临床笔记的低成本算法,表型分类,以加强流行病学监测:一个案例研究
Javier Petri1, Pilar Barcena Barbeira2, Martina Pesce2
1Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Departamento de Computación, Argentina.
Journal of biomedical informatics
|April 10, 2025
概括
简单的自然语言处理模型可以有效地提高COVID-19监测的流行情报,即使数据有限. 这些具有成本效益的方法准确地识别了确诊病例,有助于在资源有限的环境中开展公共卫生反应.
科学领域:
- 公共卫生 公共卫生
- 传染病流行病学 传染病流行病学
- 医疗信息学 医疗信息学
背景情况:
- 新兴的流行病需要强大的流行病情报系统.
- 使用电子健康记录 (EHR) 进行基于事件的监测对于早期检测至关重要.
- 挑战包括有限的数据,不断演变的症状和非标准化的编码.
研究的目的:
- 通过在新出现的流行病背景下基于事件的监测来增强流行病情报.
- 以有限的疾病知识和数据来对预测COVID-19相关类别的EHR进行分类.
- 为资源有限的环境开发快速,具有成本效益的自然语言处理 (NLP) 方法.
主要方法:
- 利用NLP技术开发快速和经济高效的分类模型.
- 对训练和测试模型进行了注释,包括后勤回归和微调变压器.
- 使用F1分数和Pearson与官方病例计数的相关性来评估模型性能.
主要成果:
- 西班牙适应的变压器模型 (BETO Clínico,RoBERTa Clínico) 实现了高性能 (F1=88.13%,87.01%).
- 一个简单的物流回归 (LR) 模型的性能具有竞争力 (F1=85.09%),超过了更复杂的模型.
- LR和BETO Clínico显示与官方的COVID-19数据有很强的相关性,识别了未编码的病例.
结论:
- 简单,资源效率高的NLP方法可以产生与复杂方法相美的结果.
- BETO Clínico和LR模型与官方监测数据有很强的相关性.
- 这些发现支持 cost-effective 的流行病应对策略,特别是在资源有限的环境中.
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