Efficient discovery of risk patterns in medical data

References 

1. Quinlan JR. C4. 5: programs for machine learning. San Mateo, CA: Morgan Kaufmann; 1993;
2. Kononenko I. Machine learning for medical diagnosis: history, state of the art and perspective. Artificial Intelligence in Medicine. 2001;1:89–109
3. Li J, Wong L. Using rules to analyse bio-medical data: a comparison between c4. 5 and PCL. In:  Dong G,  Tang C,  Wang W editor. Advances in web-age information management, proceedings of 4th international conference. Berlin/Heidelberg: Springer; 2003;p. 254–265
4. Zhou Z, Jiang Y. Medical diagnosis with C4.5 rule preceded by artificial neural network ensemble. IEEE Transactions on Information Technology in Biomedicine. 2003;1:37–42
5. Agrawal R, Imieliński T, Swami A. Mining association rules between sets of items in large databases. In:  Buneman P,  Jajodia S editor. Proceedings of ACM SIGMOD international conference on management of data. New York: ACM; 1993;p. 207–216
6. Brossette SE, Sprague AP, Hardin JM, Jones KWT, Moser SA. Association rules and data mining in hospital infection control and public health surveillance. Journal of American Medical Informatics Association. 1998;5:373–381
7. Ohsaki M, Sato Y, Yokoi H, Yamaguchi T. A rule discovery support system for sequential medical data in the case study of a chronic hepatitis dataset. In: Proceedings of the ECML/PKDD-2003 discovery challenge workshop, [ http://www.lisp.vse.cz/challenge/ecmlpkdd2003/, accessed 26.3.08].
8. Paetz J, Brause RW. A frequent patterns tree approach for rule generation with categorical septic shock patient data. In:  Crespo J,  Maojo V,  Martin F editor. Proceedings of the second international symposium on medical data analysis. London: Springer-Verlag; 2001;p. 207–212
9. Li J, chee Fu AW, He H, Chen J, Jin H, McAullay D, et al. Mining risk patterns in medical data. In:  Grossman R,  Bayardo RJ,  Bennett KP editor. Proceedings of the 11th ACM SIGKDD international conference on knowledge discovery and data mining. New York: ACM; 2005;p. 770–775
10. Quinlan JR. Induction of decision trees. Machine Learning. 1986;1:81–106
11. Ordonez C. Comparing association rules and decision trees for disease prediction. In:  Xiong L,  Xia Y editor. Proceedings of the international workshop on healthcare information and knowledge management. New York: ACM; 2006;p. 17–24
12. Hu J, Mojsilovic A. High-utility pattern mining: a method for discovery of high-utility item sets. Pattern Recognition. 2007;11:3317–3324
13. Agrawal R, Srikant R. Fast algorithms for mining association rules. In:  Bocca JB,  Jarke M,  Zaniolo C editor. Proceedings of 20th international conference on very large data bases. San Mateo, CA: Morgan Kaufmann; 1994;p. 487–499
14. Han J, Pei J, Yin Y, Mao R. Mining frequent patterns without candidate generation: a frequent-pattern tree approach. Data Mining and Knowledge Discovery Journal. 2004;1:53–87
15. Chen J, He H, Williams GJ, Jin H. Temporal sequence associations for rare events. In:  Dai H,  Srikant R,  Zhang C editor. Advances in knowledge discovery and data mining, eighth Pacific-Asia conference. Berlin/Heidelberg: Springer; 2004;p. 235–239
16. Ordonez C, Ezquerra NF, Santana CA. Constraining and summarizing association rules in medical data. Knowledge and Information Systems. 2006;3:1–2
17. Zaki MJ. Mining non-redundant association rules. Data Mining and Knowledge Discovery Journal. 2004;3:223–248
18. Bayardo R, Agrawal R, Gunopulos D. Constraint-based rule mining in large, dense database. Data Mining and Knowledge Discovery Journal 2/3. 2000;217–240
19. Roberto J, Bayardo J, Agrawal R. Mining the most interesting rules. In:  Fayyad U,  Chaudhuri S,  Madigan D editor. Proceedings of the fifth ACM SIGKDD international conference on knowledge discovery and data mining. New York: ACM; 1999;p. 145–154
20. Webb GI. Efficient search for association rules. In:  Ramakrishnan R,  Stolfo S,  Bayardo R,  Parsa I editor. Proceedings of the sixth ACM SIGKDD international conference on knowledge discovery and data mining. New York: ACM; 2000;p. 99–107
21. Webb GI, Zhang S. K-optimal rule discovery. Data Mining and Knowledge Discovery Journal. 2005;1:39–79
22. Cheung Y, Fu A. Mining association rules without support threshold: with and without item constraints. IEEE Transactions on Knowledge and Data Engineering. 2004;9:1052–1069
23. Li H, Li J, Wong L, Feng M, Tan Y-P. Relative risk and odds ratio: a data mining perspective. In:  Li C editors. Proceedings of the 24th ACM SIGMOD-SIGACT-SIGART symposium on principles of database systems. New York: ACM; 2005;p. 368–377
24. Ohsaki M, Kitaguchi S, Okamoto K, Yokoi H, Yamaguchi T. Evaluation of rule interestingness measures with a clinical dataset on hepatitis. In:  Boulicaut J-F,  Esposito F,  Giannotti F,  Pedreschi D editor. Proceedings of the eighth European conference on principles and practice of knowledge discovery in databases. New York: Springer-Verlag; 2004;p. 362–373
25. Triola MM, Triola MF. Biostatistics for the biological and health sciences. 2nd ed. Boston: Addison-Wesley; 2005;
26. Wang J, Han J, Pei J. Closet+: searching for the best strategies for mining frequent closed itemsets. In:  Getoor L,  Senator TE,  Domingos P,  Faloutsos C editor. Proceedings of the ninth ACM SIGKDD international conference on knowledge discovery and data mining. New York: ACM; 2003;p. 236–245
27. Asuncion A, Newman DJ. UCI repository of machine learning databases, [ http://www.archive.ics.uci.edu/ml, accessed 26.6.08].