Signaling potential adverse drug reactions from administrative health databases
Date
2010
Authors
Jin, Huidong (Warren)
Chen, Jie
He, Hongxing
Kelman, Chris
McAullay, Damien
O'Keefe, Christine
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IEEE Computer Society
Abstract
The work is motivated by real-world applications of detecting Adverse Drug Reactions (ADRs) from administrative health databases. ADRs are a leading cause of hospitalization and death worldwide. Almost all current postmarket ADR signaling techniques are based on spontaneous ADR case reports, which suffer from serious underreporting and latency. However, administrative health data are widely and routinely collected. They, especially linked together, would contain evidence of all ADRs. To signal unexpected and infrequent patterns characteristic of ADRs, we propose a domain-driven knowledge representation Unexpected Temporal Association Rule (UTAR), its interestingness measure, unexlev, and a mining algorithm MUTARA (Mining UTARs given the Antecedent). We then establish an improved algorithm, HUNT, for highlighting infrequent and unexpected patterns by comparing their ranks based on unexlev with those based on traditional leverage. Various experimental results on real-world data substantiate that both MUTARA and HUNT can signal suspected ADRs while traditional association mining techniques cannot. HUNT can reliably shortlist statistically significantly more ADRs than MUTARA (p=0.00078). HUNT, e.g., not only shortlists the drug alendronate associated with esophagitis as MUTARA does, but also shortlists alendronate with diarrhoea and vomiting for older (age ≥ 60) females. We also discuss signaling ADRs systematically by using HUNT.
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Keywords
Keywords: Adverse drug reactions; Alendronate; Association mining; Case reports; Esophagitis; Health data; Improved algorithm; Interestingness measures; Mining algorithms; Mining methods and algorithms; Real world data; Real-world application; Signaling techniques; Association rules; Medicine and science; Mining methods and algorithms
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IEEE Transactions on Knowledge and Data Engineering
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Journal article
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2037-12-31
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