使用自然语言处理和主题建模进行临床报告分类
Efsun Sarioglu1, Hyeong-Ah Choi1, Kabir Yadav2
1Computer Science Department The George Washington University, Washington, DC, USA.
概括
这项研究增强了使用自然语言处理 (NLP) 和主题建模的紧急部门计算机断层扫描 (CT) 报告的分类. 使用修饰器过的NLP显著提高了创伤患者的伤害编码精度.
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.
- 机器学习 机器学习
背景情况:
- 电子健康记录包含大量的非结构化临床文本.
- 有效地处理和编码这些文本对于医疗决策至关重要.
- 需要准确分类紧急服务部 (ED) 计算机断层扫描 (CT) 报告.
研究的目的:
- 调查改善EDCT报告分类的技术.
- 开发一种使用NLP和机器学习的系统,用于编码创伤性轨道骨折受害者的临床重要损伤.
- 探索主题建模作为分类的替代文本表示.
主要方法:
- 使用自然语言处理 (NLP) 来从自由文本CT报告中提取结构化信息.
- 应用机器学习技术来分类受伤的存在.
- 采用主题建模来在低维空间中表示患者报告.
- 通过修改器和二进制主题分类来研究NLP特征过.
主要成果:
- 无论是NLP还是主题建模方法都提高了原始文本分类性能.
- 用修饰器过NLP代码产生了最好的分类结果.
- 主题建模提供了有效的维度减少,与NLP特征相比较的分类准确度.
- 二进制主题分类没有超过原始文本分类.
结论:
- 对于提高CT报告分类,NLP技术,特别是修饰器过,是有效的.
- 主题建模为临床文本的维度减少和分类提供了可行的替代方案.
- 进一步的研究可能会优化主题建模,以改善临床数据分析.
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