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Journal of personalized medicine
Aug 24, 2022;12 (9):

Artificial Intelligence-Based Medical Data Mining.

Amjad Zia, Muzzamil Aziz, Ioana Popa, Sabih Ahmed Khan, Amirreza Fazely Hamedani, Abdul R Asif
Author Information
  1. Amjad Zia: Department for Clinical Chemistry/Interdisciplinary UMG Laboratories, University Medical Center, 37075 Göttingen, Germany. ORCID
  2. Muzzamil Aziz: Future Networks, eScience Group, Gesellschaft für Wissenschaftliche Datenverarbeitung mbH Göttingen (GWDG), 37077 Göttingen, Germany.
  3. Ioana Popa: Department for Clinical Chemistry/Interdisciplinary UMG Laboratories, University Medical Center, 37075 Göttingen, Germany.
  4. Sabih Ahmed Khan: Future Networks, eScience Group, Gesellschaft für Wissenschaftliche Datenverarbeitung mbH Göttingen (GWDG), 37077 Göttingen, Germany.
  5. Amirreza Fazely Hamedani: Future Networks, eScience Group, Gesellschaft für Wissenschaftliche Datenverarbeitung mbH Göttingen (GWDG), 37077 Göttingen, Germany.
  6. Abdul R Asif: Department for Clinical Chemistry/Interdisciplinary UMG Laboratories, University Medical Center, 37075 Göttingen, Germany.
PMID: 36143144 DOI: 10.3390/jpm12091359

Abstract

Understanding published unstructured textual data using traditional text mining approaches and tools is becoming a challenging issue due to the rapid increase in electronic open-source publications. The application of data mining techniques in the medical sciences is an emerging trend; however, traditional text-mining approaches are insufficient to cope with the current upsurge in the volume of published data. Therefore, artificial intelligence-based text mining tools are being developed and used to process large volumes of data and to explore the hidden features and correlations in the data. This review provides a clear-cut and insightful understanding of how artificial intelligence-based data-mining technology is being used to analyze medical data. We also describe a standard process of data mining based on CRISP-DM (Cross-Industry Standard Process for Data Mining) and the most common tools/libraries available for each step of medical data mining.

Keywords

artificial intelligence healthcare information machine learning medical data text mining

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Grants

  1. 16KIS1292/Federal Ministry of Education and Research
Journal Article Review
Article has an altmetric score of 4

Word Cloud

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