REMed: automatic relation extraction from medical documents

The large amount of unstructured medical documents written in natural language bears a massive quantity of knowledge, whose extraction becomes useful. An automatic relation identification strategy leads to the discovery of relations, (possible unknown) interactions, and associations between medical conditions, investigations and treatments. The current paper introduces a learning based approach for the automatic discovery of relations between medical concepts, entitled REMed. We propose an original list of features, grouped into four categories with the following distribution: lexical - 3, context - 6, grammatical -- 4 and syntactic - 4. We analyzed the influence of each category on the classification performance and determined that the performance of the REMed solution is comparable with similar solutions. We report the overall F-measure as 74.9% that outperforms the best solution reported in the similar systems with 1.2%. This performance was achieved mostly by the features from the lexical and context categories.