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通过使用自然语言处理临床笔记的初级诊断对立体射线手术患者进行分类.

Mario Fugal1, David Marshall1, Alexander V Alekseyenko1

  • 1Medical University of South Carolina, Charleston, SC.

JCO clinical cancer informatics
|June 13, 2025
PubMed
概括

自然语言处理 (NLP) 改善了对立体射线手术 (SRS) 患者的电子健康记录的初级瘤识别. 这种NLP方法提高了比传统编码系统更高的准确性和效率.

科学领域:

  • 医疗信息学医学信息学
  • 计算瘤学是一种计算瘤学.
  • 自然语言处理自然语言处理.

背景情况:

  • 准确的原发性瘤诊断对于接受立体性放射性手术 (SRS) 的患者至关重要.
  • 使用国际疾病分类 (ICD) 代码的现有方法缺乏必要的细节,特别是转移性癌症.
  • 电子健康记录 (EHR) 包含有价值的诊断信息,但难以准确提取.

研究的目的:

  • 开发和评估一种自然语言处理 (NLP) 方法,以精确地从EHR中提取初级瘤组织学.
  • 改善ICD编码的局限性,以便在SRS患者中进行详细的瘤分类.
  • 加强特定组织学亚型的识别,这些亚型在ICD-10 CM.中没有被捕获.

主要方法:

  • 使用先进的NLP算法对患者电子健康记录进行文字分析.
  • 手动注释患者数据以训练和验证NLP模型.
  • 开发准确的初级瘤类型和组织学亚型分类的算法.

主要成果:

  • 在初级瘤分类的准确性方面取得了显著的改进.
  • 在提取详细的瘤组织学方面表现出更高的效率.
  • 成功识别了超出ICD-10CM代码提供的细粒度的组织学亚型.

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结论:

  • 在瘤学研究过程中,NLP提供了一种有价值的工具.
  • 改善了用于研究和临床试验的患者队列识别.
  • 有潜力提高运营效率,并最终改善SRS治疗患者的治疗结果.