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病历中的新颖性检测:基于文本压缩的测量实验

Novelty Detection in Patient Histories: Experiments with Measures Based on Text Compression
课程网址: http://videolectures.net/ida07_edsberg_ndiph/  
主讲教师: Ole Edsberg
开课单位: 挪威科技大学
开课时间: 2007-10-08
课程语种: 英语
中文简介:
回顾患者病史可能非常耗时,部分原因是咨询记录数量众多。通常,大多数笔记都包含很少的新信息。如果我们能够自动检测新颖的音符,则可以构建促进此任务和其他任务的工具。我们建议使用基于文本压缩的度量,作为Kolmogorov复杂度的近似值,用于对注释新颖性进行分类。我们定义了四个基于压缩和八个其他度量。我们评估他们预测从一般实践中存在与患者病史中的注释相关联的先前未见的诊断代码的能力。最好的测量方法显示出有前途的分类能力,虽然不足以作为临床工具单独服务,但作为考虑更多数据类型的系统的一部分可能是有用的。最佳的个体测量是连接的先前音符和当前音符之间的归一化不对称压缩距离。
课程简介: Reviewing a patient history can be very time consuming, partly because of the large number of consultation notes. Often, most of the notes contain little new information. Tools facilitating this and other tasks could be constructed if we had the ability to automatically detect the novel notes. We propose the use of measures based on text compression, as an approximation of Kolmogorov complexity, for classifying note novelty. We define four compression-based and eight other measures. We evaluate their ability to predict the presence of previously unseen diagnosis codes associated with the notes in patient histories from general practice. The best measures show promising classification ability, which, while not enough to serve alone as a clinical tool, might be useful as part of a system taking more data types into account. The best individual measure was the normalized asymmetric compression distance between the concatenated prior notes and the current note.
关 键 词: 自动检测; 文本压缩; 注释新颖性
课程来源: 视频讲座网
最后编审: 2019-04-27:lxf
阅读次数: 48