Future of Medical Decision Support Systems

Köse, Utku; Deperlioğlu, Ömer; Alzubi, Jafar A.; Pătruţ, Bogdan

doi:10.1007/978-981-15-6325-6_10

Cited by 3 publications

(2 citation statements)

References 61 publications

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“…By demonstrating the potential of machine learning models to make accurate short-term forecasts in the context of a global pandemic, we underscore the transformative potential of these technologies in public health decision-making. As healthcare systems worldwide grapple with the challenges of the 21st century, from pandemics to chronic diseases, the integration of machine learning tools, as evidenced by our research, will be pivotal in driving innovation, efficiency, and improved patient outcomes [43].…”

Section: Discussionmentioning

confidence: 97%

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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