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在电子医疗记录中预处理叙事文本以识别医院不良事件:范围审查

Hamed Jafarpour¹, Guosong Wu², Cheligeer Ken Cheligeer²

¹Concordia University, Gina Cody School of Engineering and Computer Science, Concordia Institute for Information Systems Engineering, 1515 Sainte Catherine West, Montreal, H3G 2W1, Quebec, Canada.

Artificial intelligence in medicine

|October 10, 2025

概括

本综述强调了用于医院不良事件 (HAE) 检测的叙事电子病历 (EMR) 预处理方面的挑战. 解决数据预处理和系统需求对于提高HAE识别精度至关重要.

科学领域:

自然语言处理 (NLP) 是一种自然语言处理.
机器学习 (ML) 是指机器学习.
大型语言模型 (LLM)

背景情况:

叙述式电子病历 (EMR) 包含有价值的临床数据,但由于其独特的文本特性,需要大量的预处理.
有效的EMR预处理对于提高数据质量和医疗保健文档的一致性至关重要.
在NLP,ML和LLM的进步使得利用EMR来检测医院不良事件 (HAE) 成为可能.

研究的目的:

进行对预处理叙事EMR用于HAE识别的范围审查.
确定这一领域的挑战,研究缺口和最佳实践.
用临床文本数据为研究人员提供HAE检测的见解.

主要方法:

遵守PRISMA-ScR系统范围审查指南.
开发和使用详细的范围审查协议.
在九个工程和医疗电子数据库进行了全面的搜索.

主要成果:

从3,264项检索中,包括了48项独特的研究.
在预处理叙事EMR以检测HAE时发现了挑战.
突出了三个关键的研究缺口:预处理管道,强大的数据管理系统和时间事件系统.
强调了N-gram提取, lemmatization/stemming和语义特征提取对HAE检测性能的重要性.

结论:

预处理任务显著提高了HAE检测性能.
解决了被忽视的研究缺口,这对于推进HAE检测至关重要.
需要进行进一步的研究,以解决发现的差距,并改进使用叙事EMR的HAE检测.

关键词:

临床文字临床文本医院的不良事件发生在医院.大型语言模型.叙述式的EMR可以说是故事式的EMR.自然语言处理自然语言处理.预处理预处理

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