BMC Medical Informatics and Decision Making (Apr 2019)

Clinical text classification with rule-based features and knowledge-guided convolutional neural networks

  • Liang Yao,
  • Chengsheng Mao,
  • Yuan Luo

DOI
https://doi.org/10.1186/s12911-019-0781-4
Journal volume & issue
Vol. 19, no. S3
pp. 31 – 39

Abstract

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Abstract 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. Conclusion 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.

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