@Article{jbd.2020.010090,
AUTHOR = {Janyl Jumadinova, Oliver Bonham-Carter, Hanzhong Zheng, Michael Camara, Dejie Shi},
TITLE = {A Novel Framework for Biomedical Text Mining},
JOURNAL = {Journal on Big Data},
VOLUME = {2},
YEAR = {2020},
NUMBER = {4},
PAGES = {145--155},
URL = {http://www.techscience.com/jbd/v2n4/41032},
ISSN = {2579-0056},
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.},
DOI = {10.32604/jbd.2020.010090}
}