Deep Convolutional Neural Network Based Medical Concept Normalization

Medical concept normalization is a critical problem in biomedical research and clinical applications. In this article, we focus on normalizing diagnostic and operation names in Chinese discharge summaries to standard concepts, which is formulated as a semantic matching problem. However, non-standard Chinese expressions, short-text normalization, heterogeneity of tasks and flexible input of disambiguation mentions pose critical challenges in our problem. We propose two models, the basic model and flexible model, to tackle these problems. The basic model solves the core problem (the first three challenges) in ambiguous mentions normalization, while the flexible model deals with flexible input of ambiguous mentions and further explores the correlation among them. Specifically, in the basic model, we present a general framework to disambiguate a diagnosis and its corresponding operation simultaneously, which introduces a tensor generator and a novel multi-view convolutional neural network (CNN) with a multi-task shared structure. We propose that the key to address non-standard expressions and the short-text problem is to incorporate a matching tensor with multiple granularities. Then a multi-view CNN is adopted to extract semantic matching patterns. Finally, the multi-task shared structure allows the model to exploit medical correlations between diagnosis and operation mentions to better perform disambiguation tasks. Subsequently, we design a flexible model based on the basic model. Specifically, we add a flexible attention layer to all procedure representation vectors, and then apply a flexible multi-task scheme to share the correlated information. Comprehensive experimental analysis indicates that our model outperforms existing baselines, demonstrating the effectiveness and robustness of our model.