Using Decision-Making Block of Computer-Based Intelligent Biomedical Avatar for Applied Research in Bioinformatics

Using Decision-Making Block of Computer-Based Intelligent Biomedical Avatar for Applied Research in Bioinformatics

Leyla Ayvarovna Gamidullaeva (Penza State University, PSU, Penza, Russian Federation) and Vsevolod Chernyshenko (Financial University under the Government of the Russian Federation, Moscow, Russian Federation)
Copyright: © 2019 |Pages: 11
DOI: 10.4018/IJARB.2019070102

Abstract

A biomedical task in which the definitions and properties of applied research indicators under study in bioinformatics is formalized. A wide range of traditional approaches used for predicting medical time series were reviewed. Advanced algorithms for predicting moments of reversals of biomedical trends based on machine learning tools were investigated as well. The effectiveness of different kinds of approaches was discussed, and related examples are given. An original securities price dynamics trend classification algorithm, based on the use of the sliding window methodology and biomedical avatar, is described. A general scheme of the classification algorithm to identify biomedical market phases is analyzed and results of computer modelling are presented. Selection of initial and resulting metrics is grounded.
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Introduction

Design of intelligent computer systems capable to lead to effective biomedical (particularly biomedical) decisions is a topical problem that is still far from being solved. It encounters a number of difficulties, one of which is a high randomness of the dynamics of biomedical indicators, immanently inherent in this class of dynamic processes. On the other hand, they nature may not be considered as a completely chaotic: there are, of course, certain patterns that can be recognized by high-intelligent algorithms. It should be mentioned that even a slight increase of the accuracy of a volatility forecast may provide an investor with a quite significant yield.

Obviously, algorithms that make reliable forecasts regarding market trends dynamic cannot be based just on simple mathematical models with fixed properties. Recent trends in this filed – solutions based on machine learning that collect and analyze big statistical data in real time (including data for an evaluation of the quality of these models previous prognoses and the effectiveness of the corresponding recommended solutions). Such solutions may be represented as computer agents or avatars – pieces of a program code, to be separate objects with their inputs and outputs, interacting with/in a common software environment and having access to the relevant databases. Such computer “biomedical agents” are widely used in a biomedical sphere and partly determine biomedical markets dynamics themselves. That fact, for sure, does not contradict the effectiveness of use of such algorithms, since the described approach doesn`t link to a particular market model but is able to adapt to any conditions.

Obviously, one of the key qualities of an agent (determining its success mostly) is its ability to predict the market trend in relation to a certain indicator (e.g. share quotation of certain companies). In this chapter, one of such an approach to create an “avatar self-learning” algorithm will be considered, allowing user to effectively predict changes of biomedical markets` trends.

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