Open AccessEvolutionary algorithm for automated formation of decision-making models for predicting the safety of opioid therapy
Author(s) -
Leonid Lipinskiy,
Olga Melnikova,
A S Polyakova,
Svetlana Evseeva,
О. П. Боброва,
Natalia A. Shnayder,
S. K. Zyryanov,
M.M. Petrova,
Y A Dychno,
С. Н. Зобова,
А. В. Бобров
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1047/1/012126
Subject(s) - outcome (game theory) , opioid , convolution (computer science) , set (abstract data type) , fentanyl , computer science , algorithm , genetic algorithm , medicine , artificial intelligence , machine learning , mathematics , pharmacology , receptor , mathematical economics , artificial neural network , programming language
In this paper, an evolutionary algorithm for solving the problem of predicting the safety of opioid therapy for patients with pancreatic cancer is proposed. Opioid analgesics such as fentanyl and morphine are used as a therapy for pain syndromes. Using the patient database, based on the results of the therapy applied to them, it is determined whether there is a correlation between the outcome and the combination of input data taken into account. To find a set of informative features, it is proposed to use the genetic algorithm for multi-criterion optimization, in which two criteria are reduced to one generalized criterion using the method of “additive convolution”. The formed combination of the selected input features, which affects the outcome, is used to build a decision support model and to evaluate it afterwards.