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相关概念视频

Cancer Survival Analysis01:21

Cancer Survival Analysis

342
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
342

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Endobronchial Ultrasound-guided Intratumoral Injection of Cisplatin for the Treatment of Isolated Mediastinal Recurrence of Lung Cancer
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从临床笔记中提取系统性抗癌疗法和反应信息,遵循RECIST定义.

Xu Zuo1, Ashok Kumar2, Shuhan Shen2

  • 1University of Texas Health Science Center, Houston, TX.

JCO clinical cancer informatics
|June 17, 2024
PubMed
概括

本研究引入了一种自然语言处理 (NLP) 系统,用于自动从临床笔记中提取癌症治疗和反应数据. 这种方法提高了评估抗癌治疗有效性的效率和可靠性.

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科学领域:

  • 在瘤学瘤学.
  • 医疗信息学 医疗信息学
  • 计算语言学 计算语言学

背景情况:

  • 手动RECIST (固体瘤响应评估标准) 从临床笔记中提取数据是劳动密集的.
  • 标准化的RECIST评估对于比较癌症治疗疗效至关重要.
  • 电子健康记录包含复杂的临床笔记,阻碍了手动数据收集.

研究的目的:

  • 开发和应用自然语言处理 (NLP) 技术,以自动化RECIST数据提取.
  • 尽量减少用于癌症治疗反应评估的手动数据收集工作.
  • 提高RECIST评估的一致性和可靠性.

主要方法:

  • 开发了一个混合NLP系统,结合机器学习,深度学习和基于规则的模块.
  • 该系统执行命名实体识别,断言分类,关系提取和文本规范化.
  • 针对特定领域的语言模型 (BioBERT,BioClinicalBERT) 用于治疗和响应提取.

主要成果:

  • 该NLP系统成功地提取,链接和总结了抗癌疗法和RECIST类反应.
  • 最好的模型在链接治疗和RECIST提及方面获得了0.66分.
  • 在关系正常化后,端到端的性能达到0.74,显示出显著的疗效.

结论:

  • 从临床笔记中开发和测试了一种用于癌症治疗和疗效数据的自动信息提取系统.
  • 该系统预计将通过改善癌症治疗方法的评估来支持未来的瘤研究.
  • 这种NLP方法提供了一种更有效,更可靠的方法来评估治疗的有效性.