促进乳腺癌护理的公平性:利用自然语言处理技术分析代表性不足人群的治疗效果。

IF 4.1 Q1 HEALTH CARE SCIENCES & SERVICES
Jung In Park, Jong Won Park, Kexin Zhang, Doyop Kim
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引用次数: 0

摘要

研究目的该研究旨在开发自然语言处理(NLP)算法,以便从电子健康记录(EHR)的临床笔记中自动提取以患者为中心的乳腺癌治疗结果,尤其是针对代表性不足人群的妇女:研究使用了美国一家三级医院 2010 年至 2021 年的临床记录。研究采用了各种 NLP 技术,包括矢量化方法(词频-反文档频率 (TF-IDF)、Word2Vec、Doc2Vec)和分类模型(支持矢量分类、K-近邻 (KNN)、随机森林 (RF))。此外,还通过随机搜索和五重交叉验证进行了特征选择和优化:研究对 1000 份临床笔记中的 100 份进行了注释,使用 970 份笔记建立了文本语料库。TF-IDF和Doc2Vec与RF的结合表现出最高的性能,而Word2Vec的效果较差。RF 分类器的性能最好,但召回率较低,表明假阴性较多。KNN 由于对数据噪声敏感,召回率较低:本研究强调了使用 NLP 分析临床笔记以了解代表性不足人群的乳腺癌治疗结果的重要性。与 Word2Vec 相比,TF-IDF 和 Doc2Vec 模型能更有效地捕捉相关信息。研究观察到 RF 模型的召回率较低,这归因于数据集的不平衡性和临床笔记的复杂性:该研究开发了高性能的 NLP 管道,用于捕捉代表性不足人群的乳腺癌治疗结果,证明了文档级矢量化和集合方法在临床笔记分析中的重要性。研究结果为制定更公平的医疗保健战略提供了启示,并展示了在临床环境中更广泛应用 NLP 的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Advancing equity in breast cancer care: natural language processing for analysing treatment outcomes in under-represented populations.

Objective: The study aimed to develop natural language processing (NLP) algorithms to automate extracting patient-centred breast cancer treatment outcomes from clinical notes in electronic health records (EHRs), particularly for women from under-represented populations.

Methods: The study used clinical notes from 2010 to 2021 from a tertiary hospital in the USA. The notes were processed through various NLP techniques, including vectorisation methods (term frequency-inverse document frequency (TF-IDF), Word2Vec, Doc2Vec) and classification models (support vector classification, K-nearest neighbours (KNN), random forest (RF)). Feature selection and optimisation through random search and fivefold cross-validation were also conducted.

Results: The study annotated 100 out of 1000 clinical notes, using 970 notes to build the text corpus. TF-IDF and Doc2Vec combined with RF showed the highest performance, while Word2Vec was less effective. RF classifier demonstrated the best performance, although with lower recall rates, suggesting more false negatives. KNN showed lower recall due to its sensitivity to data noise.

Discussion: The study highlights the significance of using NLP in analysing clinical notes to understand breast cancer treatment outcomes in under-represented populations. The TF-IDF and Doc2Vec models were more effective in capturing relevant information than Word2Vec. The study observed lower recall rates in RF models, attributed to the dataset's imbalanced nature and the complexity of clinical notes.

Conclusion: The study developed high-performing NLP pipeline to capture treatment outcomes for breast cancer in under-represented populations, demonstrating the importance of document-level vectorisation and ensemble methods in clinical notes analysis. The findings provide insights for more equitable healthcare strategies and show the potential for broader NLP applications in clinical settings.

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来源期刊
CiteScore
6.10
自引率
4.90%
发文量
40
审稿时长
18 weeks
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