Intelligent Decision-Support System for Epidemiological Diagnostics. I. A Concept of Architecture Design

Bazilevych, Kseniia; Chumachenko, Dmytro; Hulianytskyi, L. F.; Meniailov, Ievgen; Yakovlev, Sergiy

doi:10.1007/s10559-022-00466-x

Cited by 7 publications

(3 citation statements)

References 24 publications

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“…Research is part of a complex intelligent information system for epidemiological diagnostics, the concept of which is discussed in [16,17].…”

Section: Introductionmentioning

confidence: 99%

Information system for assessing the informativeness of an epidemic process features

Bazilevych,

Kyrylenko,

Parfenyuk

et al. 2023

SRIT

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The primary objective of this study is to assess the informativeness of various parameters influencing epidemic processes utilizing the Shannon and Kullback–Leibler methods. These methods were selected based on their foundation in the principles of information theory and their extensive application in machine learning, statistics, and other relevant domains. A comparative analysis was performed between the results acquired from both methods, and an information system was designed to facilitate the uploading of data samples and the calculation of factor informativeness impacting the epidemic processes. The findings revealed that certain features, such as “Chronic lung disease,” “Chronic kidney disease,” and “Weakened immunity,” did not carry significant information for further analysis and hindered the forecasting process, as per the data set examined. The developed information system efficiently supports the assessment of feature informativeness, thereby aiding in the comprehensive analysis of epidemic processes and enabling the visualization of the results. This study contributes to the current body of knowledge by providing specific examples of applying the described algorithmic models, comparing various methods and their outcomes, and developing a supportive tool for analyzing epidemic processes.

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“…Research is part of a complex intelligent information system for epidemiological diagnostics, the concept of which is discussed in [16,17].…”

Section: Introductionmentioning

confidence: 99%

Information system for assessing the informativeness of an epidemic process features

Bazilevych,

Kyrylenko,

Parfenyuk

et al. 2023

SRIT

View full text Add to dashboard Cite

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“…Research is part of a complex intelligent information system for epidemiological diagnostics, the concept of which is discussed in [9].…”

Section: Introductionmentioning

confidence: 99%

Effective Utilization of Data for Predicting COVID-19 Dynamics: An Exploration through Machine Learning Models

Chumachenko,

Dudkina,

Yakovlev

et al. 2023

International Journal of Telemedicine and Applications

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This study is centered around the COVID-19 pandemic which has posed a global health concern for over three years. It emphasizes the importance of effectively utilizing epidemic simulation models for informed decision-making concerning epidemic control. The challenge lies in appropriately choosing, adapting, and interpreting these models. The research constructs three statistical machine learning models to predict the spread of COVID-19 in specific regions and evaluates their performance using real COVID-19 incidence data. The paper presents short-term (3, 7, 14, 21, and 30 days) forecasts of COVID-19 morbidity and mortality for Germany, Japan, South Korea, and Ukraine. The precision of each model was scrutinized based on the type of input data used. Recommendations are provided on how various data sources can enhance the interpretation quality of machine learning models predicting infectious disease dynamics. The initial findings suggest the need for the comprehensive utilization of all available data, favoring cumulative data during holiday-rich periods and daily data otherwise. To minimize the absolute error, databases should be compiled using daily morbidity and mortality rates.

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“…In general terms, DSSs are capable of managing, controlling, monitoring, and executing operational procedures, either scheduled or in an emergency [9]. This type of system has already been developed for realistic cases in the political sector for addressing natural disasters [10], epidemic incidents [11], transportation of inflammable or other hazardous materials, etc. [12] and in the security sector for search and rescue missions [13], urgent air transportation of patients [14], supply missions [15], and other similar complex missions in several areas in case of emergency situations [16] (see Appendix A).…”

mentioning

confidence: 99%

Methodologies and Handling Techniques of Large-Scale Information in Decision Support Systems for Complex Missions

Tsavdaridis,

Papaodysseus,

Karadimas

et al. 2024

Applied Sciences

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Designing integrating systems for support, real-time monitoring, and executing of complex missions is challenging, since they often fail due to high levels of complexity and overwhelming volume of input data. Past attempts have resorted to “ad hoc” solutions, which face issues of being non-updatable, non-upgradable, and not applicable to similar missions, necessitating a complete redesign and reconstruction of the system. In the national defense and security sector, the impact of this reconstruction requirement leads to significant costs and delays. This study presents advanced methodologies for organizing large-scale datasets and handling complex operational procedures systematically, enhancing the capabilities of Decision Support Systems (DSSs). By introducing Complex Mission Support Systems (CMSSs), a novel SS sub-component, improved accuracy and effectiveness are achieved. The CMSS includes mission conceptualization, analysis, real-time monitoring, control dynamics, execution strategies, and simulations. These methods significantly aid engineers in developing DSSs that are highly user-friendly and operational, thanks to human-reasoning-centered design, increasing performance and efficiency. In summary, the systematic development of data cores that support complex processes creates an adaptable and adjustable framework in a wide range of diverse missions. This approach significantly enhances the overall sustainability and robustness of an integrated system.

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Intelligent Decision-Support System for Epidemiological Diagnostics. I. A Concept of Architecture Design

Cited by 7 publications

References 24 publications

Information system for assessing the informativeness of an epidemic process features

Information system for assessing the informativeness of an epidemic process features

Effective Utilization of Data for Predicting COVID-19 Dynamics: An Exploration through Machine Learning Models

Methodologies and Handling Techniques of Large-Scale Information in Decision Support Systems for Complex Missions

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