用指导方针驱动的临床决策支持,用于使用分层多标签深度学习方法的结肠镜患者
Junling Wu1,2, Jun Chen1,2, Hanwen Zhang1,2
1Medical School of Chinese PLA, Beijing 100853, China.
Chinese medical journal
|May 23, 2025
概括
一个新的临床决策支持系统 (CDSS) 使用先进的人工智能准确分析结肠镜报告. 该系统在识别癌症和正常病例方面表现出高性能,帮助医生和患者.
科学领域:
- 人工智能在医学中的应用
- 医疗保健的自然语言处理.
- 临床决策支持系统 临床决策支持系统
背景情况:
- 每年在中国进行超过2000万次结肠镜检查,这增加了对高效医疗保健解决方案的需求.
- 自动临床决策支持系统 (CDSS) 可以标准化医疗保健并减少医疗负担.
- 本研究的重点是开发一个具有语义识别的CDSS,用于结肠镜报告.
研究的目的:
- 为CDSS建立和验证一个可解释的,层次化的分类框架.
- 利用最先进的基于变压器的模型来准确分析结肠镜检查报告.
- 在多中心验证环境中评估CDSS的性能.
主要方法:
- 使用了302,965份结肠镜报告的数据集,采用了2041份记录以进行注释.
- 应用了层次标签 (5个主要标签,22个子标签),并使用基于BERT的方法 (BC,BWEC,E3BZ) 训练了模型.
- 该系统在五家医院验证了连续3177例结肠镜检查病例.
主要成果:
- E3BZ预训练模型实现了90.18%的整体准确率和69.14%的宏观F1得分.
- 该模型在癌症识别方面显示出100%的准确性,在正常病例方面显示出99.16%的准确性.
- 五家医院的外部验证显示了有利的一致性和良好的表现.
结论:
- 开发的CDSS提供了对结肠镜报告的高级语义识别,并提供了有价值的建议.
- 这种新的CDSS有可能成为医生和患者的重要工具.
- 层次化的多标签策略和预训练方法可以适应未来的医学文本分析.
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