从癌症患者叙述中检测具有严重性等级分类的不良事件信号
Satoshi Nishioka1, Masaki Asano1, Shuntaro Yada2
1Division of Drug Informatics, Keio University Faculty of Pharmacy, Tokyo, Japan.
Studies in health technology and informatics
|January 25, 2024
概括
使用深度学习模型从患者博客中检测不良事件 (AE) 信号可以改善癌症治疗. T5模型在识别AE方面表现最好,可能导致更早的干预和更好的患者生活质量.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
背景情况:
- 有效的不良事件 (AE) 管理对于优化抗癌治疗结果至关重要.
- 目前的临床监测可能会错过关键的AE信号,需要用于持续的,现实世界的患者监测方法.
- 早期发现AE可以促进及时干预,改善患者的预后和生活质量.
研究的目的:
- 开发和评估深度学习 (DL) 模型,用于检测和分类来自患者生成文本的AE信号.
- 评估不同DL架构 (BERT,ELECTRA,T5) 在从癌症患者博客中识别不同严重程度的AE的性能.
- 建立一种在传统临床环境之外早期检测AE信号的方法.
主要方法:
- 利用日本癌症患者博客的数据集作为AE信号检测的来源.
- 开发和训练了三个不同的DL模型:BERT,ELECTRA和T5,用于分类提及AE的博客文章.
- 使用F1分数来评估模型的性能,用于对具有等级≥1和等级≥2AEs的产品进行分类.
主要成果:
- 在这两项分类任务中,T5深度学习模型获得了最高的F1分数.
- 达到F1分数为0.85的等级≥1AE分类和0.53的等级≥2AE分类.
- 证明了使用DL来检测非结构化患者文本中的AE信号的可行性.
结论:
- 深度学习模型,特别是T5,可以有效地检测癌症患者博客中的不良事件信号.
- 这种方法可以更早地检测潜在的副作用,从而促进及时的医疗干预.
- 实施这些模型可以通过改善癌症护理中的AE管理来显著提高患者的生活质量.
关键词:
贝尔特 (BERT) 公司电力电器 (Electra) 是一个电器.T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T1 T1 T1 T1 T1 T1 T2 T1 T2 T1 T1 T1 T2 T1 T1 T2 T1 T1不良事件 (AE) 是一种不良事件.深度学习 (DL) 是指深度学习.自然语言处理 (NLP)生活质量 (QoL)社交媒体 社交媒体
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