Algorithms for Forming a Knowledge Base for Decision Support Systems in Cybersecurity Tasks

In the past, much effort of healthcare decision support systems were focused on the data acquisition and storage, in order to allow the use of this data at some later point in time. Medical data was used in static manner, for analytical purposes, in order to verify the undertaken decisions. Due to the immense volumes of medical data, the architecture of the future healthcare decision support systems focus more on interoperability than on integration. With the raising need for the creation of unified knowledge base, the federated approach to distributed data warehouses (DWH) is getting increasing attention. The exploitation of evidence-based guidelines becomes a priority concern, as the awareness of the importance of knowledge management rises. Consequently, interoperability between medical information systems is becoming a necessity in modern health care. Under strong security measures, health care organizations are striking to unite and share their (partly very high sensitive) data assets in order to achieve a wider knowledge base and to provide a matured decision support service for the decision makers. Ontological integration of the very complex and heterogeneous medical data structures is a challenging task. The authors’ objective is to point out the advantages of the deployment of a federated data warehouse approach for the integration of the wide range of different medical data sources and for distribution of evidence-based clinical knowledge, to support clinical decision makers, primarily clinicians at the point of care.

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Health Informatics. Requirements for a knowledge base for clinical decision support systems to be used in medication-related processes

10.3403/30376130 ◽

2020 ◽

Keyword(s):

Decision Support ◽

Decision Support Systems ◽

Knowledge Base ◽

Clinical Decision Support ◽

Health Informatics ◽

Support Systems ◽

Clinical Decision Support Systems ◽

Clinical Decision

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Predictive Models for Agricultural Management

International Journal of Environmental Science and Development ◽

10.18178/ijesd.2021.12.7.1343 ◽

2021 ◽

Vol 12 (7) ◽

pp. 220-225

Author(s):

Natalia N. Bakhtadze ◽

◽

Evgeny M. Maximov ◽

Natalia E. Maximova ◽

Lamara N. Kozlovskaya

Keyword(s):

Decision Support ◽

Grain Yield ◽

Decision Support Systems ◽

Knowledge Base ◽

Predictive Models ◽

Agricultural Production ◽

Support Systems ◽

Ecological Effect ◽

The Cost

This paper is devoted to the development of predictive models for decision support systems applied in precision farming. Application of predictive models makes it possible to use resources effectively, which reduces the cost of production and increases the efficiency of agricultural production. In addition, the forecast makes it possible to reach a long-term agronomic and ecological effect due to more careful tillage and reduced use of fertilizers. The algorithms using knowledge base for creating models of grain yield are described and the results of applying these models are presented.

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