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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

Biomedical text mining; reinforcement learning; multi-agent; distributed text mining; cluster

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.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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