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Data Mining in Biomedicine: Current Applications and Further Directions for Research

机译:生物医学中的数据挖掘:当前的应用和进一步的研究方向

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Data mining is the process of finding the patterns, associations or relationships among data using different analytical techniques involving the creation of a model and the concluded result will become useful information or knowledge. The advancement of the new medical deceives and the database management systems create a huge number of data-bases in the biomedicine world. Establishing a methodology for knowledge discovery and management of the large amounts of heterogeneous data has become a major priority of research. This paper introduces some basic data mining techniques, unsupervised learning and supervising learning, and reviews the application of data mining in biomedicine. Applications of the multimedia mining, including text, image, video and web mining are discussed. The key issues faced by the computing professional, medical doctors and clinicians are highlighted. We also state some foreseeable future developments in the field. Although extracting useful information from raw biomedical data is a challenging task, data mining is still a good area of scientific study and remains a promising and rich field for research.
机译:数据挖掘是使用涉及模型创建的不同分析技术在数据之间查找模式,关联或关系的过程,得出的结论将成为有用的信息或知识。新的医学欺骗手段和数据库管理系统的发展在生物医学领域创造了大量的数据库。建立用于大量异类数据的知识发现和管理的方法已成为研究的主要重点。本文介绍了一些基本的数据挖掘技术,无监督学习和监督学习,并综述了数据挖掘在生物医学中的应用。讨论了多媒体挖掘的应用,包括文本,图像,视频和Web挖掘。强调了计算机专业人员,医生和临床医生面临的关键问题。我们还陈述了该领域的一些可预见的未来发展。尽管从原始生物医学数据中提取有用的信息是一项艰巨的任务,但是数据挖掘仍然是科学研究的一个好领域,并且仍然是一个充满希望和丰富的研究领域。

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