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Towards a canonical representation for machine understanding of natural language in radiology reports.

机译：争取在放射学报告中对自然语言进行机器理解的规范表示。

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A large amount of clinical data is recorded in health-care centers in the United States as a result of routine clinical care. This collection of data captures snapshots of disease processes across a large number of patients over time, presenting the possibility for discovering new scientific knowledge. However, the utility of this information is largely unfulfilled as the majority of today's clinical data is not directly amenable to scientific analysis, primarily due to the largely unstructured representation of the medical record contents that are not directly computer understandable. This dissertation investigates methods to transform anatomical knowledge found in free-text radiology reports to a representation more suitable for computational analysis.

机译：作为常规临床护理的结果，在美国的医疗中心记录了大量的临床数据。随着时间的流逝，这些数据收集可以捕获大量患者的疾病过程快照，从而为发现新的科学知识提供了可能性。但是，由于大多数当今的临床数据不能直接进行科学分析，因此该信息的实用性在很大程度上无法实现，这主要是由于病历内容的很大程度上非结构化表示无法通过计算机直接理解。本文研究了将自由文本放射学报告中发现的解剖学知识转换为更适合计算分析的表示方法。

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作者
Bashyam, Vijayaraghavan.;
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作者单位

University of California, Los Angeles.;

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授予单位 University of California, Los Angeles.;
学科 Information Science.;Artificial Intelligence.;Computer Science.
学位 Ph.D.
年度 2008
页码 140 p.
总页数 140
原文格式 PDF
正文语种 eng
中图分类信息与知识传播;自动化技术、计算机技术;人工智能理论;
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Towards a canonical representation for machine understanding of natural language in radiology reports.

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