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在西班牙儿科医疗报告中用于罕见疾病检测和分类的综合方法

Andres Duque^1,2, Lourdes Araujo^3,4, Juan Martinez-Romo^3,4

¹Departamento de Lenguajes y Sistemas Informáticos, ETS Ingeniería Informática, Universidad Nacional de Educación a Distancia (UNED), 28040, Madrid, Spain. aduque@lsi.uned.es.

Scientific reports

|October 31, 2025

概括

这项研究引入了一种用于在临床笔记中检测和分类罕见疾病的新系统. 最先进的监督模型显著优于半监督方法,改善了儿科患者记录中罕见疾病的识别.

科学领域:

生物医学自然语言处理
临床信息学临床信息学
罕见疾病研究罕见疾病研究

背景情况:

罕见疾病的检测和分类在生物医学文本分析中带来了重大挑战.
在临床笔记中准确识别罕见疾病对于患者护理和研究至关重要.

研究的目的:

在儿科临床笔记中开发和评估用于检测和分类罕见疾病的系统.
为了比较半监督和最先进的监督模型的性能,用于罕见疾病分类.

主要方法:

一个半监督的,基于关键短语的系统被开发用于最初的罕见疾病检测.
经过专家验证和改进,创建了一个整合数据集,包含1900个注释的罕见疾病提及.
实验使用半监督和监督 (歧视和生成) 模型进行分类.

主要成果:

最好的监督模型实现了78.74%的微平均F-Measure,比半监督系统 (67.37%) 提高了10%以上.
监督系统表现出强的表现,即使有有限的注释数据.
半监督模型显示了在罕见疾病研究中处理稀缺信息的潜力.

结论:

最先进的监督模型为临床文本中罕见疾病的检测和分类提供了有希望的结果.
监督方法显著提高了识别特定罕见疾病的准确性.
在罕见疾病信息学中,半监督方法在处理有限的数据时提供了宝贵的功能.

关键词:

基于关键短语的信息提取.大型语言模型.自然语言处理自然语言处理.检测罕见疾病的检测.西班牙医疗报告西班牙医疗报告

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