Introduction to the special section on clinical data mining

Shipeng Yu, R. B. Rao
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引用次数: 1

Abstract

Mining clinical data is a fast-evolving field, ranging from mining patient data of a particular type (e.g., images, genomics) to mining the increased amount of mixed-format information (databases, free text, images, labs, etc) in electronic health records (EHR), to selecting, extracting and synthesizing relevant knowledge from large medical corpuses, to the promise of personalized medicine where therapy and prevention are tailored to smaller and smaller patient subpopulations, down to the individual patient. Clinical data mining can be a key asset in driving vast systemic improvements in healthcare, leading to improved patient outcomes and reduced healthcare costs. In this report we briefly survey the latest advancements in this field, and introduce four selected articles that cover both state-of-the-art data mining techniques for clinical data and discuss emerging clinical data mining applications.
介绍临床数据挖掘的特殊部分
挖掘临床数据是一个快速发展的领域,从挖掘特定类型的患者数据(例如,图像、基因组学)到挖掘电子健康记录(EHR)中越来越多的混合格式信息(数据库、自由文本、图像、实验室等),再到从大型医学语料库中选择、提取和合成相关知识,再到个性化医疗的承诺,即针对越来越小的患者亚群量身定制治疗和预防。具体到每个病人。临床数据挖掘可以成为推动医疗保健系统大规模改进的关键资产,从而改善患者的治疗效果并降低医疗保健成本。在本报告中,我们简要介绍了该领域的最新进展,并介绍了四篇精选的文章,这些文章涵盖了临床数据的最新数据挖掘技术,并讨论了新兴的临床数据挖掘应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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