Detail publikačního výsledku

CyberKnife and Data Mining: Exploring Opportunities for Clinical Advancements

SCHWARZEROVÁ, J.; STEFEK, L.; SIMPACH, J.; PAVLISKA, L.; WALEK, B.; EVIN, L.; PROVAZNÍK, V.; WECKWERTH, W.; REGULI, S.

Originální název

CyberKnife and Data Mining: Exploring Opportunities for Clinical Advancements

Anglický název

CyberKnife and Data Mining: Exploring Opportunities for Clinical Advancements

Druh

Stať ve sborníku v databázi WoS či Scopus

Originální abstrakt

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.

Anglický abstrakt

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.

Klíčová slova

Clinical data | Cyberknife | Data mining | Machine Learning

Klíčová slova v angličtině

Clinical data | Cyberknife | Data mining | Machine Learning

Autoři

SCHWARZEROVÁ, J.; STEFEK, L.; SIMPACH, J.; PAVLISKA, L.; WALEK, B.; EVIN, L.; PROVAZNÍK, V.; WECKWERTH, W.; REGULI, S.

Vydáno

16.11.2025

Nakladatel

Springer Science and Business Media Deutschland GmbH

ISBN

9783032084514

Kniha

Lecture Notes in Computer Science

Periodikum

Lecture Notes in Computer Science

Stát

Švýcarská konfederace

Strany od

219

Strany do

229

Strany počet

11

BibTex

@inproceedings{BUT200049,
  author="Jana {Schwarzerová} and  {} and  {} and  {} and  {} and  {} and Valentýna {Provazník} and  {} and  {}",
  title="CyberKnife and Data Mining: Exploring Opportunities for Clinical Advancements",
  booktitle="Lecture Notes in Computer Science",
  year="2025",
  journal="Lecture Notes in Computer Science",
  pages="219--229",
  publisher="Springer Science and Business Media Deutschland GmbH",
  doi="10.1007/978-3-032-08452-1\{_}18",
  isbn="9783032084514"
}