用自然语言处理对患者对其医生的判断在基于网络的书面评论中的分类:算法开发和验证
Farrah Madanay1,2, Karissa Tu3,4, Ada Campagna5,6
1Sanford School of Public Policy, Duke University, Durham, NC, United States.
Journal of medical Internet research
|August 1, 2024
概括
先进的自然语言处理模型在线医生评论中准确地分类患者的判断,分析人际关系方式和技术能力. 这项技术可以对患者反进行大规模分析,从而提高对医疗保健质量的理解.
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
- 计算语言学 计算语言学
背景情况:
- 患者越来越多地使用网络评论来选择医生,但分析这种非结构化的文本是具有挑战性的.
- 传统的方法,如手工编码和字典,在准确性和规模上都有局限性.
- 先进的自然语言处理 (NLP) 为分析大量医生评论提供了一个解决方案.
研究的目的:
- 开发和验证NLP算法,用于在基于网络的医生评论中对患者的判断进行分类.
- 具体确定关于人际关系方式和技术能力的判断的存在和情绪 (价值).
- 克服先前方法在规模分析患者反方面的局限性.
主要方法:
- 训练并测试了一个变压器分类算法,强大优化BERT预训练方法 (RoBERTa),在2000个手工编码的医生评论.
- 开发了两个微调的Roberta模型来分类人际关系方式和技术能力判断.
- 根据200个手工编码的评论和345,053个评论的完整数据集验证了模型性能.
主要成果:
- 人际关系方式模型实现了90%的准确性 (F1得分为0.89).
- 技术能力模型的准确度达到90% (F1得分为0.90).
- 积极的判断与较高的星级评分相关,负面的判断与较低的评分相关,与先前的研究保持一致.
结论:
- 罗伯塔模型准确地根据医生的人际关系方式和技术能力对患者的判断进行分类.
- 经过验证的模型证明了先进的NLP在分析非结构化审查数据方面的潜力.
- 未来的应用可能会扩展到社交媒体和电子健康记录.
关键词:
罗伯特 罗伯特是一个人.医疗保健质量 医疗保健质量机器学习是机器学习.自然语言处理自然语言处理.患者护理 患者护理患者体验患者体验患者的判断 患者的判断患者撰写的评论是患者撰写的评论.心理学 心理学 心理学文字分类 文本分类 文本分类基于网络的医生评论更多相关视频
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