促进乳腺癌护理的公平:用于分析代表性不足的人群治疗结果的自然语言处理
Jung In Park1, Jong Won Park2, Kexin Zhang3
1University of California Irvine, Irvine, California, USA junginp@uci.edu.
BMJ health & care informatics
|July 2, 2024
概括
开发了自然语言处理 (NLP) 算法,以从代表性不足的人群的电子健康记录中提取乳腺癌治疗结果. 文档层面的向量化和整体方法被证明是有效的,提高了医疗保健的公平性.
科学领域:
- 医疗信息学 医疗信息学
- 计算语言学 计算语言学
- 在瘤学瘤学.
背景情况:
- 电子健康记录 (EHR) 包含了以患者为中心的宝贵乳腺癌治疗结果.
- 手动提取这些数据是耗时且容易产生偏见的,特别是对于代表性不足的群体.
- 需要自动化方法来有效和准确地从临床笔记中捕捉治疗结果.
研究的目的:
- 开发和评估自然语言处理 (NLP) 算法,从EHR临床笔记中自动提取乳腺癌治疗结果.
- 专注于改善对代表性不足的患者群体的数据提取.
- 确定最有效的NLP技术来分析复杂的临床文本.
主要方法:
- 利用美国一家三级医院 (2010-2021) 的临床笔记.
- 应用了各种NLP技术,包括TF-IDF,Word2Vec和Doc2Vec用于矢量化.
- 采用了支持向量分类,K-最近邻居 (KNN) 和随机森林 (RF) 等分类模型,通过随机搜索和交叉验证进行特征优化.
主要成果:
- 使用TF-IDF和Doc2Vec结合随机森林 (RF) 实现了高性能.
- 与文档级别矢量化方法相比,Word2Vec的有效性较低.
- 射频分类器显示了最好的整体性能,尽管召回率较低,但由于数据不平衡和复杂性,显示了更多虚假负数的可能性.
结论:
- 在分析临床笔记以了解乳腺癌治疗结果方面,NLP对于分析临床笔记很重要,特别是在代表性不足的人群中.
- 文档级向量化 (TF-IDF,Doc2Vec) 和组合方法对于有效的临床笔记分析至关重要.
- 开发的NLP管道为公平的医疗保健策略提供了洞察力,并在临床环境中展示了广泛的适用性.
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