Artificial Intelligence-Based Medical Data Mining.

Area of Science:

• Medical Informatics
• Data Science
• Artificial Intelligence

Background:

• Traditional text mining struggles with the exponential growth of unstructured medical publications.
• Existing methods are insufficient for analyzing the vast volume of open-source medical data.
• The need for advanced techniques to extract meaningful insights from medical literature is critical.

Purpose of the Study:

• To provide a comprehensive understanding of artificial intelligence-based data mining for medical data analysis.
• To review the application of AI text mining tools in exploring hidden features and correlations within medical data.
• To outline a standard data mining process and available tools for medical applications.

Main Methods:

• Review of current literature on AI-driven text mining in medical sciences.
• Explanation of the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework.
• Identification and categorization of common data mining tools and libraries for medical data.

Main Results:

• AI-based text mining offers superior capabilities for handling large-scale medical data compared to traditional approaches.
• The CRISP-DM methodology provides a structured framework for medical data mining projects.
• A range of AI tools and libraries are available to support various stages of medical data analysis.

Conclusions:

• Artificial intelligence is essential for overcoming the limitations of traditional text mining in the medical field.
• AI-powered text mining facilitates deeper insights and discovery from the growing body of medical literature.
• Adoption of AI and structured methodologies like CRISP-DM will advance medical data analysis and research.