应用深度学习模型来预测医疗记录的诊断代码
Jakir Hossain Bhuiyan Masud1, Chen-Cheng Kuo1, Chih-Yang Yeh1
1Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 11031, Taiwan.
Diagnostics (Basel, Switzerland)
|July 14, 2023
概括
使用卷积神经网络 (CNN) 的深度学习模型可以从临床笔记中准确预测国际疾病分类 (ICD) -10代码. 这种人工智能方法通过自动化诊断编码来帮助医生,提高医疗保健的效率和准确性.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 临床文档 临床文档
背景情况:
- 国际疾病分类 (ICD) 代码对于医疗保健参考和计费至关重要.
- 从患者记录中手动编码ICD是耗时且容易出现错误的.
- 深度学习 (DL) 提供了自动化和提高ICD编码准确性的潜在解决方案.
研究的目的:
- 开发和评估一个深度学习模型,用临床笔记来预测ICD-10代码.
- 评估模型在不同医院部门的表现.
- 展示AI在简化临床编码过程中的潜力.
主要方法:
- 利用了来自大学医院门诊部 (2016年) 21,953份医疗记录的数据集.
- 应用自然语言处理 (NLP) 技术,包括Word2Vector,用于文本数据处理.
- 开发并训练了一种基于深度学习的卷积神经网络 (CNN) 模型.
主要成果:
- 在五个部门中,CNN模型在临床上取得了可接受的表现 (精度:0.53-0.96,回忆:0.85-0.99,F分数:0.65-0.98).
- 该模型在心脏病学中表现出卓越的性能 (精度:0.95,回忆:0.99,F-score:0.98).
- 观察到自动化ICD-10代码预测准确性的显著改善.
结论:
- 拟议的CNN模型有效地从临床笔记中预测ICD-10代码.
- 这种人工智能驱动的方法可以显著减少医生手动编码工作负担.
- 该系统有可能提高医疗保健机构的诊断准确性和运营效率.
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