Multi-Task Medical Concept Normalization Using Multi-View Convolutional Neural Network


  • Yi Luo University of California, San Diego
  • Guojie Song Peking University
  • Pengyu Li Peking University
  • Zhongang Qi Oregon State University


Medical concept normalization is a critical problem in biomedical research and clinical applications. In this paper, we focus on normalizing diagnostic and procedure names in Chinese discharge summaries to standard entities, which is formulated as a semantic matching problem. However, non-standard Chinese expressions, short-text normalization and heterogeneity of tasks pose critical challenges in our problem. This paper presents a general framework which introduces a tensor generator and a novel multi-view convolutional neural network (CNN) with multi-task shared structure to tackle the two tasks simultaneously. We propose that the key to address non-standard expressions and short-text problem is to incorporate a matching tensor with multiple granularities. Then multi-view CNN is adopted to extract semantic matching patterns and learn to synthesize them from different views. Finally, multi-task shared structure allows the model to exploit medical correlations between disease and procedure names to better perform disambiguation tasks. Comprehensive experimental analysis indicates our model outperforms existing baselines which demonstrates the effectiveness of our model.




How to Cite

Luo, Y., Song, G., Li, P., & Qi, Z. (2018). Multi-Task Medical Concept Normalization Using Multi-View Convolutional Neural Network. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from