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For this problem, we proposed the use of bidirectional-LSTM’s(Long Short Term Memory) with 1-D CNN layer to classify patient notes at character level and at word level. The 1-D CNN is employed to scale back the training time. In order to improve the performance, we will also feed the network combined word embedding consisting of Pre-trained word2vec 100 dimension word embedding trained on the Twitter ADR Dataset database and character embedding generated by a Char-CNN for Named Entity Recognition

Jupyter Notebook 93.89% Python 6.11%
adverse-drug-reaction unstructured-data extraction lstm lstm-neural-networks lstm-model bidirectional-lstm cnn-text-classification word-embeddings word2vec

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