Mining Pharmacy Database Using Evolutionary Genetic Algorithm

• Autorzy: Ykhlef M. , ElGibreen H.
• Języki publikacji: EN

Abstrakty

EN

Medication management is an important process in pharmacy field. Prescribing errors occur upstream in the process, and their effects can be perpetuated in subsequent steps. Prescription errors are an important issue for which conflicts with another prescribed medicine could cause severe harm for a patient. In addition, due to the shortage of pharmacists and to contain the cost of healthcare delivery, time is also an important issue. Former knowledge of prescriptions can reduce the errors, and discovery of such knowledge requires data mining techniques, such as Sequential Pattern. Moreover, Evolutionary Algorithms, such as Genetic Algorithm (GA), can find good rules in short time, thus it can be used to discover the Sequential Patterns in Pharmacy Database. In this paper GA is used to assess patient prescriptions based on former knowledge of series of prescriptions in order to extract sequenced patterns and predict unusual activities to reduce errors in timely manner.

Słowa kluczowe

EN

data mining; evolutionary algorithms; genetic algorithm; pharmacy database; sequential patterns

• Wydawca: Polish Academy of Sciences, Committee of Electronics and Telecommunication
• Czasopismo: International Journal of Electronics and Telecommunications
• Rocznik: 2010
• Tom: Vol. 56, No. 4
• Strony: 427--432
• Opis fizyczny: Bibliogr. 26 poz., wykr.

Twórcy

autor

Ykhlef M.

autor

ElGibreen H.

• College of Computer and Information Sciences, Information System Department, King Saud University, Kingdom of Saudi Arabia,

Bibliografia

• [1] R. Agrawal and R. Srikant, “Mining sequential patterns,” in IBM Almaden Research Center, 650 Harry Road, San Jose, CA 95120-6099, 1995.
• [2] R. Agrawal and R. Srikant, “Mining sequential patterns: Generalizations and performance improvements,” in IBM Almaden Research Center, 650 Harry Road, San Jose, CA 95120, 1996.
• [3] U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth, “From data mining to knowledge discovery in databases,” American Association for Artificial Intelligence: AI Magazine, pp. 37-54, 1996.
• [4] D. Goldberg, Genetic Algorithms. Addison Wesley, 1989.
• [5] F. Herrera, M. Lozano, and J. L. Verdegay, “Tackling real-coded genetic algorithms: Operators and tools for the behaviour analysis,” Artificial Intelligence Review, vol. 12, pp. 256–319, 1998.
• [6] J. Tay and D. Wibowo, “An effective chromosome representation for evolving flexible job shop schedules,” in The Genetic and Evolutionary Computation Conference (2), 2004, pp. 210-221.
• [7] S. Wannarumon, “Aesthetic creation of endless forms: An application in jewelry design,” pp. 395-410.
• [8] Y. Zhou, Study on Genetic Algorithm Improvement and Application. Master thesis, May 2006.
• [9] L. Geng and H. J. Hamilton, “Interestingness measures for data mining: A survey,” ACM Computing Surveys (CSUR), vol. 38, 2006.
• [10] S. Sakurai, Y. Kitahara, and R. Orihara, “A sequential pattern mining method based on sequential interestingness,” International Journal of Computational Intelligence, pp. 252-260, 2008.
• [11] Q. Zhao and S. S. Bhowmick, “Sequential pattern mining: A survey,” Nanyang Technological University, Tech. Rep. 2003118, 2003.
• [12] M. Kaya and R. Alhajj, “Multi-objective genetic algorithm based approach for optimizing fuzzy sequential patterns,” in 16th IEEE International Conference on Tools with Artificial Intelligence, 2004.
• [13] M. Leonard, M. Cimino, S. S. S. McDougal, J. Pilliod, and L. Brodsky, “Risk reduction for adverse drug events through sequential implementation of patient safety initiatives in a children’s hospital,” in American Academy of Pediatrics, vol. 18, no. 4, 2006, pp. 1124-1129.
• [14] C. Antunes and A. L. Oliveira, “Sequential pattern mining algorithms: Trade-offs between speed and memory,” in Instituto Superior Tcnico/INESC-ID, 2004.
• [15] J. Wook and S. Woo, “New encoding/converting methods of binary GA/real-coded GA,” IEICE Transaction, vol. E88-A, no. 6, pp. 1545-1564, 2005.
• [16] J. Ayres, J. Gehrke, T. Yiu, and J. Flannick, Sequential Pattern Mining using a Bitmap Representation, 2nd ed. Alberta, Canada: SIGKDD, 2002.
• [17] W. Spears and V. Anand, “A study of crossover operators in genetic programmin,” in Proceedings of the 6th International Symposium on Methodologies for Intelligent Systems, 1991.
• [18] W. Spears, “Crossover or mutation?” Navy Center for Applied Research in Artificial Intelligence, 1992.
• [19] A. Freitas, Computing Laboratory. UK: University of Kent, 2008, ch. A Review of Evolutionary Algorithms for Data Mining.
• [20] A. Freitas, Data Mining and Knowledge Discovery with Evolutionary Algorithms. Berlin: Spinger-Verlag, 2002.
• [21] Y. Hirate and H. Yamana, “Generalized sequential pattern mining with item intervals,” Journal of computers, vol. 1, no. 3, pp. 51-60, 2006.
• [22] D. Olson and D. Delen, Advanced Data Mining Techniques. Berlin Heidelberg: Springer-Verlag, 2008.
• [23] M. Pakhira and R. De, “Generational pipelined genetic algorithm (PLGA) using stochastic selection,” International Journal of Computer Systems Science and Engineering, vol. 4, no. 1, pp. 75-88, 2007.
• [24] C. Romero, S. Ventura, and P. Debra, Knowledge Discovery with Genetic Programming for Providing Feedback to Courseware Authors. Netherlands: Kluwer Academic Publishers, 2004.
• [25] D. Taniar, Data Mining and Knowledge Discovery Technologies. New York: Hershey, 2008.
• [26] S. Y. W. “Paper survey on sequential pattern data mining,” December 2004.