Show simple item record

Files in this item


Item metadata

dc.contributor.authorMontvida, Olga
dc.contributor.authorArandelovic, Ognjen
dc.contributor.authorReiner, Edward
dc.contributor.authorPaul, Sanjoy K.
dc.identifier.citationMontvida , O , Arandelovic , O , Reiner , E & Paul , S K 2017 , ' Data mining approach to estimate the duration of drug therapy from longitudinal electronic medical records ' , Open Bioinformatics Journal , vol. 10 .
dc.identifier.otherPURE: 250098892
dc.identifier.otherPURE UUID: c6963b37-4879-402f-8338-40c48a841cb5
dc.identifier.otherScopus: 85030775986
dc.description.abstractBackground: Electronic Medical Records (EMRs) from primary/ ambulatory care systems present a new and promising source of information for conducting clinical and translational research. Objectives: To address the methodological and computational challenges in order to extract reliable medication information from raw data which is often complex, incomplete and erroneous. To assess whether the use of specific chaining fields of medication information may additionally improve the data quality. Methods: Guided by a range of challenges associated with missing and internally inconsistent data, we introduce two methods for the robust extraction of patient-level medication data. First method relies on chaining fields to estimate duration of treatment (“chaining”), while second disregards chaining fields and relies on the chronology of records (“continuous”). Centricity EMR database was used to estimate treatment duration with both methods for two widely prescribed drugs among type 2 diabetes patients: insulin and glucagon-like peptide-1 receptor agonists. Results: At individual patient level the “chaining” approach could identify the treatment alterations longitudinally and produced more robust estimates of treatment duration for individual drugs, while the “continuous” method was unable to capture that dynamics. At population level, both methods produced similar estimates of average treatment duration, however, notable differences were observed at individual-patient level. Conclusion: The proposed algorithms explicitly identify and handle longitudinal erroneous or missing entries and estimate treatment duration with specific drug(s) of interest, which makes them a valuable tool for future EMR based clinical and pharmaco-epidemiological studies. To improve accuracy of real-world based studies, implementing chaining fields of medication information is recommended.
dc.relation.ispartofOpen Bioinformatics Journalen
dc.rights© 2017 Montvida et al. Open-Access License: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: ( This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.en
dc.subjectElectronic medical recordsen
dc.subjectTreatment durationen
dc.subjectData miningen
dc.subjectType 2 diabetesen
dc.subjectRule-based algorithmen
dc.subjectPatient-level data aggregationen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectRM Therapeutics. Pharmacologyen
dc.titleData mining approach to estimate the duration of drug therapy from longitudinal electronic medical recordsen
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews. School of Computer Scienceen
dc.description.statusPeer revieweden

This item appears in the following Collection(s)

Show simple item record