Abstract
The obstetric Electronic Medical Record (EMR) contains a large amount of medical data and health information. It plays a vital role in improving the quality of the diagnosis assistant service. In this paper, we treat the diagnosis assistant as a multi-label classification task and propose a Knowledge-Enabled Diagnosis Assistant (KEDA) model for the obstetric diagnosis assistant. We utilize the numerical information in EMRs and the external knowledge from Chinese Obstetric Knowledge Graph (COKG) to enhance the text representation of EMRs. Specifically, the bidirectional maximum matching method and similarity-based approach are used to obtain the entities set contained in EMRs and linked to the COKG. The final knowledge representation is obtained by a weight-based disease prediction algorithm, and it is fused with the text representation through a linear weighting method. Experiment results show that our approach can bring about +3.53 F1 score improvements upon the strong BERT baseline in the diagnosis assistant task.
This work has been supported by the National Key Research and Development Project (Grant No. 2017YFB1002101), Major Program of National Social Science Foundation of China (Grant No. 17ZDA138), China Postdoctoral Science Foundation (Grant No. 2019TQ0286), Science and Technique Program of Henan Province (Grant No. 192102210260), Medical Science and Technique Program Co-sponsored by Henan Province and Ministry (Grant No. SB201901021), Key Scientific Research Program of Higher Education of Henan Province (Grant No. 19A520003, 20A520038), the MOE Layout Foundation of Humanities and Social Sciences (Grant No. 20YJA740033), and the Henan Social Science Planning Project (Grant No. 2019BYY016).
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Zhang, K., Zhao, X., Zhuang, L., Xie, Q., Zan, H. (2020). Knowledge-Enabled Diagnosis Assistant Based on Obstetric EMRs and Knowledge Graph. In: Sun, M., Li, S., Zhang, Y., Liu, Y., He, S., Rao, G. (eds) Chinese Computational Linguistics. CCL 2020. Lecture Notes in Computer Science(), vol 12522. Springer, Cham. https://doi.org/10.1007/978-3-030-63031-7_32
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