自动化实体数据整合改善了癌症预测结果
Justin Jee1, Christopher Fong1, Karl Pichotta1
1Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Nature
|November 7, 2024
概括
通过自然语言处理 (NLP) 和基因组信息利用非结构化健康数据, 显著改善了癌症预测结果的模型. 这种方法提高了对临床基因组关系的理解,以获得更好的患者护理.
科学领域:
- 癌症学
- 生物信息学
- 医疗信息学
背景情况:
- 数字化健康记录和瘤DNA测序为癌症研究提供了丰富的数据.
- 患者数据通常存在于非结构化的文本和孤立的数据集中,限制了全面的分析.
- 整合多种数据来源对于推进精确瘤学至关重要.
研究的目的:
- 通过将NLP注释与结构化临床和基因组数据相结合,创建一个协调的临床基因组实践数据集 (MSK-CHORD).
- 利用这一数据集发现新的临床基因组关系.
- 开发和验证用于预测患者结果的机器学习模型,包括整体存活率和转移.
主要方法:
- 综合自然语言处理 (NLP) 标注与24950名患者的结构化数据 (药物,人口统计,瘤登记,基因组学).
- 开发了Memorial Sloan Kettering-Cancer Research Commons (MSK-CHORD) 数据集,其中包括肺癌,乳腺癌,结直肠癌,前列腺癌和胰腺癌的数据.
- 通过使用NLP和基因组数据来预测总体生存和转移潜力的训练机器学习模型.
主要成果:
- 结合NLP衍生特征 (例如疾病部位) 的机器学习模型仅基于基因组数据或癌症阶段的模型来预测整体存活率.
- MSK-CHORD数据集使得在较小的数据集中无法发现临床基因组关系.
- 已确定转移到特定器官部位的预测因素,包括SETD2突变与肺腺癌转移潜力降低之间的验证关联.
结论:
- 使用NLP对非结构化临床笔记进行自动注释是可行的,对于预测患者的结果是有价值的.
- 综合临床基因组数据集 (MSK-CHORD) 显著提高了发现复杂癌症决定因素的能力.
- MSK-CHORD数据集被发布为公共资源,以促进现实世界瘤研究.
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