Michael Camara
ABSTRACT
Due to the expanding growth of information in the biomedical literature and biomedical databases, researchers and practitioners in the biomedical field require efficient methods of handling and extracting useful information. We present a novel framework for biomedical text mining based on a learning multi-agent system. Our distributed system comprises of several software agents, where each agent uses a reinforcement learning method to update the sentiment of a relevant text from a particular set of research articles related to specific keywords. Our system was tested on the biomedical research articles from PubMed, where the goal of each agent is to accrue utility by correctly determining the relevant information that is communicated with other agents. Our results tested on the abstracts collected from PubMed related to muscular atrophy, Alzheimer's disease, and diabetes show that our system is able to appropriately learn the sentiment score related to specific keywords by parallel and distributed analysis of the documents by multiple software agents.
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Index Terms
- A multi-agent system with reinforcement learning agents for biomedical text mining
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