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2020 | OriginalPaper | Buchkapitel

Knowledge-Enabled Diagnosis Assistant Based on Obstetric EMRs and Knowledge Graph

verfasst von : Kunli Zhang, Xu Zhao, Lei Zhuang, Qi Xie, Hongying Zan

Erschienen in: Chinese Computational Linguistics

Verlag: Springer International Publishing

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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.

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Titel: Knowledge-Enabled Diagnosis Assistant Based on Obstetric EMRs and Knowledge Graph
verfasst von: Kunli Zhang
Xu Zhao
Lei Zhuang
Qi Xie
Hongying Zan
Verlag: Springer International Publishing
Buch: Chinese Computational Linguistics
Print ISBN: 978-3-030-63030-0

Electronic ISBN: 978-3-030-63031-7

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-030-63031-7_32

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