| dc.description |
We propose an information extraction framework to support automated construction of decision models in biomedicine. Our proposed technique classifies text-based documents from a large biomedical literature repository, e.g., MEDLINE, into predefined categories, and identifies important keywords for each category based on their discriminative power. Relevant documents for each category are retrieved based on the keywords, and a classification algorithm is developed based on machine learning techniques to build the final classifier. We apply the HITS algorithm to select the authoritative and typical documents within a category, and construct templates in the form of Bayesian networks. Data mining and information extraction techniques are then applied to extract the necessary semantic knowledge to fill in the templates to construct the final decision models. |
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