Mining clinical text for signals of adverse drug-drug interactions

Iyer, Srinivasan; Harpaz, Rave; LePendu, Paea; Bauer-Mehren, Anna; Shah, Nigam H.

doi:10.1136/amiajnl-2013-001612

Cited by 135 publications

(127 citation statements)

References 49 publications

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“…In other cases, NLP is implemented using basic text search of a list of 'key words' identified by the authors and subsequent analysis of the set of terms extracted with Boolean logic [150,151], disproportionality analysis [152], contingency tables, [153] logistic regression [154], and classification methods [155]. Fields of applications include EHR-data driven phenotyping, ADR signaling, and the assessment of effects of mood instability on clinical outcomes.…”

Section: F Extraction Of Information From Unstructured Clinical Datamentioning

confidence: 99%

Clinical Data Reuse or Secondary Use: Current Status and Potential Future Progress

Meystre¹,

Lovis²,

Bürkle³

et al. 2017

Yearb Med Inform

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SummaryObjective: To perform a review of recent research in clinical data reuse or secondary use, and envision future advances in this field. Methods: The review is based on a large literature search in MEDLINE (through PubMed), conference proceedings, and the ACM Digital Library, focusing only on research published between 2005 and early 2016. Each selected publication was reviewed by the authors, and a structured analysis and summarization of its content was developed. Results: The initial search produced 359 publications, reduced after a manual examination of abstracts and full publications. The following aspects of clinical data reuse are discussed: motivations and challenges, privacy and ethical concerns, data integration and interoperability, data models and terminologies, unstructured data reuse, structured data mining, clinical practice and research integration, and examples of clinical data reuse (quality measurement and learning healthcare systems). Conclusion: Reuse of clinical data is a fast-growing field recognized as essential to realize the potentials for high quality healthcare, improved healthcare management, reduced healthcare costs, population health management, and effective clinical research.

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Section: F Extraction Of Information From Unstructured Clinical Datamentioning

confidence: 99%

Clinical Data Reuse or Secondary Use: Current Status and Potential Future Progress

Meystre¹,

Lovis²,

Bürkle³

et al. 2017

Yearb Med Inform

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“…Thus, it is difficult to detect adverse drug reactions SDRs in a timely manner and some adverse drug reactions may even not be captured by those reporting system. Alternative sources of data have already been used to detect drug-adverse event associations including claims data [1], electronic medical records (EMRs) [2][3][4] and consumer search logs [5][6]. In addition, approaches have also been developed to combine SDRs from data sources such as EMRs, claims, internet search logs with SDRs from FDA Adverse Event Reporting System (FAERS) [5,[7][8].…”

Section: Introductionmentioning

confidence: 99%

Web-Based Signal Detection Using Medical Forums Data in France: Comparative Analysis (Preprint)

Kürzinger¹,

Schück²,

Texier³

et al. 2018

Preprint

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Background: While traditional signal detection methods in pharmacovigilance are based on spontaneous reports, the use of social media is emerging. The potential strength of webbased data relies on their volume and real time availability allowing early signals of disproportionate reporting (SDRs) detection.Objectives: This study aimed at assessing (1) the consistency of SDRs detected from patient's medical forums in France compared to traditional reporting systems and (2) the ability of identifying SDRs earlier than the traditional reporting systems. Methods:Messages posted on patients' forums between 2005 and 2015 were used. Eight disproportionality definitions were retained. Comparison of SDRs from the forums to SDRs detected in VigiBase® was done by describing the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, the Receiver Operating Characteristics curve and the area under the curve (AUC). Time difference in months between the detection dates of SDRs from the forums and from VigiBase® was provided. Results:Comparison analysis showed that the sensitivity ranged from 29 to 50.6%, the specificity from 86.1 to 95.5%, the PPV from 51.2 to 75.4%, the NPV from 68.5 to 91.6% and the accuracy from 68 to 87.7%. The AUC reached 0.85 when using the metric EBGM. Up to 37.5% of the SDRs were detected earlier in the forums than in VigiBase®. Conclusion:The specificity, PPV and NPV were high. The overall performance was good showing that data from medical forums may be a valuable source for signal detection. In total, up to 37.5% of the SDRs could have been detected earlier, thus facilitating increased safety of the patients. Further enhancements are needed to investigate the reliability and validation of patients' medical forums worldwide, the extension of this analysis to all possible drugs or at least to a wider selection of drugs as well as to further assess performance against established signals.

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“…For pharmacovigilance, analysis of large data sets (4,5 ) has uncovered novel adverse events (6 -8 ), off-label usage patterns (9 ), and interactions between drugs (10 ).…”

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confidence: 99%

Postmarket Surveillance of Point-of-Care Glucose Meters through Analysis of Electronic Medical Records

Schroeder¹,

Giacherio²,

Gianchandani³

et al. 2016

Clinical Chemistry

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BACKGROUND:The electronic medical record (EMR) holds a promising source of data for active postmarket surveillance of diagnostic accuracy, particularly for pointof-care (POC) devices. Through a comparison with prospective bedside and laboratory accuracy studies, we demonstrate the validity of active surveillance via an EMR data mining method [Data Mining EMRs to Evaluate Coincident Testing (DETECT)], comparing POC glucose results to near-in-time central laboratory glucose results.

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Mining clinical text for signals of adverse drug-drug interactions

Abstract: It is feasible to identify DDI signals and estimate the rate of adverse events among patients on drug combinations, directly from clinical text; this could have utility in prioritizing drug interaction surveillance as well as in clinical decision support.

Cited by 135 publications

References 49 publications

Clinical Data Reuse or Secondary Use: Current Status and Potential Future Progress

Clinical Data Reuse or Secondary Use: Current Status and Potential Future Progress

Web-Based Signal Detection Using Medical Forums Data in France: Comparative Analysis (Preprint)

Postmarket Surveillance of Point-of-Care Glucose Meters through Analysis of Electronic Medical Records

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