TY - EJOU AU - Jumadinova, Janyl AU - Bonham-Carter, Oliver AU - Zheng, Hanzhong AU - Camara, Michael AU - Shi, Dejie TI - A Novel Framework for Biomedical Text Mining T2 - Journal on Big Data PY - 2020 VL - 2 IS - 4 SN - 2579-0056 AB - 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. KW - Biomedical text mining; reinforcement learning; multi-agent; distributed text mining; cluster DO - 10.32604/jbd.2020.010090