A Novel Framework for Biomedical Text Mining  

作　　者：Janyl Jumadinova Oliver Bonham-Carter Hanzhong Zheng Michael Camara Dejie Shi 

机构地区：[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

出　　处：《Journal on Big Data》2020年第4期145-155,共11页大数据杂志（英文）

基　　金：This research is supported by Natural Science Foundation of Hunan Province(No.2019JJ40145);Scientific Research Key Project of Hunan Education Department(No.19A273);open Fund of Key Laboratory of Hunan Province(2017TP1026).

摘　　要：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 multi-agent 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.

关 键 词：Biomedical text mining reinforcement learning MULTI-AGENT distributed text mining CLUSTER 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]