无监督的大型语言模型用于识别癌症中心患者门户网站消息中的主题
Ji Hyun Chang1,2, Amir Ashraf-Ganjouei1, Isabel Friesner1
1Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA.
JCO clinical cancer informatics
|October 1, 2025
概括
患者门户消息激增,特别是调度询问,增加了医生工作量. 需要新的策略和人工智能来管理数量并防止倦怠.
科学领域:
- 医疗信息学 医疗信息学
- 瘤学通讯 瘤学通讯
- 自然语言处理自然语言处理.
背景情况:
- 患者门户增强了沟通,但增加了提供者信息量.
- 高信息量有助于医生倦怠.
研究的目的:
- 分析患者生成的门户消息的趋势和主题.
- 为了确定信息量对医疗保健提供者的影响.
主要方法:
- 利用BERTopic (一种自然语言处理技术) 和GPT-4进行主题建模和对超过220万个患者门户消息的分类.
- 采用统一的多重近似和投影用于可视化和学生的t测试用于体积分析.
- 分析了来自单一癌症中心 (2011-2023) 的信息.
主要成果:
- 消息量显著增加,从2012年的每月2,071个增加到2022年的43,430个.
- 在COVID-19大流行后观察到消息量显著增加.
- 与调度相关的消息最频繁,其次是症状和药物查询,尽管自动调度系统发生了变化,但没有减少.
结论:
- 患者门户信息的急剧增加需要改进的沟通策略,以减轻提供者的负担.
- 这些发现支持开发人工智能驱动的分类系统,以管理消息量和打击医生倦怠.
- 解决信息过载对于保持高质量的患者护理和提供者福祉至关重要.
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