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Stochastic Programming on Optimal Drug Administration for Two Stage Cancer Treatment Problems

Stochastic Programming on Optimal Drug Administration for Two Stage Cancer Treatment Problems

P. Tirupathi Rao, D. Flora Evangil, K. Madhavi
Copyright: © 2012 |Volume: 3 |Issue: 1 |Pages: 10
ISSN: 1948-5018|EISSN: 1948-5026|EISBN13: 9781466612297|DOI: 10.4018/jgc.2012010101
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MLA

Rao, P. Tirupathi, et al. "Stochastic Programming on Optimal Drug Administration for Two Stage Cancer Treatment Problems." IJGC vol.3, no.1 2012: pp.1-10. http://doi.org/10.4018/jgc.2012010101

APA

Rao, P. T., Evangil, D. F., & Madhavi, K. (2012). Stochastic Programming on Optimal Drug Administration for Two Stage Cancer Treatment Problems. International Journal of Green Computing (IJGC), 3(1), 1-10. http://doi.org/10.4018/jgc.2012010101

Chicago

Rao, P. Tirupathi, D. Flora Evangil, and K. Madhavi. "Stochastic Programming on Optimal Drug Administration for Two Stage Cancer Treatment Problems," International Journal of Green Computing (IJGC) 3, no.1: 1-10. http://doi.org/10.4018/jgc.2012010101

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Abstract

Either Continuous drug administration or continuous drug vacation for long spells of cancer chemotherapy is not suggestible. Similarly the quantum of administered drug dose either above the required level or below the wanted level is also not advised. Effective drug administration has to consider the optimal threshold limits on the drug administration/drug vacation times; upper and lower limits of drug quantity; along with the suitable number of drug administration/drug vacation cycles; and the number of spells within the cycle of drug usage/stoppage. This paper develops an optimization programming problem for designing drug administration strategies for a cancer patient under chemotherapy. This study will help in exploring the decision parameters at the targeted objectives. Optimal decisions on drug dosage level, drug administration period, drug vacation period, number of drug administration cycles; number of drugs applied within a cycle, etc., can be obtained with the model. Sensitivity analysis is carried out for understanding the model behavior. This work has a scope for developing health care Decision Support Systems.

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