基于变压器的工具,用于在线健康信息的自动事实检查:发展研究研究.
Azadeh Bayani1,2, Alexandre Ayotte1,2, Jean Noel Nikiema1,2,3
1Laboratoire Transformation Numérique en Santé, LabTNS, Montreal, QC, Canada.
JMIR infodemiology
|January 15, 2025
概括
现在可以对在线健康信息进行自动的事实核查. 这项研究使用自然语言处理和PubMed来验证在线健康内容,提高信息可靠性.
科学领域:
- 自然语言处理自然语言处理.
- 生物信息学是一种生物信息学.
- 医疗信息学 医疗信息学
背景情况:
- 在线健康信息丰富,但错误信息带来了重大风险.
- 从虚假内容中辨别可靠的健康信息对公众来说是一个越来越大的挑战.
- 需要自动化,可靠的健康信息验证至关重要.
研究的目的:
- 引入一种新的自动化方法,用于在线健康信息的事实核查.
- 为了利用PubMed作为健康信息验证的真理的主要来源.
- 通过使用自然语言处理 (NLP) 变压器模型,提高健康信息的可信性.
主要方法:
- 通过BERT,BioBERT和SciBERT模型将538个健康网页分类为语义学,流行病学和管理.
- 使用WellcomeBertMesh和KeyBERT自动生成每个类别的PubMed查询.
- 从PubMed中提取了前20个相关文章,并应用了等号相似性和雅卡德距离来进行内容比较.
主要成果:
- 在内容分类方面,BERT模型取得了很高的表现 (93-96% F1分).
- 自动化PubMed查询成功检索了相关的系统性审查和元分析.
- 同位数相似性被证明比雅卡德距离更有效,尽管假阳性需要注意.
结论:
- 一项试点研究证明了在线健康信息事实核查的成功自动化方法.
- 整合PubMed内容可以自动识别可信的健康信息.
- 这种方法有可能提高数字健康资源的可信度.
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