Solving the Individual Control Strategy Tasks Using the Optimal Complexity Models Built on the Class of Similar Objects

Настенко, Євген; Pavlov, Volodymyr; Nosovets, Olena; Zelensky, Kyryl; Davidko, Ol.; Pavlov, Ol.

doi:10.1007/978-3-030-33695-0_36

Cited by 8 publications

(5 citation statements)

References 4 publications

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“…The task of choosing the optimal treatment strategy is to find the dosage of drugs that will give the optimal patient's condition during treatment using the optimization method [9]. The technique for solving such problems was proposed in the work [10].…”

Section: Calculation Of Optimal Patient Treatment Strategiesmentioning

confidence: 99%

Data Structure of Medical Information Organization and Selecting Optimal Patient Treatment Strategies

Davydko

Levchyk

Khlimenkov

et al. 2021

Proceedings of the NAU

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Purpose: The purpose of this article is to develop a clinical information storage and medical decision support system for daily use by physicians during their work with patients. Methods: The article considered approaches to achieve the research goal through the creation of a backend on Django Rest Framework, as well as the application of the simplex method to perform treatment optimization. Results: A server-side software product was obtained, where a special database was implemented, which takes into account the requirements for the data structure of the organization of medical information, and a module for selecting optimal treatment strategies for patients was created. Discussion: The conducted research allowed us to obtain a prototype of medical information system, which will be useful due to its versatility in the data structure of the organization of medical data and the use of machine learning methods to optimize treatment strategies.

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Section: Calculation Of Optimal Patient Treatment Strategiesmentioning

confidence: 99%

Data Structure of Medical Information Organization and Selecting Optimal Patient Treatment Strategies

Davydko

Levchyk

Khlimenkov

et al. 2021

Proceedings of the NAU

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“…Also noteworthy is the Ukrainian work by Nastenko et al [47], which used Group Method of Data Handling (GMDH) [48] models and Simplex Method [49] optimization algorithm to select the treatment strategy.…”

Section: Information Systems Of State Assessment and Forecastingmentioning

confidence: 99%

“…Consequently, a multi-criteria optimization problem arises, where it is necessary to find such values of I-vector that will give the optimum of Y-vector. Values of X-vector are set by default, so the personalized solution search will be done by considering the patient's initial state indicators (this idea was proposed in [47] for a single-criteria problem). So, it is necessary to create an algorithm that will solve such a task.…”

Section: Mathematical Backgroundmentioning

confidence: 99%

Personalized Clinical Treatment Selection Using Genetic Algorithm and Analytic Hierarchy Process

Nosovets¹,

Babenko²,

Davydovych³

et al. 2021

Adv. sci. technol. eng. syst. j.

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The development of Machine Learning methods and approaches offers enormous growth opportunities in the Healthcare field. One of the most exciting challenges in this field is the automation of clinical treatment selection for patient state optimization. Using necessary medical data and the application of Machine Learning methods (like the Genetic Algorithm and the Analytic Hierarchy Process) provides a solution to such a challenge. Research presented in this paper gives the general approach to solve the clinical treatment selection task, which can be used for any type of disease. The distinguishing feature of this approach is that clinical treatment is tailored to the patient's initial state, thus making treatment personalized. The article also presents a comparison of the different classification methods used to model patient indicators after treatment. Additionally, special attention was paid to the possibilities and potential of using the developed approach in real Healthcare challenges and tasks.

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“…An example os such a task is finding an individualized treatment strategy. In Ukraine, the first echo of the solution to this problem can be found in [10], where linear programming was used as a solution method. This method was finding the extremum of a certain patient indicator after treatment while driving the rest into restrictions (which is essentially a conditional optimization).…”

Section: Analysis Of the Latest Research And Publicationsmentioning

confidence: 99%

“…The strength is objectively determined via fitness function: the bigger it is, the more chances there are for an individual to take part in the crossover. There are two most popular individual selection methods [10]. The first one is a tournament selection when t (usually equals 2) individuals are randomly chosen from the population and the one with the biggest fitness function is selected among them.…”

Section: Fig 1 Flowchart Of Genetic Algorithmmentioning

confidence: 99%

Setting Up the Genetic Algorithm for the Individualized Treatment Strategy Searching

Dydyk,

Nosovets,

Babenko

2020

HAIT

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The genetic algorithm is a verified mechanism for optimization task solutions. Being a heuristic algorithm, it allows speeding up the task solving by the biological evolution principle use. Recently, this algorithm was offered as a method of an individualized treatment strategy search, where it was necessary to optimize the patient’sstatein the distant period, going through various treatment combinations.In that research as an optimization function, the additive convolution function of the patient’sstateindicatorsin the distant period was used, obtained with the help of Saaty analytic hierarchy process, which is one of the multi-criteria decision making methods. Despite showing good results, the genetic algorithm was set with standard parameters. Taking into consideration a big quantity of the parameters, the present study has the aim to find the optimum parameters for the algorithm. First of all, it is necessary for those, who incorporate this algorithm in their work, namely doctors, when they need to prescribe a treatment for a patient. The study describes the analysis of various genetic algorithm parameters and their use in algorithm test launches for individualized treatment strategy search. Also, the optimal patient’s input parameter subsets wereselected,using the correlation feature selection criterion. The selected parameters were necessary for modeling indicators of the patient’sstateafter treatment.Modeling wasperformed via random forest classifier with preliminarydivided total sample intotraining(eighty percent) and testing(twenty percent) ones. Two different databases of patients with congenital heart diseases were usedfor the study, allowing the optimal parameters being more reliable for their future use. In the end, it all allows finding the parameters, which are first of allexclusively recommendatoryto the doctors before using the algorithm.

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Solving the Individual Control Strategy Tasks Using the Optimal Complexity Models Built on the Class of Similar Objects

Cited by 8 publications

References 4 publications

Data Structure of Medical Information Organization and Selecting Optimal Patient Treatment Strategies

Data Structure of Medical Information Organization and Selecting Optimal Patient Treatment Strategies

Personalized Clinical Treatment Selection Using Genetic Algorithm and Analytic Hierarchy Process

Setting Up the Genetic Algorithm for the Individualized Treatment Strategy Searching

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