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Data mining approach to estimate the duration of drug therapy from longitudinal electronic medical records

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Montvida_2017_Data_mining_TOBIOIJ_CC.pdf (435.9Kb)
Date
2017
Author
Montvida, Olga
Arandelovic, Ognjen
Reiner, Edward
Paul, Sanjoy K.
Keywords
Electronic medical records
Treatment duration
Data mining
Type 2 diabetes
Rule-based algorithm
Patient-level data aggregation
QA75 Electronic computers. Computer science
RM Therapeutics. Pharmacology
NDAS
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Abstract
Background: 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.
Citation
Montvida , 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 . https://doi.org/10.2174/1875036201709010001
Publication
Open Bioinformatics Journal
Status
Peer reviewed
DOI
https://doi.org/10.2174/1875036201709010001
ISSN
1875-0362
Type
Journal article
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: (https://creativecommons.org/licenses/by/4.0/legalcode). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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  • University of St Andrews Research
URI
http://hdl.handle.net/10023/11555

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