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This paper presents a Biomedical Semantic-based Association Rule method that significantly reduces irrelevant connections through semantic filtering. The experiment result shows that compared to traditional association rule-based approach, our approach generates much fewer rules and a lot of these rules represent relevant connections among biological concepts.
We define and study a novel text mining problem for biomedical literature digital library, referred to as the class-attribute mining. Given a collection of biomedical literature from a digital library addressing a set of objects (e.g., proteins) and their descriptions (e.g., protein functions), the tasks of class-attribute mining include: (1) to identify and summarize latent classes in the space of...
Electronic medical records are important to manage health data and save lives to improve the quality of service in hospitals. Clinical medical records contain a wealth of information, largely in free-text form. This paper proposes a generic framework to semi-automatically extract and mine data from clinical note, automatically learn patterns for each physician's clinical notes, and automatically populate...
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