  Clinical text classification is an important problem in medical natural
language processing. Existing studies have conventionally focused on rules or
knowledge sources-based feature engineering, but only a few have exploited
effective feature learning capability of deep learning methods. In this study,
we propose a novel approach which combines rule-based features and
knowledge-guided deep learning techniques for effective disease classification.
Critical Steps of our method include identifying trigger phrases, predicting
classes with very few examples using trigger phrases and training a
convolutional neural network with word embeddings and Unified Medical Language
System (UMLS) entity embeddings. We evaluated our method on the 2008
Integrating Informatics with Biology and the Bedside (i2b2) obesity challenge.
The results show that our method outperforms the state of the art methods.
