整合预测编码和以用户为中心的界面,以加强癌症登记数据的审计和质量
Hong-Jie Dai1,2,3,4, Chien-Chang Chen5, Tatheer Hussain Mir1,2
1Intelligent System Laboratory, Department of Electrical Engineering, College of Electrical Engineering and Computer Science, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan.
这项研究引入了一个人工智能系统,可以自动化从电子健康记录中编码癌症登记册,显著提高准确性并减少癌症登记员的手动工作.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 癌症登记数据管理 癌症登记数据管理
背景情况:
- 从电子健康记录 (EHR) 中手动抽取数据对于癌症注册人员来说是劳动密集型的.
- 准确的癌症注册数据对于研究,质量改善和患者护理至关重要.
研究的目的:
- 开发和评估混合自然语言处理 (NLP) 和专家系统,以简化癌症注册数据抽象.
- 提高识别肺癌注册相关概念和生成代码的效率和准确性.
主要方法:
- 开发了一个混合系统,将深度学习和基于规则的NLP结合起来,用于概念识别.
- 集成了一个符号专家系统,用于基于规则的加权注册表编码.
- 在医院信息系统中实施了患者旅程可视化平台.
主要成果:
- 该系统在肺癌数据集 (1,428名患者) 的30个编码项目中获得了高F1分数 (0.85-1.00).
- 登记员的反证实了该系统在协助和审计数据抽象方面的可靠性.
- 显著减少了数据抽象的劳动力和时间.
结论:
- 拟议的混合神经符号系统对于癌症注册表编码是有效和高效的.
- 该系统提高了注册人员结果的质量,并支持临床决策.
- 人工智能的进步可以优化癌症注册工作流程,并有助于改善临床结果.
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