Abstract
With the increasing adoption of Electronic Medical Records (EMRs) system, how to extract and use the medical data resources stored in the EMR is starting to attract the attention of researchers. Recently, Natural language processing (NLP) has been used as a common method to process text information in the EMR, which can also be used for Text categorization, Sentiment analysis, Word segmentation, Part-of-speech tagging, etc. Named Entity Recognition (NER) is the primary task of NLP, which can effectively identify valuable information in the text. For these reasons, this work aims to explore the NER model in the field of Rheumatism and the influence of text annotation methods on the performance of NER model. The results show that having compared with four different deep learning models, our proposed approach has achieved the higher level of accuracy. In addition, we found that the reduction of annotation types can improve the performance of the NER model applied to Rheumatism.
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This work was supported by the National Natural Science Foundation of China (Grant No. 61771297).
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Liu, N., Gai, N., Huang, Z. (2021). Rheumatism Information Extraction from Electronic Medical Records Using Deep Learning Approach. In: Stephanidis, C., Antona, M., Ntoa, S. (eds) HCI International 2021 - Posters. HCII 2021. Communications in Computer and Information Science, vol 1420. Springer, Cham. https://doi.org/10.1007/978-3-030-78642-7_69
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DOI: https://doi.org/10.1007/978-3-030-78642-7_69
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