share pharmacological or metabolic pathways. In extreme cases, some drugs have caused death due to the heightened adverse effect of the drug affected by the interaction.…”

Section: Introductionmentioning

confidence: 99%

Drug—drug interaction through molecular structure similarity analysis

Vilar

Harpaz

Uriarte

et al. 2012

J Am Med Inform Assoc

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207

151

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Background Drugedrug interactions (DDIs) are responsible for many serious adverse events; their detection is crucial for patient safety but is very challenging. Currently, the US Food and Drug Administration and pharmaceutical companies are showing great interest in the development of improved tools for identifying DDIs. Methods We present a new methodology applicable on a large scale that identifies novel DDIs based on molecular structural similarity to drugs involved in established DDIs. The underlying assumption is that if drug A and drug B interact to produce a specific biological effect, then drugs similar to drug A (or drug B) are likely to interact with drug B (or drug A) to produce the same effect. DrugBank was used as a resource for collecting 9454 established DDIs. The structural similarity of all pairs of drugs in DrugBank was computed to identify DDI candidates. Results The methodology was evaluated using as a gold standard the interactions retrieved from the initial DrugBank database. Results demonstrated an overall sensitivity of 0.68, specificity of 0.96, and precision of 0.26. Additionally, the methodology was also evaluated in an independent test using the Micromedex/Drugdex database. Conclusion The proposed methodology is simple, efficient, allows the investigation of large numbers of drugs, and helps highlight the etiology of DDI. A database of 58 403 predicted DDIs with structural evidence is provided as an open resource for investigators seeking to analyze DDIs.

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Section: Introductionmentioning

confidence: 99%

Drug—drug interaction through molecular structure similarity analysis

Vilar

Harpaz

Uriarte

et al. 2012

J Am Med Inform Assoc

Self Cite

207

151

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“…The literature contains a number of standard class association rule mining solutions in biomedical domain to discover patterns in medical data [90][91][92][93][94]. For instance, Yang et al [95] adopt Apriori and support and confidence as the main underlying measures to mine meaningful patterns.…”

Section: Discussion and Implications Of Findingsmentioning

confidence: 99%

Decision support methods for community-driven knowledge curation platforms in the biomedical domain

Paul¹

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The skeletal dysplasia domain is characterised by highly complex, heterogeneous and sparse data.In this domain, the analysis and interpretation of new patient cases relies on comparisons to past case studies due to the absence of defined guidelines and the lack of mature domain knowledge for this group of genetic disorders. In order to carry out the analysis and interpretation of new patient cases, practitioners currently query many heterogeneous data sources and aggregate diverse types of data. This integration represents a significant challenge due to the extreme heterogeneity of the data models, metadata schemas, vocabularies and data formats and inconsistencies in naming and identification conventions. Therefore, there is an urgent need to investigate the development of medical decision support systems which can assist clinicians and researchers to improve their understanding of the causes, behaviours, symptoms and attributes of the diseases and assist them in the decision-making process (e.g., diagnosis).In the skeletal dysplasia domain, the absence of mature domain knowledge and the lack of documented and well-structured past cases, in addition to the general sparseness of skeletal dysplasia data, hinder the development of reliable decision support methods. In this thesis, the first step is taken towards developing a decision support framework in order to assist clinicians, doctors and researchers in this domain.The developed framework is a phenotype-disorder lifecycle to assist practitioners in finalising patient cases by going from phenotypes to disorders and vice versa. The framework was developed in four phases and combines ontological techniques with inductive and statistical reasoning techniques. In the initial phase, the underlying data characteristics were analyzed and a novel machine learning approach was developed to produce probabilistic candidate rankings, which can serve as support for medical decision-making (e.g., diagnosis). In the second phase, the semantics encoded in the domain were exploited in order to find possible disorders for a new patient case by developing a semantic similarity-based approach. In the third phase, to discover the implicit relationships between different ontological concepts (e.g., phenotypes and disorders), semantic similarity methods, formulated using the intrinsic structure of a given ontology, were combined with traditional interestingness measures in the process of discovering association rules. The final phase proposes a data mining approach for discovering characteristic features in the context of a set of disorders.From the data and domain knowledge perspective, the developed approaches and evaluation strategies led to a number of significant findings towards building a fully-fledged decision support framework; (i) the properties of data in the domain are rareness, sparseness, and high dimensionality; (ii) the domain knowledge introduced more noise compared to the noise produced ii by patient cases; and (iii) semantic similarity improves the overall...

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“…We discuss the study performed in [11] and the observations of [30] on using association rules for mining ADE databases.…”

Section: Association Rules and Adverse Drug Reactionsmentioning

confidence: 99%

“…The simplest association rule describing an ADE has the form Vioxx → heart attack and involves one drug and one reactions. Clearly, rule of this form cannot capture ADE that result from undesirable drug interactions and this is the focus of [11]. This study is based on a set of 162,744 reports of suspected ADEs reported to AERS and published in the year 2008.…”

Section: Association Rules and Adverse Drug Reactionsmentioning

confidence: 99%

“…A sample of multi-item ADE associations found in [11] is: Since each metformin and metoprolol cause nausea, association rules of the form metformin metoprolol → NAUSEA is forseable. The rule cyclophosphamide prednisone vincristine → FEBRILE NEUTROPENIA involves a drug combination used in cancer treatment and describes a known complication.…”

Section: Association Rules and Adverse Drug Reactionsmentioning

confidence: 99%

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Data Mining of Medical Data: Opportunities and Challenges in Mining Association Rules

Hyun¹,

Paslack²,

Stolte³

Transdisciplinary Interfaces and Innovation in the Life Sciences

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The beginning clinical clerk, the house officer and the practicing physician are all confronted with conditions that are frustrating in every phase of medical action. ... To deal effectively with these frustrations it will be necessary to develop a more organized approach to the medical record, a more rational acceptance and use of the paramedical personnel and a more positive attitude about the computer in medicine. L. L. Weed: Medical records that guide and teach, New England Journal of Medicine, 1968 Abstract Association rules represent knowledge embedded in data sets as probabilistic implications and are intimately related to computation of frequent item sets. We survey applications of frequent item sets and association rules in medical practice in such areas as nosocomial infections, adverse drug reactions, and the interplay between co-morbidities and the lack of transitivity of association rules.To make this survey as self-content as possible we present in an appendix the Fisher exact test and the χ 2 -test, enumeration of subsets, frequent item sets and the Apriori algorithm, and combinatorial properties of association rules.

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Mining multi-item drug adverse effect associations in spontaneous reporting systems

Cited by 142 publications

References 25 publications

Drug—drug interaction through molecular structure similarity analysis

Drug—drug interaction through molecular structure similarity analysis

Decision support methods for community-driven knowledge curation platforms in the biomedical domain

Data Mining of Medical Data: Opportunities and Challenges in Mining Association Rules

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