从自由文本放射学报告中提取基于大型语言模型的信息:一个范围审查协议
Daniel Reichenpfader1, Henning Müller2,3, Kerstin Denecke4
1Institute for Patient-centered Digital Health, Bern University of Applied Sciences, Bern, Switzerland daniel.reichenpfader@bfh.ch.
BMJ open
|December 9, 2023
概括
本范围审查审查使用大语言模型 (LLM) 从放射学报告中提取信息. 它强调了LLMs在放射学研究中二次数据使用的潜力.
科学领域:
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
- 放射学 放射学是一门学科.
背景情况:
- 放射学报告包含有价值的自由文本数据,目前这些数据在研究等次要目的中未得到充分利用.
- 使用自然语言处理 (NLP) 的信息提取 (IE) 可以解锁这些数据.
- 大型语言模型 (LLM) 代表了NLP的重大进步,提高了IE的性能.
研究的目的:
- 通过使用LLMs的自由文本放射学报告对IE研究现状进行范围审查.
- 调查基于LLM的放射学IE中应用的方法.
- 为未来的研究指导确定目前基于LLM的方法中的挑战和局限性.
主要方法:
- 根据JBI"证据综合手册"第11.2.2章设计的协议.
- 定义了包含标准和在四个主要数据库 (PubMed,IEEE Xplore,Web of Science,ACM数字图书馆) 中的全面搜索策略.
- 详细描述选过程,数据图表,分析和提取数据的呈现.
主要成果:
- 该部分将在审查完成后填写.
- 该审查旨在综合LLM在放射学报告IE中的应用的发现.
- 预计将识别趋势,常用方法和当前文献中的差距.
结论:
- 本协议概述了范围审查,以解决缺乏基于LLM的放射学IE的全面概述的问题.
- 这些发现将通过突出有效的方法和需要进一步研究的领域来指导未来的研究.
- 结果将发表在一个专注于生物医学信息学/数字健康的开放获取期刊上.
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