Application of the Apriori algorithm for adverse drug reaction detection

Studies in Health Technology and Informatics

Volumn 148, Issue , 2009, Pages 95-101

  Kuo, M H ^a   Kushniruk, A W ^a   Borycki, E M ^a   Greig, D ^a  

^a UNIVERSITY OF VICTORIA   (Canada)

Author keywords

Adverse drug reactions; Data mining; Pharmacovigilance

Indexed keywords

ASSOCIATION REACTIONS; DATA MINING; DIAGNOSIS; HUMAN ENGINEERING; LEARNING ALGORITHMS;

ADVERSE DRUG REACTIONS; ADVERSE EVENTS; APRIORI ALGORITHMS; ASSOCIATION ANALYSIS; CO-MORBID CONDITIONS; DATA SET; HEALTH CARE DATUM; PHARMACOVIGILANCE;

PHARMACODYNAMICS;

EID: 71749092341     PISSN: 09269630     EISSN: 18798365     Source Type: Book Series    
DOI: 10.3233/978-1-60750-043-8-95     Document Type: Conference Paper

References (14)

1. DuMouchel, W., Fram, D., Yang, X., Mahmoud, R. A., Grogg, A. L., Engelhart, E. L. & Ramaswamy, K. (2008). Antipsychotics, Glycemic Disorders, and Life-Threatening Diabetic Events: A Bayesian Data-Mining Analysis of the FDA Adverse Event Reporting System (1968-2004), Annals of Clinical Psychiatry, 20(1), 21-31.
2. Matsushita Matsushita, Y., Kuroda, Y., Niwa, S., Sonehara, S., Hamada, C. & Yoshimura, I. (2007). Criteria Revision and Performance Comparison of Three Methods of Signal Detection Applied to the Spontaneous Reporting Database of a Pharmaceutical Manufacturer. Drug Safety; 30(8), 715-726.
3. Hauben, M., Horn, S. & Reich, L. (2007). Potential Use of Data-Mining Algorithms for the Detection of 'Surprise' Adverse Drug Reactions. Drug Safety; 30(2), 143-155.
4. Almenoff, J. S., LaCroix, K. K., Yuen, N. A., Fram, D. & DuMouchel, W. (2006). Comparative Performance of Two Quantitative Safety Signalling Methods - Implications for Use in a Pharmacovigilance Department. Drug Safety; 29(10), 875-87.
5. Hauben, M., Patadia, V., Gerrits, C., Walsh, L. and Reich, L. (2005). Data Mining in Pharmacovigilance - The Need for a Balanced Perspective. Drug Safety; 28(10), 835-842.
6. Hauben, M. & Zhou, X. (2003). Quantitative Methods in Pharmacovigilance. Drug Safety; 26(3), 159-186.
7. Szarfman, A., Machado, S. G. & O'Neill, R. T. (2002). Use of Screening Algorithms and Computer Systems to Efficiently Signal Higher-Than-Expected Combinations of Drugs and Events in the US FDA's Spontaneous Reports Database. Drug Safety; 25(6), 381-392.
8. Evans, S. J. W., Waller, P. C. & Davis, S. (2001). Use of proportional reporting ratios (PRRs) for signal generation from spontaneous adverse drug reaction reports. Pharmacoepidemiology. Drug Safety; 10, 483-86.
9. DuMouchel, W. & Pregibon D. (2001). Empirical Bayes screening for multi-item associations. In: Conference on knowledge discovery in data. Proceedings of the Seventh ACM SIGKDD Inter national Conference on Knowledge Discovery and Data Mining; San Francisco (CA). New York: ACM Press, 67-76.
10. Bate, A., Lindquist, M., Edwards, I.R., et al. (1998). A Bayesian neural network method for adverse drug reaction signal generation. Eur J Clin Pharmacol; 54, 315-21.
11. Koh,Y., Yap, C. W. & Li, S.C. (2008). A quantitative approach of using genetic algorithm in designing a probability scoring system of an adverse drug reaction assessment system. International Journal of Medical Informatics, 7(7), 421-430
12. Tan, P. N., Steinbach, M. & Kumar, V. (2006). Introduction to Data Mining. Addison-Wesley.
13. Agrawal, R. & Srikant, R. (1994). Fast Algorithms for Mining Association Rules, VLDB, Proceedings of the 20th VLDB Conference, Santiago, Chile, 487-99.
14. Kotsiantis, S. & Kanellopoulos, D. (2006). Association Rules Mining: A Recent Overview, GESTS International Transactions on Computer Science and Engineering. 32 (1), 71-82