The Community for Technology Leaders
RSS Icon
Issue No.06 - June (2010 vol.22)
pp: 839-853
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.
Association rules, mining methods and algorithms, medicine and science.
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 & Data Engineering, vol.22, no. 6, pp. 839-853, June 2010, doi:10.1109/TKDE.2009.212
[1] H. Jin, J. Chen, C. Kelman, H. He, D. McAullay, and C.M. O'Keefe, "Mining Unexpected Associations for Signalling Potential Adverse Drug Reactions from Administrative Health Databases," Proc. Pacific-Asia Conf. Knowledge Discovery and Data Mining (PAKDD '06), pp. 867-876, Apr. 2006.
[2] H. Jin, J. Chen, H. He, C. Kelman, D. McAullay, and C.M. O'Keefe, "Signalling Potential Adverse Drug Reactions from Multiple Administrative Health Data," Proc. Mining Multiple Information Sources (MMIS '08), pp. 9-17, Aug. 2008.
[3] L. Cao and C. Zhang, "Domain-Driven Data Mining: A Practical Methodology," Int'l J. Data Warehousing and Mining, vol. 2, no. 4, pp. 49-65, 2006.
[4] L. Cao, C. Zhang, Q. Yang, D. Bell, M. Vlachos, B. Taneri, E. Keogh, P.S. Yu, N. Zhong, M.Z. Ashrafi, D. Taniar, E. Dubossarsky, and W. Graco, "Domain-Driven, Actionable Knowledge Discovery," IEEE Intelligent Systems, vol. 22, no. 4, pp. 78-88, July 2007.
[5] L. Cao, C. Zhang, Y. Zhao, P.S. Yu, and G. Williams, "DDDM2007: Domain Driven Data Mining," ACM SIGKDD Explorations Newsletter, vol. 9, no. 2, pp. 84-86, 2007.
[6] The ICH Expert Working Group, "Post-Approval Safety Data Management: Definitions and Standards for Expedited Reporting," ICH Harmonised Tripartite Guideline, , Nov. 2003.
[7] The Adverse Drug Reactions Advisory Committee, "A Gut Feeling for Alendronate," Australian Adverse Drug Reaction Bull., vol. 18, no. 3, p. 11, Aug. 1999.
[8] E. Roughead, "The Nature and Extent of Drug-Related Hospitalisations in Australia," J. Quality in Clinical Practice, vol. 19, no. 1, pp. 19-22, Mar. 1999.
[9] C.L. Burgess, C.D. Holman, and A.G. Satti, "Adverse Drug Reactions in Older Australians, 1981-2002," The Medical J. Australia, vol. 182, no. 6, pp. 267-270, Mar. 2005.
[10] J. Lazarou, B. Pomeranz, and P. Corey, "Incidence of Adverse Drug Reactions in Hospitalized Patients: A Meta-Analysis of Prospective Studies," The J. Am. Medical Assoc., vol. 279, no. 15, pp. 1200-1205, 1998.
[11] Compass, "Adverse Drug Reactions Waste NHS £2bn," www. , Apr. 2008.
[12] D.W. Bates et al., "Incidence of Adverse Drug Events and Potential Adverse Drug Events: Implications for Prevention," The J. Am. Medical Assoc., vol. 274, no. 1, pp. 29-34, July 1995.
[13] M. Hauben and X. Zhou, "Quantitative Methods in Pharmacovigilance: Focus on Signal Detection," Drug Safety, vol. 26, no. 3, pp. 159-186, 2003.
[14] V. Curcin, M. Ghanem, M. Molokhia, Y. Guo, and J. Darlington, "Mining Adverse Drug Reactions with E-science Workflows," Proc. IEEE Cairo Int'l Biomedical Eng. Conf. (CIBEC '08), pp. 1-5, Dec. 2008.
[15] D.W. Bates, R.S. Evans, H. Murff, P.D. Stetson, L. Pizziferri, and G. Hripcsak, "Detecting Adverse Events Using Information Technology," J. Am. Medical Informatics Assoc., vol. 10, no. 2, pp. 115-128, 2003.
[16] J. Li, A.W.-C. Fu, H. He, J. Chen, H. Jin, D. McAullay, G. Williams, R. Sparks, and C. Kelman, "Mining Risk Patterns in Medical Data," Proc. ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining (KDD '05), pp. 770-775, 2005.
[17] M. Stephens, J. Talbot, and P. Routledge, Detection of New Adverse Drug Reactions. Macmillan Reference Ltd, 1998.
[18] MedlinePlus, http:/, 2010.
[19] D.M. Fram, J.S. Almenoff, and W. DuMouchel, "Empirical Bayesian Data Mining for Discovering Patterns in Post-Marketing Drug Safety," Proc. ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining (KDD '03), pp. 359-368, 2003.
[20] A. Bate, M. Lindquist, I. Edwards, and R. Orre, "A Data Mining Approach for Signal Detection and Analysis," Drug Safety, vol. 25, no. 6, pp. 393-397, 2002.
[21] S. Evans, P. Waller, and S. Davis, "Use of Proportional Reporting Ratios for Signal Generation from Spontaneous Adverse Drug Reaction Reports," Pharmacoepidemiology and Drug Safety, vol. 10, no. 6, pp. 483-486, Oct./Nov. 2001.
[22] Australian ADR Reporting System,, 2010.
[23] H.J. Murff, V.L. Patel, G. Hripcsak, and D.W. Bates, "Detecting Adverse Events for Patient Safety Research: A Review of Current Methodologies," J. Biomedical Informatics, vol. 36, no. 1/2, pp. 131-143, 2003.
[24] P.E. Langton, G.J. Hankey, and J.W. Eikelboom, "Cardiovascular Safety of Rofecoxib (Vioxx): Lessons Learned and Unanswered Questions," The Medical J. Australia, vol. 181, no. 10, pp. 524-525, 2004.
[25] C. Kelman, S. Perason, R. Day, C. Holman, E. Kliewer, and D. Henry, "Evaluating Medicines: Let's Use All the Evidence," Medical J. Australia, vol. 186, no. 5, pp. 249-252, Mar. 2007.
[26] J. Chen, H. He, G. Williams, and H. Jin, "Temporal Sequence Associations for Rare Events," Proc. Pacific-Asia Conf. Knowledge Discovery and Data Mining (PAKDD '04), pp. 235-239, May 2004.
[27] G.I. Webb, "Efficient Search for Association Rules," Proc. ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining (KDD '00), pp. 99-107, 2000.
[28] D. McAullay, G. Williams, J. Chen, H. Jin, H. He, R. Sparks, and C. Kelman, "A Delivery Framework for Health Data Mining and Analytics," Proc. Australasian Computer Science Conf. (ACSC '05), V. Estivill-Castro, ed., vol. 38, pp. 381-390, 2005.
[29] H. Jin, J. Chen, H. He, G.J. Williams, C. Kelman, and C.M. O'Keefe, "Mining Unexpected Temporal Associations: Applications in Detecting Adverse Drug Reactions," IEEE Trans. Information Technology in Biomedicine, vol. 12, no. 4, pp. 488-500, July 2008.
[30] R. Srikant and R. Agrawal, "Mining Sequential Patterns: Generalizations and Performance Improvements," Proc. Advances in Database Technology (EDBT '96), pp. 3-17, 1996.
[31] Y. Li, P. Ning, X.S. Wang, and S. Jajodia, "Discovering Calendar-Based Temporal Association Rules," Data & Knowledge Eng., vol. 44, no. 2, pp. 193-218, 2003.
[32] C.-H. Lee, M.-S. Chen, and C.-R. Lin, "Progressive Partition Miner: An Efficient Algorithm for Mining General Temporal Association Rules," IEEE Trans. Knowledge and Data Eng., vol. 15, no. 4, pp. 1004-1017, July/Aug. 2003.
[33] J. Han, J. Pei, B. Mortazavi-Asl, Q. Chen, U. Dayal, and M.-C. Hsu, "FreeSpan: Frequent Pattern-Projected Sequential Pattern Mining," Proc. ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining (KDD '00), pp. 355-359, 2000.
[34] M. Maclure, "The Case-Crossover Design: A Method for Studying Transient Effects on the Risk of Acute Events," Am. J. Epidemiology, vol. 133, no. 2, pp. 144-153, 1991.
[35] The Drug Utilisation Sub-Committee (DUSC), Australian Statistics on Medicines, 1999-2000, DoHA, 2003.
[36] M. Stephens, Causality Assessment and Signal Recognition. Macmillan Reference Ltd, ch. 11, pp. 296-318, 1998.
[37] RxList, , 2010.
[38] H. Mannila, H. Toivonen, and A.I. Verkamo, "Discovery of Frequent Episodes in Event Sequences," Data Mining and Knowledge Discovery, vol. 1, no. 3, pp. 259-289, 1997.
[39] J. Pei, H. Wang, J. Liu, K. Wang, J. Wang, and P.S. Yu, "Discovering Frequent Closed Partial Orders from Strings," IEEE Trans. Knowledge and Data Eng., vol. 18, no. 11, pp. 1467-1481, Nov. 2006.
[40] K. Wang, Y. Jiang, and L.V. Lakshmanan, "Mining Unexpected Rules by Pushing User Dynamics," Proc. ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining (KDD '03), pp. 246-255, 2003.
[41] B. Liu, W. Hsu, L.-F. Mun, and H.-Y. Lee, "Finding Interesting Patterns Using User Expectations," IEEE Trans. Knowledge and Data Eng., vol. 11, no. 6, pp. 817-832, Nov. 1999.
[42] X. Wu, C. Zhang, and S. Zhang, "Efficient Mining of Both Positive and Negative Association Rules," ACM Trans. Information Systems, vol. 22, no. 3, pp. 381-405, 2004.
[43] S.E. Brossette, A.P. Sprague, J.M. Hardin, K.B. Waites, W.T. Jones, and S.A. Moser, "Association Rules and Data Mining in Hospital Infection Control and Public Health Surveillance," J. Am. Medical Informatics Assoc., vol. 5, pp. 373-381, 1998.
[44] S.E. Brossette, A.P. Sprague, W.T. Jones, and S.A. Moser, "A Data Mining System for Infection Control Surveillance," Methods of Information in Medicine, vol. 39, nos. 4-5, pp. 303-310, Dec. 2000.
21 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool