Active learning for personalizing treatment

IEEE SSCI 2011: Symposium Series on Computational Intelligence - ADPRL 2011: 2011 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning

Volumn , Issue , 2011, Pages 32-39

  Deng, Kun ^a   Pineau, Joelle ^b   Murphy, Susan ^a  

^a UNIVERSITY OF MICHIGAN   (United States)

^b MCGILL UNIVERSITY   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ACTIVE LEARNING; CLINICAL RESEARCHERS; CLINICAL TRIAL; MINIMAX; PERSONALIZATIONS; RISK CATEGORIES; SIMULATED DATA; TREATMENT EFFECTS;

ARTIFICIAL INTELLIGENCE; EXPERIMENTS; GENETIC PROGRAMMING; MEDICAL APPLICATIONS; REINFORCEMENT LEARNING;

DYNAMIC PROGRAMMING;

EID: 80052250780     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ADPRL.2011.5967348     Document Type: Conference Paper

References (32)

1. S. Thrun, "Efficient exploration in reinforcement learning (Technical Report CS-92-102)." Carnegie Mellon University, Pittsburgh, PA, 1992.
2. M. Strens, "A Bayesian framework for reinforcement learning," in machine learning-international workshop then conference, pp. 943-950, 2000.
3. M. Kearns and S. Singh, "Near-optimal reinforcement learning in polynomial time," Machine Learning, vol. 49, no. 2, pp. 209-232, 2002. (Pubitemid 34325687)
4. R. I. Brafman and M. Tennenholtz, "R-max-a general polynomial time algorithm for near-optimal reinforcement learning," The Journal of Machine Learning Research, vol. 3, pp. 213-231, 2003.
5. A. Epshteyn, A. Vogel, and G. DeJong, "Active reinforcement learning," in Proceedings of the 25th international conference on Machine learning, pp. 296-303, 2008.
6. X. Zhou, S. Liu, E. S. Kim, R. S. Herbst, and J. J. Lee, "Bayesian adaptive design for targeted therapy development in lung cancer-a step toward personalized medicine," Clinical Trials, vol. 5, no. 3, pp. 181- 193, 2008.
7. S. Biswas, D. D. Liu, J. J. Lee, and D. A. Berry, "Bayesian clinical trials at the University of Texas M. D. Anderson Cancer Center," Clinical Trials, vol. 6, no. 3, pp. 205-216, Jun. 2009.
8. J. J. Lee, Xuemin Gu, and Suyu Liu, "Bayesian adaptive randomization designs for targeted agent development," Clinical Trials, vol. 7, no. 5, pp. 584-596, Oct. 2010.
9. A. Antos, V. Grover, and C. Szepesvri, "Active learning in Multiarmed Bandits," in Proceedings of the 19th international conference on Algorithmic Learning Theory, pp. 287-302, 2008.
10. D. Cohn, L. Atlas, and R. Ladner, "Improving generalization with active learning," Machine Learning, vol. 15, no. 2, pp. 201-221, 1994.
11. K. Chaloner and I. Verdinelli, "Bayesian experimental design: A review," Statistical Science, vol. 10, no. 3, pp. 273-304, Aug. 1995.
12. Y. Freund, H. S. Seung, E. Shamir, and N. Tishby, "Selective sampling using the Query by Committee algorithm," Mach. Learn., vol. 28, no. 2, pp. 133-168, 1997. (Pubitemid 127506338)
13. S. Tong and E. Chang, "Support vector machine active learning for image retrieval," in Proceedings of the ninth ACM international conference on Multimedia, pp. 107-118, 2001. (Pubitemid 32962460)
14. G. Xiao, F. Southey, R. C. Holte, and D. F. Wilkinson, "Software testing by active learning for commercial games," in AAAI, pp. 898-903, 2005.
15. S. Tong and D. Koller, "Active learning for parameter estimation in bayesian networks," in In NIPS, pp. 647-653, 2001.
16. Y. Baram, R. El-Yaniv, and K. Luz, "Online choice of active learning algorithms," J. Mach. Learn. Res., vol. 5, pp. 255-291, 2004.
17. O. Madani, D. J. Lizotte, and R. Greiner, "The budgeted multi-armed bandit problem," in Learning theory: 17th annual Conference on Learning Theory, COLT 2004, vol. 3120, pp. 643-645, 2004.
18. A. Kapoor and R. Greiner, "Reinforcement learning for active model selection," in Proceedings of the 1st international workshop on Utilitybased data mining, pp. 17-23, 2005.
19. H. Raghavan, O. Madani, and R. Jones, "Active learning with feedback on features and instances," J. Mach. Learn. Res., vol. 7, pp. 1655-1686, 2006. (Pubitemid 44223089)
20. K. Deng, C. Bourke, S. Scott, J. Sunderman, and Y. Zheng, "Banditbased algorithms for budgeted learning," in Data Mining, 2007. ICDM 2007. Seventh IEEE International Conference on, pp. 463-468, 2008.
21. S. Bubeck, R. Munos, and G. Stoltz, "Pure exploration in multi-armed bandits problems," in Proceedings of the 20th international conference on Algorithmic learning theory, pp. 23-37, 2009.
22. B. Settles, "Active learning literature survey." University of Wisconsin- Madison, 2009.
23. V. Fedorov, "Optimal experimental design," Wiley Interdisciplinary Reviews: Computational Statistics,2010.
24. A. I. Schein, "Active learning for logistic regression," University of Pennsylvania, 2005.
25. K. Yu, J. Bi, and V. Tresp, "Active learning via transductive experimental design," in Proceedings of the 23rd international conference on machine learning, pp. 1081-1088, 2006.
26. L. Wasserman, "All of Statistics: A Concise Course in Statistical Inference." Springer, 2003.
27. S. A. Murphy, "Optimal dynamic treatment regimes," Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol. 65, no. 2, pp. 331-355, 2003.
28. R. Dawson and P. W. Lavori, "Placebo-free designs for evaluating new mental health treatments: the use of adaptive treatment strategies," Statistics in medicine, vol. 23, no. 21, pp. 3249-3262, 2004. (Pubitemid 39462085)
29. S. A. Murphy, K. G. Lynch, D. Oslin, J. R. McKay, and T. TenHave, "Developing adaptive treatment strategies in substance abuse research," Drug and Alcohol Dependence, vol. 88, pp. S24, 2007.
30. A. Guez, R. D. Vincent, M. Avoli, and J. Pineau, "Adaptive treatment of epilepsy via batch-mode reinforcement learning," in Proceedings of the Twentieth Innovative Applications of Artificial Intelligence Conference, pp. 1671-1678, 2008.
31. J. K. Wathen, P. F. Thall, J. D. Cook, and E. H. Estey, "Accounting for patient heterogeneity in phase II clinical trials," Statistics in Medicine, vol. 27, no. 15, pp. 2802-2815, Jul. 2008. (Pubitemid 351982677)
32. M. B. Keller et al., "A comparison of nefazodone, the cognitive behavioral-analysis system of psychotherapy, and their combination for the treatment of chronic depression," New England Journal of Medicine, vol. 342, no. 20, p. 1462, 2000.