从言语到行动? 关于自动情绪分析的范围审查来自在线来源和调查的患者体验评论和调查
Elma Jelin1, Lilja Charlotte Storset2, Rebecka M Norman3
1Health Services Research, Norwegian Institute of Public Health, Oslo, Norway elma.jelin@fhi.no.
BMJ health & care informatics
|October 22, 2025
概括
使用自然语言处理 (NLP) 的患者反的情感分析 (SA) 显示了进展,但先进的深度学习和人工智能方法在改善医疗质量方面未得到充分利用. 需要对其成本效益进行进一步的研究.
科学领域:
- 医疗保健信息学 医疗保健信息学
- 人工智能在医学中的应用
- 自然语言处理自然语言处理.
背景情况:
- 对患者反的自动分析提高了对手动审查的效率.
- 深度学习和人工智能的最新进展需要在患者体验研究中更新情感分析 (SA) 的综合.
- 2021年的一项审查强调了NLP和SA,但当前的进展需要当代的概述.
研究的目的:
- 绘制和总结最近的研究,将SA应用于非结构化的患者体验数据.
- 为医疗保健服务中SA应用提供最新的综合.
主要方法:
- 根据乔安娜·布里格斯研究所和PRISMA-ScR指南进行范围审查.
- 在Medline,CINAHL,Web of Science,Cochrane,Embase和APAPsycINFO搜索了从2020年1月到2024年3月的研究.
- 包括使用NLP分析患者反和开发/验证SA模型的研究;由两个独立审查员提取的数据.
主要成果:
- 包括30项研究,主要来自美国,欧洲和亚洲,重点关注来自在线平台的医院护理反.
- 基于规则的SA在18项研究中使用,监督机器学习 (ML) 在12项研究中使用,而深度学习模型仅在4项研究中使用.
- 很少有研究涉及到SA在医疗保健中的可视化或实际实施.
结论:
- 尽管取得了进展,但深度学习和生成人工智能在南非地区对患者体验数据的使用仍然不足.
- 对实施的有限关注阻碍了SA在改善医疗保健质量方面的作用.
- 未来的研究应该评估先进的方法及其成本效益相比传统的ML.
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