Abstract
Healthcare data mining and business intelligence are attracting huge industry interest in recent years. Engineers encounter a bottleneck when applying data mining tools to textual healthcare records. Many medical terms in the healthcare records are different from the standard form, which are referred to as informal medical terms in this work. Study indicates that in Chinese healthcare records, a majority of the informal terms are abbreviations or typos. In this work, a multi-field indexing approach is proposed, which accomplishes the term normalization task with information retrieval algorithm with four level indices: word, character, pinyin and its initial. Experimental results show that the proposed approach is advantageous over the state-of-the-art approaches.
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Xia, Y., Zhao, H., Liu, K., Zhu, H. (2014). Normalization of Chinese Informal Medical Terms Based on Multi-field Indexing. In: Zong, C., Nie, JY., Zhao, D., Feng, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2014. Communications in Computer and Information Science, vol 496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45924-9_28
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DOI: https://doi.org/10.1007/978-3-662-45924-9_28
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