The EU‐ADR Web Platform: delivering advanced pharmacovigilance tools

Oliveira, José Luís; Lopes, Pedro; Nunes, Tiago; Campos, David; Boyer, Scott; Ahlberg, Ernst; Mulligen, Erik M. van; Kors, Jan A.; Singh, Bharat; Furlong, Laura I.; Sanz, Ferrán; Bauer-Mehren, Anna; Carrascosa, Maria C.; Mestres, Jordi; Avillach, Paul; Diallo, Gayo; Acedo, Carlos Diaz; Lei, Johan van der

doi:10.1002/pds.3375

Cited by 38 publications

(35 citation statements)

References 37 publications

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“…There is also washout for exposure and outcome in the 183 days prior to but not including the study entry date. There are two windows during which covariates are assessed, covariates [1][2][3][4][5] are defined in the 90 days prior to but not including the study index date whereas covariates 6-25 are defined in the 183 days prior to but not including the index date. There is an induction period following study entry so follow up for the outcome begins on day 30 and continues until a censoring mechanism is met.…”

Section: Designmentioning

confidence: 99%

“…1,2 The importance and influence of such "real world" evidence is demonstrated by commitment of governments around the world to develop infrastructure and technology to increase the capacity for use of these data in comparative effectiveness and safety research as well as health technology assessments. [3][4][5][6][7][8][9][10][11][12] Research conducted using healthcare databases currently suffers from a lack of transparency in reporting of study details. [13][14][15][16] This has led to high profile controversies over apparent discrepancies in results and reduced confidence in evidence generated from healthcare databases.…”

Section: Introductionmentioning

confidence: 99%

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Reporting to Improve Reproducibility and Facilitate Validity Assessment for Healthcare Database Studies V1.0

et al. 2017

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Purpose: Defining a study population and creating an analytic dataset from longitudinal healthcare databases involves many decisions. Our objective was to catalogue scientific decisions underpinning study execution that should be reported to facilitate replication and enable assessment of validity of studies conducted in large healthcare databases. This article is a joint publication by Pharmacoepidemiology and Drug Safety and Value in Health. Methods:This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reporting to Improve Reproducibility and Facilitate Validity Assessment for Healthcare Database Studies V1.0

et al. 2017

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“…caBIG [29], BRIDG [30], I2B2 [31], STRIDE [32], R-oogle [33]), and several recent projects are of interest for data warehousing, sharing and analysing of heterogeneous clinical dataset (e.g. DebugIT to improve detection and elimination of bacteria [34], EU-ADR to improve detection of adverse drug events [35] and EHR4CR to improve clinical trials recruitment [36]). Recognition pattern algorithm and CBR are promising methods for the secondary use of data, for instance in an hospital information system by automatically identifying new eligible patients to the clinical trials going on at the hospital (ASTEC project [37]), or by automatically detecting new healthcare-associated infections [38].…”

Section: Discussionmentioning

confidence: 99%

Improving Case-Based Reasoning Systems by Combining K-Nearest Neighbour Algorithm with Logistic Regression in the Prediction of Patients’ Registration on the Renal Transplant Waiting List

et al. 2013

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IntroductionCase-based reasoning (CBR) is an emerging decision making paradigm in medical research where new cases are solved relying on previously solved similar cases. Usually, a database of solved cases is provided, and every case is described through a set of attributes (inputs) and a label (output). Extracting useful information from this database can help the CBR system providing more reliable results on the yet to be solved cases.ObjectiveWe suggest a general framework where a CBR system, viz. K-Nearest Neighbour (K-NN) algorithm, is combined with various information obtained from a Logistic Regression (LR) model, in order to improve prediction of access to the transplant waiting list.MethodsLR is applied, on the case database, to assign weights to the attributes as well as the solved cases. Thus, five possible decision making systems based on K-NN and/or LR were identified: a standalone K-NN, a standalone LR and three soft K-NN algorithms that rely on the weights based on the results of the LR. The evaluation was performed under two conditions, either using predictive factors known to be related to registration, or using a combination of factors related and not related to registration.Results and ConclusionThe results show that our suggested approach, where the K-NN algorithm relies on both weighted attributes and cases, can efficiently deal with non relevant attributes, whereas the four other approaches suffer from this kind of noisy setups. The robustness of this approach suggests interesting perspectives for medical problem solving tools using CBR methodology.

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“…The problem is felt worldwide, with initiatives stemming from international entities such as the European union’s EU-ADR (Oliveira et al, 2013) and PROTECT 1 . Responses in US have included the FDA’s Sentinel Initiative and Mini-Sentinel, the Observational Medical Outcomes Partnership (OMOP) 2 , the Reagan-Udall Foundation (RUF), and the Innovation in Medical Evidence Development and Surveillance (IMEDS) 3 arm of RUF that incorporates and builds upon content from the earlier OMOP and Mini-Sentinel.…”

Section: Introductionmentioning

confidence: 99%

Markov logic networks for adverse drug event extraction from text

et al. 2016

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Adverse drug events (ADEs) are a major concern and point of emphasis for the medical profession, government, and society. A diverse set of techniques from epidemiology, statistics, and computer science are being proposed and studied for ADE discovery from observational health data (e.g., EHR and claims data), social network data (e.g., Google and Twitter posts), and other information sources. Methodologies are needed for evaluating, quantitatively measuring, and comparing the ability of these various approaches to accurately discover ADEs. This work is motivated by the observation that text sources such as the Medline/Medinfo library provide a wealth of information on human health. Unfortunately, ADEs often result from unexpected interactions, and the connection between conditions and drugs is not explicit in these sources. Thus, in this work we address the question of whether we can quantitatively estimate relationships between drugs and conditions from the medical literature. This paper proposes and studies a state-of-the-art NLP-based extraction of ADEs from text.

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The EU‐ADR Web Platform: delivering advanced pharmacovigilance tools

Cited by 38 publications

References 37 publications

Reporting to Improve Reproducibility and Facilitate Validity Assessment for Healthcare Database Studies V1.0

Reporting to Improve Reproducibility and Facilitate Validity Assessment for Healthcare Database Studies V1.0

Improving Case-Based Reasoning Systems by Combining K-Nearest Neighbour Algorithm with Logistic Regression in the Prediction of Patients’ Registration on the Renal Transplant Waiting List

Markov logic networks for adverse drug event extraction from text

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