MedReadr:开发和评估一个基于规则的浏览器内自然语言处理算法,以估计消费者健康文章的可靠性
Joshua Winograd1, Autumn Kim2, Nikit Venishetty3
1Joan and Sanford I. Weill Medical College, Weill Cornell Medicine, New York, NY, USA.
Biomedicine hub
|October 9, 2025
概括
一个名为MedReadr的新工具使用自然语言处理 (NLP) 来自动评估在线健康文章的可靠性. 这种算法与手动评分有很强的一致性,改善了获得可靠医疗信息的机会.
科学领域:
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
- 卫生沟通健康沟通
背景情况:
- 在线健康信息质量不一致,这给患者和提供者带来了挑战.
- 评估健康内容可靠性的现有手动工具往往是低效或有限的.
- 需要对在线健康文章可靠性的自动评估.
研究的目的:
- 开发和验证MedReadr,一种新的自然语言处理 (NLP) 算法,用于消费者健康文章的自动可靠性评分.
- 评估MedReadr的性能与已建立的手动评分系统相比.
- 确定预测在线健康内容可靠性的关键特征.
主要方法:
- 开发了MedReadr,这是一个基于规则的NLP算法,集成到Web浏览器中.
- 使用QUEST和桑德维克评分系统对35种消费医疗产品进行了手动评估.
- 训练了一种多变量线性回归模型来预测手动可靠性得分,在20篇额外的文章中得到验证.
主要成果:
- MedReadr表现出强大的预测性能,R2=0.90 (发展) 和R2=0.83 (验证).
- 影响可靠性得分的关键特征包括文章货币,引用,情绪和特定内容短语.
- 该算法与手动评分系统达到了很高的一致性 (科恩的 κ > 0.6).
结论:
- MedReadr提供了一种自动化,透明的方法来评估在线健康文章的可靠性.
- 该工具有望改善数字健康素养和临床医生与患者的沟通.
- 建议在各种网络内容中进行进一步验证,以扩大其实用性.
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