Applicability Assessment of Technologies for Predictive and Prescriptive Analytics of Nephrology Big Data.

Journal: Proteomics
Published Date: May 27, 2025

Abstract

The integration of big data into nephrology research will open new avenues for analyzing and understanding complex biological datasets, driving advances in personalized management of kidney diseases. This paper describes the multifaceted challenges and opportunities by incorporating big data in nephrology, emphasizing the importance of data standardization, advanced 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 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 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.

Authors

  • Riste Stojanov
    Faculty of Computer Science and Engineering, Ss Cyril and Methodius, University- Skopje, Skopje, the Former Yugoslav Republic of Macedonia.
  • Milos Jovanovik
    Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University in Skopje, North Macedonia (D.T., V.T., M.D., J.D., M.J.); Computer Science Department, Metropolitan College, Boston University, Boston, Massachusetts (D.T.); and Faculty of Computer and Information Science, University of Ljubljana, Slovenia (M.K., A.Ž., M.R.- Š.).
  • Sasho Gramatikov
    Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University in Skopje, Skopje, North Macedonia.
  • Igor Mishkovski
    Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University in Skopje, Skopje, North Macedonia.
  • Eftim Zdravevski
    Faculty of Computer Science and Engineering, Saints Cyril and Methodius University, Skopje, Macedonia.
  • Darko Sasanski
    Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University in Skopje, Skopje, North Macedonia.
  • Zorica Karapancheva
    Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University in Skopje, Skopje, North Macedonia.
  • Goce Spasovski
    Department of Nephrology, Ss. Cyril and Methodius University in Skopje, Skopje, North Macedonia.
  • Ivona Vasileska
    Faculty of Mechanical Engineering, University of Ljubljana, Ljubljana, Slovenia.
  • Tome Eftimov
    Jožef Stefan Institute, Ljubljana, Slovenia.
  • Wu Zhuojun
    Institute for Molecular Cardiovascular Research IMCAR, University Hospital, Aachen, Germany.
  • Joachim Jankowski
    Institute for Molecular Cardiovascular Research IMCAR, University Hospital, Aachen, Germany.
  • Dimitar Trajanov
    Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University in Skopje, North Macedonia (D.T., V.T., M.D., J.D., M.J.); Computer Science Department, Metropolitan College, Boston University, Boston, Massachusetts (D.T.); and Faculty of Computer and Information Science, University of Ljubljana, Slovenia (M.K., A.Ž., M.R.- Š.) dimitar.trajanov@finki.ukim.mk.

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