比较生成和提取方法,从描述随机临床试验的摘要中提取信息
Christian Witte1, David M Schmidt2, Philipp Cimiano1
1Semantic Computing Group, Center for Cognitive Interaction Technology, Bielefeld University, Inspiration 1, Bielefeld, 33619, NRW, Germany.
Journal of biomedical semantics
|April 23, 2024
概括
本研究介绍了两种新的方法,可以自动从随机对照试验 (RCT) 中提取结构化的患者,干预,比较和结果 (PICO) 信息. 提取方法在结构化临床试验数据方面表现出卓越的表现,加速了基于证据的医学.
科学领域:
- 自然语言处理自然语言处理.
- 医疗信息学 医疗信息学
- 基于证据的医学基于证据的医学.
背景情况:
- 随机对照试验 (RCT) 的系统审查对于基于证据的医学至关重要,但耗时且昂贵.
- 自动提取患者,干预,比较和结果 (PICO) 元素可以显著加快系统性审查的创建.
- 现有的方法往往侧重于文本跨度,而不是对PICO信息的结构化表示.
研究的目的:
- 开发和评估两个不同的方法,自动从RCT摘要中提取结构化的PICO信息.
- 将PICO元素表示为结构化模板,有插槽来捕捉复杂的试验细节.
- 提高系统审查生成的效率和可访问性.
主要方法:
- 一种提取方法,建立在先前的工作基础上,增强了文档表示和集群,例如模板推断.
- 一种利用序列对序列模型编码摘要和解码结构化试验信息 (武器,治疗,终点,结果) 的生成方法.
- 这两种方法都在211个临床试验摘要的手动注释数据集上进行了评估,用于2型糖尿病和玻璃眼.
主要成果:
- 提取方法,特别是在flan-t5基模型中,获得了最高的F1得分:2型糖尿病的0.547和玻璃眼的0.636.
- 与2型糖尿病摘要相比,玻璃眼摘要的性能通常更高.
- 与提取方法相比,生成方法在结果中表现出更高的标准偏差.
结论:
- 两种开发的方法都显示出从RCT中提取结构化的PICO信息的前景,优于以不太结构化数据为重点的方法.
- 提取方法在两种疾病中都产生了更好的表现,在玻璃眼中具有更显著的优势.
- 未来的研究应该探索基准模型大小对性能的影响,并进一步完善生成方法.
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