根据患者的治疗过程,用德国癌症登记数据对乳腺癌患者进行分组
Kolja Blohm1, David Korfkamp1, Florian Oesterling2
1Research & Development Division Health, OFFIS - Institute for Information Technology, Oldenburg, Germany.
Methods of information in medicine
|December 10, 2025
概括
集群乳腺癌患者治疗数据揭示了具有不同生存结果的不同群体,突出了潜在的数据质量问题,并有助于获得癌症注册的见解.
科学领域:
- 在瘤学瘤学.
- 数据科学数据科学数据科学
- 生物信息学是一种生物信息学.
背景情况:
- 癌症登记册收集了全面的患者数据,但分析是复杂的.
- 机器学习,特别是聚类,可以揭示治疗数据中隐藏的模式.
研究的目的:
- 开发一种方法,根据治疗过程对乳腺癌患者进行分组.
- 展示聚类对洞察力,数据质量和模式识别的实用性.
主要方法:
- 开发了一种基于距离的Levenshtein疗程相似度测量方法.
- 综合诊断,手术,放射治疗和全身疗法.
- 通过专家审查和生存分析对17822例乳腺癌病例进行了评估.
主要成果:
- 聚类产生了具有意想不到模式和潜在数据不一致性的临床可信组.
- 生存分析显示了群体之间的显著差异.
- 确定了具有不同生存结果的患者群体.
结论:
- 治疗过程聚类可以识别数据质量问题和具有不同预后形状的患者群体.
- 支持癌症登记处的探索性分析和质量监测.
- 未来的工作应该提高数据的完整性,并纳入更多的临床变量.
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