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Artificial Intelligence-Based Medical Data Mining

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, trad...

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Detalles Bibliográficos
Autores principales: Zia, Amjad, Aziz, Muzzamil, Popa, Ioana, Khan, Sabih Ahmed, Hamedani, Amirreza Fazely, Asif, Abdul R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9501106/
https://www.ncbi.nlm.nih.gov/pubmed/36143144
http://dx.doi.org/10.3390/jpm12091359
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author Zia, Amjad
Aziz, Muzzamil
Popa, Ioana
Khan, Sabih Ahmed
Hamedani, Amirreza Fazely
Asif, Abdul R.
author_facet Zia, Amjad
Aziz, Muzzamil
Popa, Ioana
Khan, Sabih Ahmed
Hamedani, Amirreza Fazely
Asif, Abdul R.
author_sort Zia, Amjad
collection PubMed
description 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.
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spelling pubmed-95011062022-09-24 Artificial Intelligence-Based Medical Data Mining Zia, Amjad Aziz, Muzzamil Popa, Ioana Khan, Sabih Ahmed Hamedani, Amirreza Fazely Asif, Abdul R. J Pers Med Review 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. MDPI 2022-08-24 /pmc/articles/PMC9501106/ /pubmed/36143144 http://dx.doi.org/10.3390/jpm12091359 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Zia, Amjad
Aziz, Muzzamil
Popa, Ioana
Khan, Sabih Ahmed
Hamedani, Amirreza Fazely
Asif, Abdul R.
Artificial Intelligence-Based Medical Data Mining
title Artificial Intelligence-Based Medical Data Mining
title_full Artificial Intelligence-Based Medical Data Mining
title_fullStr Artificial Intelligence-Based Medical Data Mining
title_full_unstemmed Artificial Intelligence-Based Medical Data Mining
title_short Artificial Intelligence-Based Medical Data Mining
title_sort artificial intelligence-based medical data mining
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9501106/
https://www.ncbi.nlm.nih.gov/pubmed/36143144
http://dx.doi.org/10.3390/jpm12091359
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