在癌症研究中分析电子健康记录和临床笔记的NLP:一篇评论
Muhammad Bilal1, Ameer Hamza2, Nadia Malik3
1Department of Pharmaceutical Outcomes and Policy (M.B.), University of Florida, Gainesville, Florida, USA; Department of Software Engineering (M.B.), National University of Computer and Emerging Sciences, Islamabad, Pakistan.
Journal of pain and symptom management
|February 2, 2025
概括
自然语言处理 (NLP) 在癌症研究中越来越多地使用电子健康记录 (EHR). 先进的深度学习模型显示出巨大的潜力,但通用性和临床整合仍然是改善患者结果的关键挑战.
科学领域:
- 计算生物学是一种计算生物学.
- 医疗信息学医学信息学
- 在瘤学瘤学.
背景情况:
- 电子健康记录 (EHR) 和临床笔记包含大量非结构化数据,对癌症研究有价值.
- 现有的文献评论往往只关注特定的癌症类型或NLP应用,在综合分析中留下了一个空白.
- 自然语言处理 (NLP) 为从这个复杂的临床文本中提取见解提供了强大的工具.
研究的目的:
- 为癌症研究中NLP应用提供广泛的审查,利用电子健康记录和临床笔记.
- 为了确定趋势,主要的NLP任务和经常研究的癌症类型.
- 突出在瘤学中NLP的挑战和未来方向.
主要方法:
- 在Scopus数据库中进行了全面的文献搜索.
- 确定并分析了2019年至2024年间发表的94项相关研究.
- 研究根据NLP任务,癌症类型,方法和数据集特征进行分类.
主要成果:
- 观察到NLP用于癌症研究的应用显著增加.
- 信息提取和文本分类是最常见的NLP任务.
- 乳腺,肺癌和结直肠癌是最受研究的;向深度学习和变压器模型的转变是显而易见的.
- 数据集的大小差异很大,挑战包括有限的概括性和临床工作流集成.
结论:
- 在癌症研究中,NLP技术对分析电子健康记录和临床笔记具有重大前景.
- 未来的研究应该优先提高模型的概括性,临床语言的稳定性,并扩展到研究不足的癌症.
- 将NLP整合到息治疗中,并解决伦理方面的考虑对于最大限度地影响癌症诊断,治疗和患者结果至关重要.
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