Abstract
Clinical Concept normalization is an essential task aiming to normalize recognized clinical concepts from clinical narratives. This paper presents a hybrid model combining traditional rule-based methods and a novel neural network to realize the normalization process. The experiment is set on MCN corpus and we generate candidates by modifying phrases and setting sieves to acquire terminologies from the UMLS system. Then the mentions are normalized through a flow of processes. Firstly, if mentions of testing data once appeared in the training data, the ontologies of mentions in training data are referred to. Secondly, we exert the exact matching between mentions and candidates after modifying phrases. Thirdly, a new deep neural network with an attention mechanism is designed for normalizing. If the above three methods failed to link the mention to its ontology, the first candidate in its candidate set will be recommended. The experimental results show our hybrid model performs better than both rule-based and deep learning methods and it also outperforms the baseline model of MCN corpus.
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Acknowledgements
This research was financially supported by the Open Research Fund from Shenzhen Research Institute of Big Data, under Grant No. 2019ORF01011. Meanwhile, Xiang Wan and Tsung-Hui Chang also gave us much help.
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Tang, C. et al. (2021). A Hybrid Model for Clinical Concept Normalization. In: Satapathy, S., Zhang, YD., Bhateja, V., Majhi, R. (eds) Intelligent Data Engineering and Analytics. Advances in Intelligent Systems and Computing, vol 1177. Springer, Singapore. https://doi.org/10.1007/978-981-15-5679-1_55
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DOI: https://doi.org/10.1007/978-981-15-5679-1_55
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