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We propose a deep neural network based natural language processing system for clinical information (such as time information, event spans, and their attributes) extraction from raw clinical notes and pathology reports. Our approach uses the context words and their partof-speech tags and shape information as features. We utilize the temporal (1D) convolution neural network to learn the hidden feature representations. In prediction step, we use the Multilayer Perceptron (MLP) to predict event spans. The empirical evaluation demonstrates that our approach significantly outperforms baseline methods.

UTA DLNLP at SemEval-2016 Task 12: Deep Learning Based Natural Language Processing System for Clinical Information Identification from Clinical Notes and Pathology Reports