Robust discovery of local patterns: Subsets and stratification in adverse drug reaction surveillance

IHI'12 - Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium

Volumn , Issue , 2012, Pages 265-274

  Hopstadius, Johan ^a   Norén, G Niklas ^a,b  

^a UPPSALA MONITORING CENTRE   (Sweden)

^b STOCKHOLM UNIVERSITY   (Sweden)

Author keywords

Adverse drug reaction surveillance; Associations; Frequent patterns; Multiple comparisons; Permutation test; Stratification; Subset analysis

Indexed keywords

ADVERSE DRUG REACTIONS; BIOMEDICAL DATA; CORE ACTIVITY; COVARIATES; DATA SUBSETS; EFFECTIVE ALGORITHMS; EXPLORATORY ANALYSIS; FREQUENT PATTERNS; GLOBAL MEASURES; HARMFUL EFFECTS; HIGH DIMENSIONAL DATA; HIGH-DIMENSIONAL; LOCAL PATTERNS; MEDICAL CONCEPTS; MEDICINAL PRODUCTS; MULTIPLE COMPARISON; PERMUTATION TESTS; SAFETY REPORTS; SUBSET ANALYSIS; TIME-PERIODS;

ASSOCIATION REACTIONS; DATABASE SYSTEMS; DIAGNOSIS; MONITORING; PHARMACODYNAMICS; THERMAL STRATIFICATION;

SET THEORY;

EID: 84857764562     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2110363.2110395     Document Type: Conference Paper

References (18)

1. O. Caster, G. N. Norén, D. Madigan, and A. Bate. Large-scale regression-based pattern discovery: The example of screening the WHO global drug safety database. Statistical Analysis and Data Mining, 3(4):197-208, 2010.
2. G. Dong and J. Li. Eficient mining of emerging patterns: discovering trends and difierences. In Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '99, pages 43-52, New York, NY, USA, 1999. ACM.
3. W. DuMouchel and D. Pregibon. Empirical Bayes screening for multi-item associations. In KDD '01: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, pages 67-76, 2001.
4. D. J. Hand and R. Bolton. Pattern discovery and detection: A unified statistical methodology. Journal of Applied Statistics, 31(8):885-924, 2004. (Pubitemid 39518784)
5. J. Hopstadius, G. N. Norén, A. Bate, and I. R. Edwards. Adjustment for potential confounders in adverse drug reaction surveillance. Drug Safety, 31(11):1035-1048, 2008.
6. W. Klösgen. Deviation and association patterns for subgroup mining in temporal, spatial, and textual data bases. In Rough Sets and Current Trends in Computing, volume 1424 of Lecture Notes in Computer Science, pages 1-18. Springer Berlin / Heidelberg, 1998. (Pubitemid 128093384)
7. J. Li, A. Fu, H. He, J. Chen, H. Jin, D. McAullay, G. Williams, R. Sparks, and C. Kelman. Mining risk patterns in medical data. In KDD '05: Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery and data mining, pages 770-775, 2005. (Pubitemid 43218353)
8. G. N. Norén. Statistical methods for knowledge discovery in adverse drug reaction surveillance. PhD thesis, Department of Mathematics, Stockholm University, 2007.
9. G. N. Norén, J. Hopstadius, and A. Bate. Shrinkage observed-to-expected ratios for robust and transparent large-scale pattern discovery. Statistical Methods in Medical Research, Epub 2011 Jun 24.
10. G. N. Norén, J. Hopstadius, A. Bate, K. Star, and I. R. Edwards. Temporal pattern discovery in longitudinal electronic patient records. Data Mining and Knowledge Discovery, 20(3):361-387, 2010.
11. G. N. Norén, R. Orre, A. Bate, and I. R. Edwards. Duplicate detection in adverse drug reaction surveillance. Data Mining and Knowledge Discovery, pages 305-328, 2007. (Pubitemid 46582604)
12. G. N. Norén, R. Sundberg, A. Bate, and I. R. Edwards. A statistical methodology for drug-drug interaction surveillance. Statistics in Medicine, 27(16):3057-3070, 2008.
13. P. K. Novak, N. Lavrafic, and G. I. Webb. Supervised descriptive rule discovery: A unifying survey of contrast set, emerging pattern and subgroup mining. J. Mach. Learn. Res., 10:377-403, June 2009.
14. R. Orre, A. Lansner, A. Bate, and M. Lindquist. Bayesian neural networks with confidence estimations applied to data mining. Computational Statistics & Data Analysis, 34:473-493, 2000. (Pubitemid 32033820)
15. E. Simpson. The interpretation of interaction in contingency tables. Journal of the Royal Statistical Society. Series B (Methodological), 13(2):238-241, 1951.
16. E. Suzuki. Autonomous discovery of reliable exception rules. In Proceedings of the Third International Conference on Knowledge Discovery and Data Mining (KDD-97), pages 259-262, 1997.
17. G. I. Webb. Discovering significant patterns. Machine Learning, 68(1):1-33, 2007. (Pubitemid 46828725)
18. S. Wrobel. An algorithm for multi-relational discovery of subgroups. In Principles of Data Mining and Knowledge Discovery, volume 1263 of Lecture Notes in Computer Science, pages 78-87. Springer Berlin / Heidelberg, 1997. (Pubitemid 127097494)