Abstract
Today, in Ukraine the task of creating an informative system for personalized treatment strategy searching is quite relevant. Its appearance will help to solve the long-burning issue of the absence of clinical pharmacists in medical institutions. These specialists are necessary because doctors cannot make an independent decision regarding the patient’s treatment strategy, since even their full compliance with the clinical protocols does not ensure a fully optimal human state. This work describes how the informative system can be formed, which will not only serve as a decision support system for doctors but also will effectively find the necessary solutions. The personalized treatment strategy searching is a multi-objective optimization problem since the final state of the patient is described by several indicators. To solve this kind of problem, the authors developed an approach using the principles of the genetic algorithm and the analytic hierarchy process. This approach was used in the practical task of finding personalized treatment strategies for patients with congenital heart defects to demonstrate the difference between the decision made by doctors in real life and the decision produced by the algorithm. Predictive models of indicators after treatment were obtained by the random forest classifier algorithm. Most of the models had 100% accuracy on the testing sample, which indicates the high efficiency of the used classification method. The promoted approach will be the foundation for the informative system developed in the future, and medical institutions can use it for any type of task regardless of the disease types.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chen Y-F, Neil KE, Avery AJ, Dewey ME, Johnson C (2005) Prescribing errors and other problems reported by community pharmacists. Ther Clin Risk Manag 1(4):333–342
Bucchi C, Valdivia-Gandur I, Sánchez-Bizjak R, Tallón-Walton V, Manzanares-Céspedes C (2017) Regenerative endodontic therapy: a systematic review of clinical protocols. Int J Clin Exp Med 10:2006–2015
Khan FU, Waqas N, Ihsan AU, Khongorzul P, Wazir J, Gang W, Mengqi Y, Xiaoqian L, Han L, Xiaohui Z (2019) Analysis of the qualities matching new classification of clinical pharmacist. Indian J Pharm Sci 81(1):2–10. https://doi.org/10.4172/pharmaceutical-sciences.1000473
Nastenko I, Pavlov V, Nosovets O, Zelensky K, Davidko O, Pavlov O (2019) Optimal complexity models in individual control strategy task for objects that cannot be re-trialed. In: Proceedings of IEEE 2019 14th international scientific and technical conference on computer sciences and information technologies, CSIT 2019—Proceedings, pp 207–210 https://doi.org/10.1109/STC-CSIT.2019.8929831
Nastenko I, Pavlov V, Nosovets O, Zelensky K, Davidko O, Pavlov O (2020) Solving the individual control strategy tasks using the optimal complexity models built on the class of similar objects. In: Proceedings of Advances in intelligent systems and computing. Springer, pp 535–546 https://doi.org/10.1007/978-3-030-33695-0_36
Panjwani S, Kumar SN, Ahuja L (2019) Multi-criteria decision making and its applications. Int J Innov Technol Explor Eng 8(9 Special Issue 4). https://doi.org/10.35940/ijitee.I1122.0789S419
Saaty TL (1990) Decision Making for Leaders: The analytic hierarchy process for decisions in a complex world. RWS Publications. https://doi.org/10.1016/0377-2217(89)90066-0
Zgurovsky MZ, Pavlov AA (2019) The four-level model of planning and decision making. In: Studies in Systems, Decision and Control, pp 347–406. https://doi.org/10.1007/978-3-319-98977-8_8
García JM, Acosta CA, Mesa MJ (2020) Genetic algorithms for mathematical optimization. In: Proceedings of Journal of Physics: Conference Series, p 5. https://doi.org/10.1088/1742-6596/1448/1/012020
Zhang Q, Li H (2007) MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans Evol Comput 11(6):712–731. https://doi.org/10.1109/TEVC.2007.892759
Monsef H, Naghashzadegan M, Jamali A, Farmani R (2019) Comparison of evolutionary multi objective optimization algorithms in optimum design of water distribution network. Ain Shams Eng J 10(1):103–111. https://doi.org/10.1016/j.asej.2018.04.003
Biau G (2012) Analysis of a random forests model. J Mach Learn Res 13:1063–1095
Cheng L, Li L, Wang L, Li X, Xing H, Zhou J (2018) A random forest classifier predicts recurrence risk in patients with ovarian cancer. Mol Med Rep 18:3289–3297. https://doi.org/10.3892/mmr.2018.9300
Utkin LV (2020) An imprecise deep forest for classification. Expert Syst Appl 141:31. https://doi.org/10.1016/j.eswa.2019.112978
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Babenko, V. et al. (2022). Forming the System with the Functionality of Clinical Pharmacist for Personalized Treatment Strategy Searching. In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds) Proceedings of Sixth International Congress on Information and Communication Technology. Lecture Notes in Networks and Systems, vol 235. Springer, Singapore. https://doi.org/10.1007/978-981-16-2377-6_47
Download citation
DOI: https://doi.org/10.1007/978-981-16-2377-6_47
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-2376-9
Online ISBN: 978-981-16-2377-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)