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使用自然语言处理自动识别随机控制瘤试验和分配瘤实体的管道

Paul Windisch^1,2, Fabio Dennstädt², Carole Koechli^1,2

¹Department of Radiation Oncology, Cantonal Hospital Winterthur, Winterthur, Switzerland.

|June 15, 2025

概括

这项研究表明,将医学出版物分类为随机对照试验 (RCT) 或非随机对照试验,以及与瘤学相关或非相关的,是可行的. 这种专门的分类使得瘤学RCT的数据处理更有效.

科学领域:

生物医学信息学生物医学信息学
自然语言处理自然语言处理.
临床试验临床试验

背景情况:

一般信息提取工具缺乏领域特异性.
针对特定领域的试验检索可以改善数据处理.

研究的目的:

将医学出版物分类为随机对照试验 (RCT) 与非RCT以及瘤学与非瘤学主题.
为此分类任务评估小型变压器模型和大型语言模型 (GPT-4o,GPT-4o mini) 的性能.
开发一个基于规则的系统,从瘤RCT中提取瘤实体.

主要方法:

训练了一个小型变压器模型,用于对RCT状态和瘤学主题的二元分类.
在相同的分类任务中使用了GPT-4o和GPT-4o mini.
开发了一个基于规则的系统,从分类的瘤RCT中提取瘤实体.

主要成果:

小变压器在RCT分类中获得了0.96的F1评分,在瘤学分类中获得了0.84的F1评分.
GPT-4o获得了RCT分类的0.94和瘤学分类的0.91的F1分数.
基于规则的系统准确地将所有瘤RCT分配给瘤实体.

结论:

将出版物归类为随机对照瘤学试验是可行的.
这种专业的分类以基于规则的系统和专用模型为下游处理提供了便利.

关键词:

基于证据的医学是基于证据的医学.机器学习是机器学习.发生转移的转移.自然语言处理自然语言处理.在瘤学瘤学.随机对照试验是随机对照试验.变压器变压器变压器瘤实体是一个瘤实体.

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