通过临床数据注释,分析和建模,了解患者门户信息中的主要和次要关注点
Yuqi Wu1, Yang Ren1,2, Heling Jia1
1Mayo Clinic, Rochester, MN, United States.
AMIA ... Annual Symposium proceedings. AMIA Symposium
|February 23, 2026
概括
这项研究使用人工智能分析患者门户消息,更有效地识别患者的担忧. 这有助于医疗保健提供者更快地响应患者需求,改善护理.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 有效的患者门户信息 (PPM) 管理对于以患者为中心的护理至关重要.
- 在PPM中了解患者的担忧对于及时干预至关重要.
- 越来越多的PPM给医疗保健提供者带来了挑战.
研究的目的:
- 在2,239个患者门户消息中注释和分析患者关注点.
- 开发和评估人工智能驱动的方法,用于自动化患者关注的识别和分类.
- 通过使用集成的人工智能模型,提高初级患者关注点识别的准确性.
主要方法:
- 2,239个患者门户信息的手动注释,以确定主要和次要问题.
- 利用预先训练的语言模型进行二进制分类,以检测所有患者的担忧.
- 实施多类分类与集成卷积神经网络,以确定主要关注点.
主要成果:
- 人工智能方法成功地在PPM中识别和分类了患者的担忧.
- 二元分类有效地发现了患者担忧的存在.
- 通过CNN增强的多类分类显示出在识别患者主要担忧方面具有重大潜力.
结论:
- 人工智能在管理越来越多的患者门户消息方面显示出巨大的潜力.
- 自动化分析有助于迅速识别和解决患者的医疗保健需求.
- 这种方法可以导致更有效和及时的医疗干预,改善患者护理.
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