Calidad estructural de las bases de datos de interacciones

Rodríguez-Terol, A.; Caraballo, Meaghanne; Palma, David A.; Santos-Ramos, Bernardo; Molina, Teresa; Desongles, T.; Aguilar, A.

doi:10.1016/s1130-6343(09)71155-9

Cited by 26 publications

(16 citation statements)

References 25 publications

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“…Particularly, children admitted to EDs have critical medical conditions that make them more susceptible to the administration of multiple drugs, to complex treatment regimens, to long ED stays and to care by different specialist physicians for consultations [ 19 – 20 ]. There are more than 54 different softwares for DDIs assessment [ 28 ], but according to a systematic review that included articles of children and adults between 1976 and 2014, the most widely used are the Drug-Relax software from Micromedex® Healthcare Series, Drug Interactions Facts®, Lexi-Interact®, Pharmavista®, EpocratesRx®, MediQ® and Drug interaction checker® [ 29 ]. The reason why in this study the Medscape Drug Interaction Checker software was selected is because we consider it to be highly likely to be used in clinical practice owing to its simple and free access in addition to being easy to use and having good structural quality [ 28 ].…”

Section: Discussionmentioning

confidence: 99%

Potential drug-drug interactions and their risk factors in pediatric patients admitted to the emergency department of a tertiary care hospital in Mexico

et al. 2018

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BackgroundDrug-drug interactions (DDIs) detected in a patient may not be clinically apparent (potential DDIs), and when they occur, they produce adverse drug reactions (ADRs), toxicity or loss of treatment efficacy. In pediatrics, there are only few publications assessing potential DDIs and their risk factors. There are no studies in children admitted to emergency departments (ED). The present study estimates the prevalence and describes the characteristics of potential DDIs in patients admitted to an ED from a tertiary care hospital in Mexico; in addition, potential DDI-associated risk factors are investigated.MethodsA secondary analysis of data from 915 patients admitted to the ED of the Hospital Infantil de México “Federico Gómez” was conducted. The Medscape Drug Interaction Checker software was used to identify potential DDIs. The results are expressed as number of cases (%), means (95% CI) and medians (25-75th percentiles). Count data regressions for number of total and severity-stratified potential DDIs were performed adjusting for patient characteristics, number of administered drugs, days of stay, presence of ADRs and diagnoses.ResultsThe prevalence of potential DDIs was 61%, with a median of 4 (2–8). A proportion of 0.2% of potential DDIs was “Contraindicated”, 7.5% were classified as “Serious”, 62.8% as “Significant” and 29.5% as “Minor”. Female gender, age, days of stay, number of administered drugs and diagnoses of Neoplasms (C00-D48), Congenital malformations (Q00-Q99), Diseases of the Blood, Blood-forming Organs and Immunity (D50-D89) and Diseases of the nervous system (G00-G99) were significantly associated with potential DDIs.ConclusionThe prevalence of potential DDIs in the ED is high, and strategies should therefore be established to monitor patients’ safety during their stay, in addition to conducting investigations to estimate the real harm potential DDIs inflict on patients.

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Section: Discussionmentioning

confidence: 99%

Potential drug-drug interactions and their risk factors in pediatric patients admitted to the emergency department of a tertiary care hospital in Mexico

et al. 2018

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“…Although health care professionals can perform DDI extraction using different databases, those being used currently are rarely complete because their update periods can be as long as three years [38].…”

Section: Resultsmentioning

confidence: 99%

Extracting Drug-Drug Interaction from the Biomedical Literature Using a Stacked Generalization-Based Approach

Yang

Zhao

et al. 2013

PLoS ONE

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Drug-drug interaction (DDI) detection is particularly important for patient safety. However, the amount of biomedical literature regarding drug interactions is increasing rapidly. Therefore, there is a need to develop an effective approach for the automatic extraction of DDI information from the biomedical literature. In this paper, we present a Stacked Generalization-based approach for automatic DDI extraction. The approach combines the feature-based, graph and tree kernels and, therefore, reduces the risk of missing important features. In addition, it introduces some domain knowledge based features (the keyword, semantic type, and DrugBank features) into the feature-based kernel, which contribute to the performance improvement. More specifically, the approach applies Stacked generalization to automatically learn the weights from the training data and assign them to three individual kernels to achieve a much better performance than each individual kernel. The experimental results show that our approach can achieve a better performance of 69.24% in F-score compared with other systems in the DDI Extraction 2011 challenge task.

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“…Although health care professionals are supported by different DDI databases, the update periods of these databases are generally three years. Therefore, these databases are rarely complete [3]. Since drug interactions are frequently reported in clinical pharmacology journals and technical reports, a major source of detecting DDIs is the exponential increase in biomedical literature [4].…”

Section: Introductionmentioning

confidence: 99%

A Single Kernel-Based Approach to Extract Drug-Drug Interactions from Biomedical Literature

et al. 2012

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When one drug influences the level or activity of another drug this is known as a drug-drug interaction (DDI). Knowledge of such interactions is crucial for patient safety. However, the volume and content of published biomedical literature on drug interactions is expanding rapidly, making it increasingly difficult for DDIs database curators to detect and collate DDIs information manually. In this paper, we propose a single kernel-based approach to extract DDIs from biomedical literature. This novel kernel-based approach can effectively make full use of syntactic structural information of the dependency graph. In particular, our approach can efficiently represent both single subgraph topological information and the relation of two subgraphs in the dependency graph. Experimental evaluations showed that our single kernel-based approach can achieve state-of-the-art performance on the publicly available DDI corpus without exploiting multiple kernels or additional domain resources.

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Calidad estructural de las bases de datos de interacciones

Cited by 26 publications

References 25 publications

Potential drug-drug interactions and their risk factors in pediatric patients admitted to the emergency department of a tertiary care hospital in Mexico

Potential drug-drug interactions and their risk factors in pediatric patients admitted to the emergency department of a tertiary care hospital in Mexico

Extracting Drug-Drug Interaction from the Biomedical Literature Using a Stacked Generalization-Based Approach

A Single Kernel-Based Approach to Extract Drug-Drug Interactions from Biomedical Literature

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