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Development of a Depression Ontology as a Necessary Step for the Effective Implementation of IT in Psychopathology | IEEE Conference Publication | IEEE Xplore

Development of a Depression Ontology as a Necessary Step for the Effective Implementation of IT in Psychopathology


Abstract:

Informational technologies have been used in medicine and biology for decades. Machine learning algorithms have proven effective for the analysis of data of all possible ...Show More

Abstract:

Informational technologies have been used in medicine and biology for decades. Machine learning algorithms have proven effective for the analysis of data of all possible types and disorders diagnoses. Decision-making support systems, which were based on ontologies developed at the turn of the century, have also been widely disseminated. However, in psychopathology the situation is different: only a few attempts to implement the IT are presented and their efficiency remains questionable. Furthermore, in recent years, there has been lower progress in the research of mental disorders, despite the growing role of the latter in modern society. In this article, we try to explore the reasons for mentioned problems. From our point of view, the main cause is the absence of a satisfying formalized representation of mental disorders area. Therefore, we assume that it is necessary to create an ontology of mental disorders as a comprehensive and noncontradictory model and propose our approach to the development of the ontology of a more specialized data domain such as Depression, as an initial step. In the article, we also consider already existing approaches to the formalization of the depression and mental disorders data domains and identified possible perspectives.
Date of Conference: 30 June 2022 - 04 July 2022
Date Added to IEEE Xplore: 19 August 2022
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ISSN Information:

Conference Location: Altai, Russian Federation

I. Introduction

The problem of mental disorders is becoming exacerbated from year to year. Depression, which is the main subject of our current investigation, is considered one of the most significant mental disorders. In 2001 World Health Organization (WHO) noted the high dynamics of the growth of depression and suggested depression to become the second most severe public health problem in the world by the year 2020 [1], [2]. Since 2017 and until now WHO regards depression as the leading cause of disability and one of the key factors of suicide. For that reason, it is highly demanded to investigate mental disorders and the effective methods for their treatment. But researchers point out the insufficient progress in this direction [3], [4]. From our point of view, the more intensive implementation of informational technologies could solve this problem and improve the efficiency of scientific investigations, as was shown in other areas of biology and medicine. One of the dominant directions in the use of IT in medicine is machine learning, which has become widespread in oncology. Examples and principles of its implementation and the results are described in [5]. Explainable artificial intelligence methods, such as decision-making support systems based on ontologies, are also developed actively. Detailed information about such systems and the most significant medical and biological ontologies can be found in [6], [7]. However, in the area of mental disorders, the usage of such technologies is rather limited. We suppose that the primary reason for their unpopularity and inefficiency in this area is almost a total lack of commonly accepted standards and formal models, which could serve as a basis for the development and functioning of particular algorithms. Therefore, it is necessary to systematize notions and formalize this data domain. Let us consider now already existing attempts.

References

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