通过使用ChatGPT的患者体验评估来确定医疗保健需求
Jiaxuan Li1, Yunchu Yang1, Rong Chen2
1Faculty of Applied Sciences, Macao Polytechnic University, Macao, China.
PloS one
|March 18, 2025
概括
大型语言模型 (LLM) 与面向基于情感分析 (ABSA) 模板相结合,有效分析患者评论,改善医疗保健需求识别和资源分配. 这种方法提高了患者的体验,并优化了医疗保健服务.
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
- 医疗保健中的人工智能
背景情况:
- 患者在线评论为数据挖掘和改善医疗保健质量提供了宝贵的见解.
- 了解患者的观点对于提高护理质量和患者体验至关重要.
- 大型语言模型 (LLM) 在数据挖掘和医疗管理方面显示出巨大潜力.
研究的目的:
- 提出一种利用LLMs的方法论,用于分析患者体验评论.
- 通过使用LLM进步的在线评论,有效地了解患者的医疗保健需求.
主要方法:
- 使用了来自haodf.com.com的504,198个患者评论.
- 开发了基于方面的情感分析 (ABSA) 模板,将评论分类为三个关键领域.
- 将ABSA模板集成到ChatGPT提示中,使用思维链来识别患者的需求.
主要成果:
- 实现了0.944的加权总精度,超过了直接叙事任务 (0.890).
- 权重总回忆和F1分数分别达到0.884和0.912,超过了直接叙事分数.
- 在三个采样方法中显示了超过91.5%的平均准确性.
结论:
- 将ChatGPT与ABSA模板相结合,为患者评价分析提供了令人满意的结果.
- 这种方法适用于其他LLM,以了解患者和健康消费者的需求.
- 该方法有助于增强患者体验和优化医疗保健资源配置.
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