文本网络的可扩展社区提取,用于在医疗数据库中自动分组
Tomilayo Komolafe1, Allan Fong2, Srijan Sengupta3
1Qlik, 211 S Gulph Rd, King of Prussia, PA 19406.
概括
这项研究引入了一个可扩展的算法,用于文本网络中的社区提取,通过准确分组医疗错误报告来提高患者安全. 该方法的性能优于手动标记,增强了医疗保健数据库中的数据分析.
科学领域:
- 网络科学 网络科学
- 数据挖掘是一种数据挖掘.
- 医疗保健信息学 医疗保健信息学
背景情况:
- 社区结构是许多现实世界网络的关键特征.
- 现有的社区检测方法通常无法扩展到大型文本数据库.
- 文本网络对于分析医疗错误和患者安全至关重要.
研究的目的:
- 为大型文本数据库适应已知的社区提取方法.
- 在文本数据中开发一个可扩展的社区结构分析算法.
- 提高患者安全报告中识别模式的准确性.
主要方法:
- 适应一个成熟的社区提取算法.
- 开发一种可扩展的方法来进行文本网络分析.
- 应用到现实世界的患者安全报告数据库.
主要成果:
- 开发的算法有效地从大型文本数据库中提取社区结构.
- 算法生成的组比手动标记更加准确.
- 在患者安全报告数据库中证明有效性.
结论:
- 拟议的可扩展算法准确地识别文本网络中的社区结构.
- 该方法为患者安全数据的手动分析提供了优质的替代方案.
- 通过改进数据分组,增强了医疗错误和患者安全的分析.
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