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利用码间关系进行医学编码分类
Medical Coding Classification by Leveraging Inter-Code Relationships |
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| 课程网址: |
http://videolectures.net/kdd2010_yan_mccl/
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| 主讲教师: |
Yan Yan
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| 开课单位: |
美国东北大学
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| 开课时间: |
2010-10-01
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| 课程语种: |
英语
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| 中文简介: |
医疗编码或分类是将患者医疗记录中包含的信息转换为标准预定义医疗代码的过程。全世界有几种与诊断和医疗程序相关的医疗编码惯例;然而，在美国，ICD的第九次修订（ICD 9）提供了编码临床记录的标准。准确的医疗编码很重要，因为它被医院用于保险计费目的。由于在出院后可以将患者分配或分类为多个ICD 9代码，因此编码问题可以被视为多标签分类问题。在本文中，我们引入了一个多标签大边缘分类器，它自动学习底层的代码间结构，并允许控制地结合关于医疗代码关系的先验知识。除了完善和学习代码关系之外，我们的分类器还可以利用此共享信息来提高其性能。在包含临床自由文本及其相关医疗代码的公开数据集上的实验表明，我们提出的多标签分类器在该问题中优于相关的多标签模型。
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| 课程简介: |
Medical coding or classification is the process of transforming information contained in patient medical records into standard predefined medical codes. There are several worldwide accepted medical coding conventions associated with diagnoses and medical procedures; however, in the United States the Ninth Revision of ICD(ICD-9) provides the standard for coding clinical records. Accurate medical coding is important since it is used by hospitals for insurance billing purposes. Since after discharge a patient can be assigned or classified to several ICD-9 codes, the coding problem can be seen as a multi-label classification problem. In this paper, we introduce a multi-label large-margin classifier that automatically learns the underlying inter-code structure and allows the controlled incorporation of prior knowledge about medical code relationships. In addition to refining and learning the code relationships, our classifier can also utilize this shared information to improve its performance. Experiments on a publicly available dataset containing clinical free text and their associated medical codes showed that our proposed multi-label classifier outperforms related multi-label models in this problem.
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| 关 键 词: |
医疗编码; 代码间结构; 多标签分类
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| 课程来源: |
视频讲座网
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| 最后编审: |
2020-10-22：chenxin
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| 阅读次数: |
59
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