LAUSR

Extracting clinical event expressions and their types from clinical text is a fundamental task for many applications in clinical NLP. State-of-the-art systems need handcraft features and do not take into account the representation of the low-frequency words. To address these issues, a Bi-LSTM-CRF neural network architecture based on medical knowledge features is proposed. First, we employ convolutional neural networks (CNNs) to encode character-level information of a word and extract medical knowledge features from an open-source clinical knowledge system. Then, we concatenate character-level and word-level embedding and the medical knowledge features of words together, and feed them into bi-directional long short-term memory (Bi-LSTM) to build context information of each word. Finally, we jointly use a conditional random field (CRF) to decode labels for the whole sentence. We evaluate our model on two publicly available clinical datasets, namely THYME corpus and 2012 i2b2 dataset. Experimental results show that our model outperforms previous state-of-the-art systems with different methodologies, including machine learning-based methods, deep learning-based methods, and Bert-based methods.