ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2019, Vol. 56 ›› Issue (9): 1897-1906.doi: 10.7544/issn1000-1239.2019.20180729

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Lab Indicator Standardization in a Regional Medical Health Platform

Zhang Jiaying1, Wang Qi1, Zhang Zhixing1, Ruan Tong1, Zhang Huanhuan1, He Ping2   

  1. 1(East China University of Science and Technology, Shanghai 200237); 2(Shanghai Hospital Development Center, Shanghai 200041)
  • Online:2019-09-10
  • Supported by: 
    This work was supported by the National Natural Science Foundation of China (61772201), the Key Special Program of National Key Research and Development Plan of China (2018YFC0910500), and the National Major Scientific and Technological Special Project for “Significant New Drugs Development” (2018ZX09201008).

Abstract: Due to the lack of a complete synonym list for indicator mapping, different hospitals may use different names for the same lab indicator. Lab indicator name discrepancy has greatly affected the medical information sharing and exchange among hospitals. It is becoming increasingly important to standardize the lab indicators. Such a problem can be seen as an entity alignment task to map different indicators into standard ones. However, a lab indicator only involves its name and value, not including any extra properties or contexts which is needed by existing knowledge base (KB) alignment or entity linking methods. More importantly, there exist no available standard KBs to provide standard indicator terms. Therefore, we cannot implement these existing methods directly. To solve the problem, in this paper, we present the first effort to work on lab indicator standardization. We propose a novel standardization method, which firstly clusters the indicators based on their names and abbreviations, and then iteratively employs a binary classification algorithm based on similarity features and partition score features for indicator mapping. Experimental results on the real-world medical data show that the final classification achieves a F1-score of 85.27%, which indicates that our method improves the quality and outperforms state-of-the-art approaches.

Key words: regional medical health platform, lab indicator, standardization, clustering, classification

CLC Number: