CyberKnife and Data Mining: Exploring Opportunities for Clinical Advancements

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F26%3A0200049" target="_blank" >RIV/00216305:26220/26:0200049 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-032-08452-1_18" target="_blank" >http://dx.doi.org/10.1007/978-3-032-08452-1_18</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-032-08452-1_18" target="_blank" >10.1007/978-3-032-08452-1_18</a>

Jazyk výsledku

angličtina

Název v původním jazyce

CyberKnife and Data Mining: Exploring Opportunities for Clinical Advancements

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

CyberKnife and Data Mining: Exploring Opportunities for Clinical Advancements

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Lecture Notes in Computer Science

ISBN

9783032084514

ISSN

—

e-ISSN

1611-3349

Počet stran výsledku

11

Strana od-do

219-229

Název nakladatele

Springer Science and Business Media Deutschland GmbH

Místo vydání

—

Místo konání akce

Canaria, Spain

Datum konání akce

16. 7. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—