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Signaling Potential Adverse Drug Reactions from Administrative Health Databases
June 2010 (vol. 22 no. 6)
pp. 839-853
ASCII Text
x 
 
Huidong (Warren) Jin, Jie Chen, Hongxing He, Chris Kelman, Damien McAullay, Christine M. O'Keefe, "Signaling Potential Adverse Drug Reactions from Administrative Health Databases," IEEE Transactions on Knowledge and Data Engineering, vol. 22, no. 6, pp. 839-853, June, 2010.
 
 
BibTex
x
 
@article{ 10.1109/TKDE.2009.212,
author = {Huidong (Warren) Jin and Jie Chen and Hongxing He and Chris Kelman and Damien McAullay and Christine M. O'Keefe},
title = {Signaling Potential Adverse Drug Reactions from Administrative Health Databases},
journal ={IEEE Transactions on Knowledge and Data Engineering},
volume = {22},
number = {6},
issn = {1041-4347},
year = {2010},
pages = {839-853},
doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2009.212},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Knowledge and Data Engineering
TI - Signaling Potential Adverse Drug Reactions from Administrative Health Databases
IS - 6
SN - 1041-4347
SP839
EP853
EPD - 839-853
A1 - Huidong (Warren) Jin,
A1 - Jie Chen,
A1 - Hongxing He,
A1 - Chris Kelman,
A1 - Damien McAullay,
A1 - Christine M. O'Keefe,
PY - 2010
KW - Association rules
KW - mining methods and algorithms
KW - medicine and science.
VL - 22
JA - IEEE Transactions on Knowledge and Data Engineering
ER -
 
Huidong (Warren) Jin, CSIRO Mathematics, Informatics and Statistics (CMIS), Canberra
Jie Chen, CSIRO Land and Water, Canberra
Hongxing He, CSIRO Mathematics, Informatics and Statistics (CMIS), Canberra
Chris Kelman, The Australian National University, Canberra
Damien McAullay, The Australian Government, Woden
Christine M. O'Keefe, CSIRO Mathematics, Informatics and Statistics (CMIS), Canberra
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 ({\rm age} \ge 60) females. We also discuss signaling ADRs systematically by using HUNT.

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Index Terms:
Association rules, mining methods and algorithms, medicine and science.
Citation:
Huidong (Warren) Jin, Jie Chen, Hongxing He, Chris Kelman, Damien McAullay, Christine M. O'Keefe, "Signaling Potential Adverse Drug Reactions from Administrative Health Databases," IEEE Transactions on Knowledge and Data Engineering, vol. 22, no. 6, pp. 839-853, June 2010, doi:10.1109/TKDE.2009.212
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