一个基础系统的自然语言处理的审查应用到胃肠病学和肝病学
Matthew Stammers1,2,3,4, Balasubramanian Ramgopal5, Abigail Owusu Nimako5
1University Hospital Southampton, Tremona Road, Southampton, SO16 6YD, UK. m.stammers@soton.ac.uk.
BMC gastroenterology
|February 6, 2025
概括
胃肠病学中的自然语言处理 (NLP) 显示出从电子健康记录中提取数据的前景. 然而,目前的模型需要更大的透明度,验证和协作,以获得可靠的临床应用.
科学领域:
- 医疗信息学 医疗信息学
- 自然语言处理 (NLP) 是一种自然语言处理.
- 胃肠病学 胃肠病学
背景情况:
- 电子健康记录 (EHR) 包含大量非结构化的临床信息.
- 自然语言处理 (NLP) 提供了一种方法,可以从这些自由文本笔记中提取有价值的数据.
- 在胃肠病学中NLP的应用是一个具有重大潜力的新兴领域.
研究的目的:
- 审查NLP在胃肠病学研究中的现状.
- 评估方法论的严谨性,并识别研究缺口.
- 突出NLP在现场应用的未来方向.
主要方法:
- 在七个数据库中对2015年至2023年间发表的研究进行了系统的文献搜索.
- 如果描述了验证或NLP方法,则将研究纳入;如果缺乏验证,非英语,专注于非GI疾病或是重复的,则排除.
- 两名审查员提取了数据,并使用专门的检查清单评估了方法质量.
主要成果:
- 53项研究在各种胃肠病学子专业中使用NLP,其中结肠镜检查 (38.9%) 和肝病 (24.1%) 是最常见的.
- 有相当数量的研究缺乏患者人口统计数据 (43.4%) 或存在高验证偏差风险 (75.5%).
- 虽然概括性在66%的研究中被提及,但可解释性和代码共享仅在9.4%报告.
结论:
- NLP有效地从电子健康记录中提取临床信息,特别是对于结肠镜和放射学报告.
- 目前的NLP模型缺乏透明度,导致潜在的偏见和概括性问题.
- 增加临床参与,协作和数据和代码的开放共享对于推进胃肠病学NLP至关重要.
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