通过语言处理模型估计患者满意度:模型开发和评估
Shinichi Matsuda1, Takumi Ohtomo1, Masaru Okuyama2
1Drug Safety Division, Chugai Pharmaceutical Co Ltd, Tokyo, Japan.
JMIR formative research
|September 14, 2023
概括
这项研究开发了一种新的自然语言处理 (NLP) 模型,用在线疾病博客来量化患者满意度. 该模型准确地测量了患者的满意度,并检测了情绪变化,为医疗保健质量提供了见解.
科学领域:
- 自然语言处理 (NLP) 是一种自然语言处理.
- 医疗信息学 医疗信息学
- 心理医学 心理医学
背景情况:
- 在医疗保健中,患者满意度至关重要.
- 自然语言处理 (NLP) 从文本数据提供了先进的见解.
- 患者报告的数据往往有限.
研究的目的:
- 开发一个量化患者满意度的模型.
- 利用多样化的,患者撰写的文本数据进行满意度分析.
主要方法:
- 创建了一个基于神经网络的NLP模型.
- 使用了来自56357个日本疾病博客 (1994-2020) 的约2000万个句子的数据.
- 一种回归方法预测了患者满意度指数 (PSI).
主要成果:
- 该模型在量化患者满意度方面取得了卓越的效率 (斯皮尔曼的 ρ=0.832).
- 预测和实际PSI值显示高相关性.
- PSI显著降低了癌症后的通知,反映了情绪影响.
结论:
- 开发的模型量化了患者的不满和情绪变化.
- 这种方法可以识别日常医疗实践中的问题.
- 它提供了一种分析患者报告结果的新方法.
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