生物医学知识发现的进展：25年回顾
Progress in Biomedical Knowledge Discovery: A 25-year Retrospective.
作者信息
Sacchi L, Holmes J H
机构信息
John H Holmes, Institute for Biomedical Informatics, University of Pennsylvania School of Medicine, 717 Blockley Hall, 423 Guardian Drive, Philadelphia, PA 19104, USA, Tel: 215-898-4833, Fax: 215-573-5325, E-Mail:
出版信息
Yearb Med Inform. 2016 Aug 2;Suppl 1(Suppl 1):S117-29. doi: 10.15265/IYS-2016-s033.
OBJECTIVES
We sought to explore, via a systematic review of the literature, the state of the art of knowledge discovery in biomedical databases as it existed in 1992, and then now, 25 years later, mainly focused on supervised learning.
METHODS
We performed a rigorous systematic search of PubMed and latent Dirichlet allocation to identify themes in the literature and trends in the science of knowledge discovery in and between time periods and compare these trends. We restricted the result set using a bracket of five years previous, such that the 1992 result set was restricted to articles published between 1987 and 1992, and the 2015 set between 2011 and 2015. This was to reflect the current literature available at the time to researchers and others at the target dates of 1992 and 2015. The search term was framed as: Knowledge Discovery OR Data Mining OR Pattern Discovery OR Pattern Recognition, Automated.
RESULTS
A total 538 and 18,172 documents were retrieved for 1992 and 2015, respectively. The number and type of data sources increased dramatically over the observation period, primarily due to the advent of electronic clinical systems. The period 1992- 2015 saw the emergence of new areas of research in knowledge discovery, and the refinement and application of machine learning approaches that were nascent or unknown in 1992.
CONCLUSIONS
Over the 25 years of the observation period, we identified numerous developments that impacted the science of knowledge discovery, including the availability of new forms of data, new machine learning algorithms, and new application domains. Through a bibliometric analysis we examine the striking changes in the availability of highly heterogeneous data resources, the evolution of new algorithmic approaches to knowledge discovery, and we consider from legal, social, and political perspectives possible explanations of the growth of the field. Finally, we reflect on the achievements of the past 25 years to consider what the next 25 years will bring with regard to the availability of even more complex data and to the methods that could be, and are being now developed for the discovery of new knowledge in biomedical data.
目的
我们试图通过对文献的系统回顾，探究1992年以及25年后即当下生物医学数据库中知识发现的发展现状，主要聚焦于监督学习。
方法
我们对PubMed进行了严格的系统检索，并使用潜在狄利克雷分配法来识别文献中的主题以及不同时间段内知识发现科学的趋势，并比较这些趋势。我们使用前五年的时间范围来限制结果集，使得1992年的结果集限制为1987年至1992年发表的文章，2015年的结果集限制为2011年至2015年发表的文章。这是为了反映在1992年和2015年这两个目标日期时研究人员及其他人员可获取的当前文献情况。检索词设定为：知识发现或数据挖掘或模式发现或模式识别，自动化。
结果
1992年和2015年分别检索到538篇和18172篇文献。在观察期内，数据来源的数量和类型急剧增加，这主要归因于电子临床系统的出现。1992年至2015年期间，知识发现领域出现了新的研究领域，机器学习方法得到了完善和应用，而这些方法在1992年还处于萌芽阶段或尚不为人所知。
结论
在25年的观察期内，我们确定了众多影响知识发现科学的发展，包括新数据形式的可用性、新的机器学习算法以及新的应用领域。通过文献计量分析，我们研究了高度异质数据资源可用性的显著变化、知识发现新算法方法的演变，并从法律、社会和政治角度考虑了该领域发展的可能原因。最后，我们反思过去25年的成就，以思考未来25年在更复杂数据的可用性以及为生物医学数据新知识发现而可能开发和正在开发的方法方面会带来什么。
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