2023
DOI: 10.1007/s40264-023-01278-4
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Early Detection of Adverse Drug Reaction Signals by Association Rule Mining Using Large-Scale Administrative Claims Data

Abstract: Introduction Adverse drug reactions (ADRs) are a leading cause of mortality worldwide and should be detected promptly to reduce health risks to patients. A data-mining approach using large-scale medical records might be a useful method for the early detection of ADRs. Many studies have analyzed medical records to detect ADRs; however, most of them have focused on a narrow range of ADRs, limiting their usefulness. Objective This study aimed to identify methods for the ea… Show more

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Cited by 7 publications
(4 citation statements)
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“…This is consistent with other studies that have assessed empirical FPR of observational studies. [23][24][25][26] Schuemie et al found that uncalibrated FPR were higher than expected (median 0.1, interquartile range, 0.05 to 0.22). [14] In that study, empirical calibration was generally able to restore the expected FPR rate (median 0.05, interquartile range, 0.03 to 0.07).…”
Section: Discussionmentioning
confidence: 99%
“…This is consistent with other studies that have assessed empirical FPR of observational studies. [23][24][25][26] Schuemie et al found that uncalibrated FPR were higher than expected (median 0.1, interquartile range, 0.05 to 0.22). [14] In that study, empirical calibration was generally able to restore the expected FPR rate (median 0.05, interquartile range, 0.03 to 0.07).…”
Section: Discussionmentioning
confidence: 99%
“…[40][41][42][43][44][45] Moreover, we demonstrated the applicability of association rule mining, a rule-based machine learning method, for the early detection of adverse drug reaction signals. 46) These reports highlight the importance of integrativeresearchinthefieldsofpharmacologyandinformatics. However, clinical big data analysis cannot be applied to new compounds because the current epidemiological analysis of clinical big data is based on changes in the frequency of clinical events in the presence or absence of a drug of interest.…”
Section: Future Perspectives and Conclusionmentioning
confidence: 99%
“…Using ARM, [42] investigated whether there exist consistent patterns of clinical features and differentially expressed genes in type 2 diabetes mellitus, dyslipidemia, and periodontitis diseases. Yamamoto et al [43] applied ARM to patient symptoms and medications registered in claims data to identify Adverse drug reactions signals.…”
Section: Related Workmentioning
confidence: 99%