PubMedMiner：挖掘并可视化PubMed中基于医学主题词（MeSH）的关联
PubMedMiner: Mining and Visualizing MeSH-based Associations in PubMed.
作者信息
Zhang Yucan, Sarkar Indra Neil, Chen Elizabeth S
机构信息
Department of Computer Science, Univ. of Vermont, Burlington, VT ; Department of Plant Biology, Univ. of Vermont, Burlington, VT.
Department of Computer Science, Univ. of Vermont, Burlington, VT ; Department of Microbiology & Molecular Genetics, Univ. of Vermont, Burlington, VT ; Center for Clinical & Translational Science, Univ. of Vermont, Burlington, VT.
出版信息
AMIA Annu Symp Proc. 2014 Nov 14;2014:1990-9. eCollection 2014.
The exponential growth of biomedical literature provides the opportunity to develop approaches for facilitating the identification of possible relationships between biomedical concepts. Indexing by Medical Subject Headings (MeSH) represent high-quality summaries of much of this literature that can be used to support hypothesis generation and knowledge discovery tasks using techniques such as association rule mining. Based on a survey of literature mining tools, a tool implemented using Ruby and R - PubMedMiner - was developed in this study for mining and visualizing MeSH-based associations for a set of MEDLINE articles. To demonstrate PubMedMiner's functionality, a case study was conducted that focused on identifying and comparing comorbidities for asthma in children and adults. Relative to the tools surveyed, the initial results suggest that PubMedMiner provides complementary functionality for summarizing and comparing topics as well as identifying potentially new knowledge.
生物医学文献的指数级增长为开发促进识别生物医学概念之间可能关系的方法提供了机会。医学主题词表（MeSH）索引代表了这些文献中许多高质量的摘要，可用于支持使用关联规则挖掘等技术的假设生成和知识发现任务。基于对文献挖掘工具的调查，本研究开发了一种使用Ruby和R实现的工具——PubMedMiner，用于挖掘和可视化一组MEDLINE文章中基于MeSH的关联。为了展示PubMedMiner的功能，进行了一项案例研究，重点是识别和比较儿童和成人哮喘的合并症。相对于所调查的工具，初步结果表明PubMedMiner在总结和比较主题以及识别潜在新知识方面提供了补充功能。
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