Background: Clinical text classification is an fundamental problem in medical natural language processing. Existing studies have cocnventionally focused on rules or knowledge sources-based feature engineering, but only a limited number of studies have exploited effective representation learning capability of deep learning methods. Methods: In this study, we propose a new approach which combines rule-based features and knowledge-guided deep learning models for effective disease classification. Critical Steps of our method include recognizing trigger phrases, predicting classes with very few examples using trigger phrases and training a convolutional neural network (CNN) with word embeddings and Unified Medical Language System (UMLS) entity embeddings. Results: We evaluated our method on the 2008 Integrating Informatics with Biology and the Bedside (i2b2) obesity challenge. The results demonstrate that our method outperforms the state-of-the-art methods. We showed that CNN model is powerful for learning effective hidden features, and CUIs embeddings are helpful for building clinical text representations. This shows integrating domain knowledge into CNN models is promising.
Similarly, Yao et al. REF proposed a clinical text classification method that combines rule-based features to identify trigger phrases and knowledge-guided CNN for disease classification.
Clinical text classification with rule-based features and knowledge-guided convolutional neural networks
BMC Medical Informatics and Decision Making
BMC Medical Informatics and Decision Making
Biology Medicine Computer Science