AnEMIC:一种用于比较ICD编码模型的框架
Juyong Kim1, Abheesht Sharma2, Suhas Shanbhogue2
1Machine Learning Department, Carnegie Mellon University.
概括
AnEMIC是用于自动ICD编码的开源工具,解决了医疗保健中标准化基准测试的需求. 它简化了预处理,培训和评估,提供纠正的数据集和人工智能驱动的洞察力.
科学领域:
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
- 计算健康 计算健康
背景情况:
- 使用临床笔记进行手动诊断编码 (ICD编码) 是费时的,容易出现错误.
- 现有的自动ICD编码研究缺乏标准化的基准测试框架.
- 国际疾病分类 (ICD) 标准对于医疗保健数据管理至关重要.
研究的目的:
- 推出AnEMIC,一个开源的,用户友好的工具,用于自动ICD编码.
- 建立一个标准化的框架,用于对ICD编码模型进行比较.
- 提高从临床笔记中分配诊断代码的准确性和效率.
主要方法:
- 在自动ICD编码中开发了一个精简的数据预处理,模型培训和评估管道.
- 纠正了现有工程中存在的预处理错误.
- 集成可解释的人工智能用于模型分析和交互式演示用于实时推断.
主要成果:
- 提供了更正的数据集和预训练模型,并为自动ICD编码提供了权重.
- 展示了一个精简的工作流程,用于预处理,培训和评估.
- 通过可视化实现实时推断和模型可解释性.
结论:
- AnEMIC提供了一个标准化的框架,以推进自动ICD编码的研究.
- 该工具旨在帮助医疗保健专业人员利用自动编码解决方案.
- 开源AnEMIC促进可重复的ICD编码技术的研究和探索.
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