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Different Data Mining Approaches Based Medical Text Data.

Wenke Xiao1, Lijia Jing2, Yaxin Xu1

  • 1School of Medical Information Engineering, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.

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|December 16, 2021
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Summary
This summary is machine-generated.

This study reviews data mining techniques for extracting knowledge from increasing medical text data. It analyzes algorithm applications and challenges to guide researchers in selecting appropriate methods.

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Area of Science:

  • Medical Informatics
  • Data Science
  • Natural Language Processing

Background:

  • Medical text data is rapidly expanding, containing vast amounts of valuable medical knowledge.
  • This data, being natural language, is semistructured, high-dimensional, and cannot be used in arithmetic operations.
  • Extracting actionable insights from this complex data is a critical challenge.

Purpose of the Study:

  • To review and analyze various data mining techniques applicable to medical text data.
  • To explore the advantages and disadvantages of different methods in the context of medical text processing.
  • To identify and discuss the primary challenges in medical text data mining.

Main Methods:

  • Literature review of data mining techniques for medical text.
  • Comparative analysis of algorithm strengths and weaknesses for medical text data.
  • Exploration of algorithm applications for user insights and specific medical challenges.

Main Results:

  • Identification of diverse data mining approaches for medical text.
  • Analysis of the suitability of techniques based on medical text characteristics.
  • Discussion of practical applications and user-centric insights derived from data mining.

Conclusions:

  • Researchers can benefit from this review to select appropriate data mining techniques for medical text.
  • Understanding the identified challenges is crucial for effective medical text data mining.
  • This work aids in navigating the complexities of extracting knowledge from medical literature.