自然语言处理用于评估COVID-19期间患者和医疗保健提供者之间的短信对话
Richard T Lester1, Matthew Manson1, Muhammed Semakula2,3
1Division of Infectious Diseases, Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada.
PLOS digital health
|January 15, 2025
概括
卢旺达卢旺达卢旺达卢旺达卢旺达卢旺达卢旺达卢旺达卢旺达
科学领域:
- 数字健康数字健康
- 公共卫生 公共卫生
- 传染病管理 传染病管理
背景情况:
- 社区隔离对于控制SARS-CoV-2等传染病至关重要.
- 了解隔离患者的需求和挑战对于有效的流行病应对至关重要.
- 卢旺达实施了国家数字健康服务WelTel,通过短信远程监控COVID-19患者.
研究的目的:
- 分析卢旺达WelTel计划中的短信模式和沟通主题.
- 为了了解孤立的SARS-CoV-2患者和接触者的经验.
- 评估自然语言处理 (NLP) 在分析流行病相关文本数据中的实用性.
主要方法:
- 从WelTel (2020年3月 - 2022年3月) 中注册的33081名患者 (病例和接触者) 提取了数据.
- 从国家COVID-19注册表中链接人口统计和临床数据.
- 将NLP主题分类模型应用于金亚鲁万达语和英语语文对话的样本,比较传统的ML和LLMs.
主要成果:
- 12,119个互动文本对话是由孤立的患者生成的.
- 关键话题包括症状 (69%),诊断 (38%) 和社会问题 (19%).
- 传统的ML模型在金亚兰达语数据上表现优于LLM,而LLM在英语数据上表现出色.
结论:
- 交互式短信有效地支持在大规模的流行病期间孤立的患者.
- NLP分析为影响孤立患者的医疗和社会因素提供了宝贵的见解.
- 结果可以为未来的流行病反应提供精确的公共卫生策略.
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