NLP用于分析癌症研究中的电子健康记录和临床笔记:综述。

IF 3.5 2区 医学 Q2 CLINICAL NEUROLOGY
Muhammad Bilal PhD , Ameer Hamza MS , Nadia Malik MS
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引用次数: 0

摘要

本文综述了自然语言处理(NLP)技术在利用电子健康记录(EHRs)和临床记录进行癌症研究中的应用。它通过提供比以往专注于特定癌症类型或应用的研究更广阔的视角,解决了现有文献中的空白。在Scopus数据库中进行了全面的文献检索,确定了2019年至2024年间发表的94项相关研究。分析显示,NLP在癌症研究中的应用呈增长趋势,其中信息提取(47项研究)和文本分类(40项研究)成为主要的NLP任务,其次是命名实体识别(7项研究)。在癌症类型中,乳腺癌、肺癌和结直肠癌被研究得最多。从基于规则和传统的机器学习方法到先进的深度学习技术和基于变压器的模型的重大转变。研究发现,现有研究中使用的数据集大小差异很大,从手工注释的小型数据集到大型电子病历。该综述强调了主要的挑战,包括提出的解决方案的有限的普遍性,以及需要改进与临床工作流程的整合。虽然NLP技术在分析电子病历和癌症研究的临床记录方面显示出巨大的潜力,但未来的工作应侧重于提高模型的可泛化性,增强处理复杂临床语言的鲁棒性,并将其应用于尚未研究的癌症类型。将NLP工具整合到姑息医学中,并解决伦理问题,对于充分利用NLP在提高癌症诊断、治疗和患者预后方面的潜力仍然至关重要。本文综述了NLP在癌症研究中的应用现状和未来发展方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
NLP for Analyzing Electronic Health Records and Clinical Notes in Cancer Research: A Review
This review examines the application of natural language processing (NLP) techniques in cancer research using electronic health records (EHRs) and clinical notes. It addresses gaps in existing literature by providing a broader perspective than previous studies focused on specific cancer types or applications. A comprehensive literature search in the Scopus database identified 94 relevant studies published between 2019 and 2024. The analysis revealed a growing trend in NLP applications for cancer research, with information extraction (47 studies) and text classification (40 studies) emerging as predominant NLP tasks, followed by named entity recognition (7 studies). Among cancer types, breast, lung, and colorectal cancers were found to be the most studied. A significant shift from rule-based and traditional machine learning approaches to advanced deep learning techniques and transformer-based models was observed. It was found that dataset sizes used in existing studies varied widely, ranging from small, manually annotated datasets to large-scale EHRs. The review highlighted key challenges, including the limited generalizability of proposed solutions and the need for improved integration into clinical workflows. While NLP techniques show significant potential in analyzing EHRs and clinical notes for cancer research, future work should focus on improving model generalizability, enhancing robustness in handling complex clinical language, and expanding applications to understudied cancer types. The integration of NLP tools into palliative medicine and addressing ethical considerations remain crucial for utilizing the full potential of NLP in enhancing cancer diagnosis, treatment, and patient outcomes. This review provides valuable insights into the current state and future directions of NLP applications in cancer research.
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来源期刊
CiteScore
8.90
自引率
6.40%
发文量
821
审稿时长
26 days
期刊介绍: The Journal of Pain and Symptom Management is an internationally respected, peer-reviewed journal and serves an interdisciplinary audience of professionals by providing a forum for the publication of the latest clinical research and best practices related to the relief of illness burden among patients afflicted with serious or life-threatening illness.
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