PubMedMiner: mining and visualizing MeSH-based associations in PubMed Zhang, Yucan author Sarkar, Indra Neil author Chen, Elizabeth S. author text papers (documents) 2014 eng Biomedical Literature Data Mining Asthma 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. This work was supported in part by the National Library of Medicine of the National Institutes of Health under award number R01LM011364. Student paper competition finalist at the 2014 AMIA Annual Symposium AMIA Annual Symposium Proceedings Nov 14 2014 2014-11-14 p. 1990-1999 PMID: 25954472 PMCID4419975 This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License