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The integration of data mining with precision medicine is transforming healthcare by uncovering novel clinical insights and enhancing treatment accuracy in patients undergoing CyberKnife therapy. This pilot study explores the potential of data mining to improve patient outcomes by identifying hidden patterns and relationships within clinical data. We apply various data mining techniques, including classification, regression, clustering, and association rule mining, to analyze patient records, diagnostic information, and treatment outcomes. Leveraging advanced algorithms, we aim to refine disease prediction, optimize treatment plans, and support personalized medicine. Preliminary results indicate promising applications in predicting treatment success, identifying risk factors, and streamlining clinical decision-making. This research contributes to bridging the gap between data mining analytics and precision healthcare, opening new possibilities for advancing radiotherapy practices.

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CyberKnife and Data Mining: Exploring Opportunities for Clinical Advancements

  • 会议论文

  • 发表日期：

    2026年

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  • Jana Schwarzerova,
  • Libor Stefek,
  • Jiri Simpach,
  • Lubomir Pavliska,
  • Bogdan Walek,
  • Lukas Evin,
  • Valentýna Provazník,
  • Wolfram Weckwerth,
  • Stefan Reguli

摘要

The integration of data mining with precision medicine is transforming healthcare by uncovering novel clinical insights and enhancing treatment accuracy in patients undergoing CyberKnife therapy. This pilot study explores the potential of data mining to improve patient outcomes by identifying hidden patterns and relationships within clinical data. We apply various data mining techniques, including classification, regression, clustering, and association rule mining, to analyze patient records, diagnostic information, and treatment outcomes. Leveraging advanced algorithms, we aim to refine disease prediction, optimize treatment plans, and support personalized medicine. Preliminary results indicate promising applications in predicting treatment success, identifying risk factors, and streamlining clinical decision-making. This research contributes to bridging the gap between data mining analytics and precision healthcare, opening new possibilities for advancing radiotherapy practices.