Open Access
ARTICLE
A Novel Framework for Biomedical Text Mining
Janyl Jumadinova1, Oliver Bonham-Carter1, Hanzhong Zheng1,2,*, Michael Camara1, Dejie Shi3
1 Department of Computer Science, Allegheny College, Meadville, PA 16335, USA
2 Department of Computer Science, University of Pittsburgh, Pittsburgh, PA 15213, USA
3 School of Computer and Information Engineering, Hunan University of Technology and Business, Changsha, 410205, China
* Corresponding Author: Hanzhong Zheng. Email:
Journal on Big Data 2020, 2(4), 145-155. https://doi.org/10.32604/jbd.2020.010090
Received 15 June 2020; Accepted 25 October 2020; Issue published 24 December 2020
Abstract
Text mining has emerged as an effective method of handling and
extracting useful information from the exponentially growing biomedical
literature and biomedical databases. We developed a novel biomedical text
mining model implemented by a multi-agent system and distributed computing
mechanism. Our distributed system, TextMed, comprises of several software
agents, where each agent uses a reinforcement learning method to update the
sentiment of relevant text from a particular set of research articles related to
specific keywords. TextMed can also operate on different physical machines to
expedite its knowledge extraction by utilizing a clustering technique. We
collected the biomedical textual data from PubMed and then assigned to a multiagent biomedical text mining system, where each agent directly communicates
with each other collaboratively to determine the relevant information inside the
textual data. Our experimental results indicate that TexMed parallels and
distributes the learning process into individual agents and appropriately learn the
sentiment score of specific keywords, and efficiently find connections in
biomedical information through text mining paradigm.
Keywords
Cite This Article
J. Jumadinova, O. Bonham-Carter, H. Zheng, M. Camara and D. Shi, "A novel framework for biomedical text mining,"
Journal on Big Data, vol. 2, no.4, pp. 145–155, 2020.