Detecting adverse drug events through data mining

Glasgow, Justin; Kaboli, Peter J.

doi:10.2146/ajhp090115

Cited by 10 publications

(7 citation statements)

References 12 publications

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“…In decision tree classification, a decision tree is used to predict the value of a target variable (or item) based on the observations of several input variables. Classification And Regression Tree (CART) analysis, a particular type of decision tree, has been applied to detect ADRs [171,172]. The k-Nearest Neighbors (k-NN) algorithm, another classification method, assigns an object to the most common class among its k nearest neighbors.…”

Section: G Mining Structured 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: G Mining Structured 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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show abstract

“…Another application of data mining using EHRs has been to examine and predict injuries related to the administration of a drug, otherwise referred to as adverse drug events (Glasgow & Kaboli, 2010). The authors noted that unlike other statistical techniques, which make predictions about individual cases based on the observed association between group characteristics and the events of interest, data mining uses techniques such as decision-trees and neural networks to maximize the prediction of events of interest for individual cases.…”

Section: Biomarkersmentioning

confidence: 99%

“…Based on a reanalysis of an existing adverse drug events database, data mining improved the predictive accuracy of existing automated adverse drug event detection algorithms. Data mining identified the predictive importance of multiple trigger events such as nurse/physician notes suggestive of an adverse drug event, a patient missing a dose of a medication, and/or a patient abruptly discontinuing a medication (Glasgow & Kaboli, 2010). Noren et al (2010) have proposed temporal pattern research using a graphical statistical approach to examine adverse drug events using EHRs.…”

Section: Biomarkersmentioning

confidence: 99%

“…It is unclear whether there would be differences in accuracy of classification/detection of adverse drug events between the data mining approach advocated by Glasgow and Kaboli (2010) and the graphical statistical approach to exploring temporal associations used by Noren et al (2010). Nonetheless, Glasgow and Kaboli have demonstrated that data mining can be used to improve the monitoring and detection of adverse drug events.…”

Section: Biomarkersmentioning

confidence: 99%

“…The data mining approaches described here could also be applied to identify risks associated with various mental illnesses. For instance, the approach of Glasgow and Kaboli (2010) could be used to examine adverse drug events such as drug overdose and side-effects for medications used to treat psychiatric illnesses. The studies employing phenome-wide association scans analysis also demonstrate the potential to data mine EHRs to gain a better understanding of the genetic basis for various psychiatric illnesses.…”

Section: Summary Of Data Mining Applications and Implications For Menmentioning

confidence: 99%

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Increasing knowledge of mental illness through secondary research of electronic health records: opportunities and challenges

Spiranovic

Matthews

Scanlan

et al. 2015

Advances in Mental Health

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Aim/Purpose: The primary use of electronic health records (EHRs) is in the care of the individual patient. Secondary research uses employ information in EHRs for purposes beyond that of care of the individual. Secondary research uses may broadly be divided into studies which focus on improving care and treatment of individuals and those which aim to increase knowledge about disease causes, associations and prevalence at a population level. This paper provides a review of studies that have used EHRs to increase knowledge at a population level. It examines the methods used, types of research conducted, difficulties and challenges faced and implications for future research and mental health research in particular. Method: A review was undertaken based on a search for peer-reviewed and recently (i.e. since 2005) published articles with full-text available online. Findings/Results: The studies which have used EHRs to increase knowledge have predominantly involved; (1) data mining to identify biomarkers and gene-disease associations, (2) epidemiological research using linked/merged health records and (3) surveillance, prediction and alerts for diseases/illnesses. The principal methodological challenges identified were data quality, discrepancies/inconsistencies in data and interoperability of EHRs. Conclusions: Despite the challenges faced in secondary usage of EHRs, significant research has been undertaken and researchers have proposed and tested various approaches to address methodological issues. The study methods employed in other fields of medical research can be extrapolated to study issues of significance to mental health using EHRs.

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Medication Reconciliation

Kaboli¹,

Fernandes²

2012

Arch Intern Med

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Detecting adverse drug events through data mining

Cited by 10 publications

References 12 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

Increasing knowledge of mental illness through secondary research of electronic health records: opportunities and challenges

Medication Reconciliation

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