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In recent years extracting relevant information from biomedical and clinicaltexts such as research articles, discharge summaries, or electronic healthrecords have been a subject of many research efforts and shared challenges.Relation extraction is the process of detecting and classifying the semanticrelation among entities in a given piece of texts. Existing models for thistask in biomedical domain use either manually engineered features or kernelmethods to create feature vector. These features are then fed to classifier forthe prediction of the correct class. It turns out that the results of thesemethods are highly dependent on quality of user designed features and alsosuffer from curse of dimensionality. In this work we focus on extractingrelations from clinical discharge summaries. Our main objective is to exploitthe power of convolution neural network (CNN) to learn features automaticallyand thus reduce the dependency on manual feature engineering. We evaluateperformance of the proposed model on i2b2-2010 clinical relation extractionchallenge dataset. Our results indicate that convolution neural network can bea good model for relation exaction in clinical text without being dependent onexpert's knowledge on defining quality features.

Relation extraction from clinical texts using domain invariant convolutional neural network