对患者报告经验的概率情感和情感建模
Curtis Murray1, Lewis Mitchell2, Jonathan Tuke2
1The University of Adelaide, School of Computer and Mathematical Sciences, Australia; RMIT University, School of Computing Technologies, Australia; The University of Melbourne, School of Computing and Information Systems, Australia.
Artificial intelligence in medicine
|June 13, 2025
概括
这项研究引入了一种新方法,用于分析来自在线评论的患者反,以了解患者的情绪并提高医疗保健质量. 它揭示了患者与护理人员的互动显著影响患者体验,而不是临床结果.
科学领域:
- 自然语言处理自然语言处理.
- 医疗保健信息学 医疗保健信息学
- 计算语言学 计算语言学
背景情况:
- 传统的患者调查范围有限,成本高昂.
- 非结构化的在线患者反提供了丰富的见解,但难以分析.
- 从文本中解释患者情绪的方法的开发对于改善医疗保健质量至关重要.
研究的目的:
- 提出一种从患者报告的经验中对患者情绪进行可解释的概率模型的方法.
- 在患者反中揭示关键主题及其与情绪的关系.
- 开发一个特定于上下文的概率情感推系统.
主要方法:
- 超级数据网络主题建模应用于来自Care Opinion (2012-2022) 的13380名患者体验.
- 一个天真的贝叶斯分类器被用来预测多标签的情绪和二进制的情绪,用话题作为预测器.
- 使用nDCG,Q-测量和F1评分来评估性能,达到0.921.
主要成果:
- 患者的情绪 (积极性/消极性) 与患者与护理人员的互动密切相关,而不是临床结果.
- 积极的患者体验与教育参与度相关.
- 像解雇和拒绝这样的负面经历与自杀倾向和抑郁症有关.
结论:
- 拟议的方法提供了一种具有成本效益,及时和透明的方法来分析非结构化的患者反.
- 这种方法可以增强传统的患者体验收集,并提供情感洞察力.
- R包和仪表板促进了研究和临床实践的可访问性,使得量身定制的患者护理成为可能.
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