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临床数据挖掘简介。

Introduction to the mining of clinical data.

作者信息

Harrison James H

机构信息

Division of Clinical Informatics, Department of Public Health Sciences, University of Virginia, Suite 3181 West Complex, 1335 Hospital Drive, Charlottesville, VA 22908-0717, USA.

出版信息

Clin Lab Med. 2008 Mar;28(1):1-7, v. doi: 10.1016/j.cll.2007.10.001.

Abstract

The increasing volume of medical data online, including laboratory data, represents a substantial resource that can provide a foundation for improved understanding of disease presentation, response to therapy, and health care delivery processes. Data mining supports these goals by providing a set of techniques designed to discover similarities and relationships between data elements in large data sets. Currently, medical data have several characteristics that increase the difficulty of applying these techniques, although there have been notable medical data mining successes. Future developments in integrated medical data repositories, standardized data representation, and guidelines for the appropriate research use of medical data will decrease the barriers to mining projects.

摘要

包括实验室数据在内的在线医学数据量不断增加,这是一项重要资源,可为增进对疾病表现、治疗反应及医疗服务提供过程的理解奠定基础。数据挖掘通过提供一组旨在发现大数据集中数据元素之间的相似性和关系的技术来支持这些目标。目前,医学数据具有若干特性,增加了应用这些技术的难度,不过已有显著的医学数据挖掘成功案例。综合医学数据存储库、标准化数据表示以及医学数据适当研究使用指南等方面的未来发展将减少挖掘项目的障碍。

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