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Applicability Assessment of Technologies for Predictive and Prescriptive Analytics of Nephrology Big Data
  • +6
  • Riste Stojanov,
  • Milos Jovanovik,
  • Sasho Gramatikov,
  • Igor Mishkovski,
  • Eftim Zdravevski,
  • Goce Spasovski,
  • Ivona Vasileska,
  • Tome Eftimov,
  • Dimitar Trajanov
Riste Stojanov
Cyril and Methodius University in Skopje

Corresponding Author:riste.stojanov@finki.ukim.mk

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Milos Jovanovik
Cyril and Methodius University in Skopje
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Sasho Gramatikov
Cyril and Methodius University in Skopje
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Igor Mishkovski
Cyril and Methodius University in Skopje
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Eftim Zdravevski
Cyril and Methodius University in Skopje
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Goce Spasovski
Department of Nephrology, Medical Faculty, University St.Cyril and Methodius
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Ivona Vasileska
University of Ljubljana
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Tome Eftimov
Jozef Stefan Institute Department of Communication Systems
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Dimitar Trajanov
Cyril and Methodius University in Skopje
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Abstract

The integration of big data into nephrology research has opened new avenues for analyzing and understanding complex biological datasets, driving advancements in personalized management of cardiovascular and kidney diseases. This paper explores the multifaceted challenges and opportunities presented by big data in nephrology, emphasizing the importance of data standardization, sophisticated storage solutions, and advanced analytical methods. We discuss the role of data science workflows, including data collection, preprocessing, integration, and analysis, in facilitating comprehensive insights into disease mechanisms and patient outcomes. Furthermore, we highlight the potential of predictive and prescriptive analytics, as well as the application of large language models (LLMs), in improving clinical decision-making and enhancing the accuracy of disease predictions. The use of high-performance computing (HPC) is also examined, showcasing its critical role in processing large-scale datasets and accelerating machine learning algorithms. Through this exploration, we aim to provide a comprehensive overview of the current state and future directions of big data analytics in nephrology, with a focus on enhancing patient care and advancing medical research.
18 Aug 2024Submitted to PROTEOMICS
21 Aug 2024Submission Checks Completed
21 Aug 2024Assigned to Editor
21 Aug 2024Review(s) Completed, Editorial Evaluation Pending
30 Aug 2024Reviewer(s) Assigned