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Entity alignment method based on Chinese electronic medical record knowledge graph

A technology of knowledge graph and electronic medical records, applied in the field of natural language processing, to achieve the effect of integrating medical resources

Pending Publication Date: 2022-07-08
DALIAN UNIV OF TECH
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Problems solved by technology

[0006] The present invention proposes a dual-view graph neural network entity alignment model, which is used to solve the problem of medical knowledge graph fusion based on Chinese electronic medical records, which helps to improve the data quality of existing medical knowledge graphs and thus promotes the development of smart medical care

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  • Entity alignment method based on Chinese electronic medical record knowledge graph
  • Entity alignment method based on Chinese electronic medical record knowledge graph
  • Entity alignment method based on Chinese electronic medical record knowledge graph

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Embodiment Construction

[0058] The technical solutions of the present invention are described in detail with reference to the accompanying drawings.

[0059] like figure 1 The frame diagram is aligned for the medical knowledge graph, and Table 1 is the detailed statistics of the heterogeneous medical knowledge graph.

[0060] 1. Corpus preprocessing

[0061] The present invention manually marks the heterogeneous medical knowledge graphs MeKG_1 and MeKG_2 constructed from electronic medical records, and uses the manually marked seed pairs as a training set and a test set, and the number of seed pairs in the training set and the test set is 227 and 136 respectively.

[0062] 2. Use rules to complete the knowledge graph

[0063] First, experts derive rules from each medical knowledge graph, and then convert these rules between medical knowledge graphs according to the knowledge invariance assumption to form a unified set of rules. After that, rule matching is performed in each medical knowledge graph...

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Abstract

The invention provides an entity alignment method based on a Chinese electronic medical record knowledge graph. The method comprises the following steps: constructing a training set and a test set, and constructing the training set and the test set for entity alignment on a heterogeneous medical knowledge graph; reasoning the medical knowledge maps by using rules, complementing missing relationships, and relieving structural heterogeneity between the medical knowledge maps; according to the method, a dual-view graph neural network model based on a gating mechanism is constructed, the purpose is to align and fuse heterogeneous medical knowledge maps, the accuracy rate of Hits (at) 5 reaches up to 85.4% on the basis of considering the accuracy rate and the labor cost, existing medical resources are effectively integrated, and development of intelligent medical treatment is promoted.

Description

technical field [0001] The invention belongs to the field of natural language processing, and relates to an entity alignment method in the construction of a Chinese electronic medical record (Electronic Medical Record, EMR) knowledge graph (Knowledge Graph, KG), in particular to a medical knowledge based on a neural network and a gating mechanism Atlas Alignment Methods. Background technique [0002] Chinese electronic medical record is one of the products of information-based medical and health services. It contains a large number of medical facts. With the accumulation of domestic electronic medical records, the use of natural language processing technology to automatically obtain and integrate effective medical information from a large number of electronic medical records has important meaning. Building a knowledge graph related to electronic medical records is one of the most effective ways to display and utilize medical information in electronic medical records. A kno...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/36G16H10/60G06N5/02G06N3/04G06F40/295G06F16/28G06K9/62
CPCG06F16/367G16H10/60G06N5/022G06F40/295G06F16/288G06N3/045G06F18/214G06F18/2414
Inventor 李丽双董姜媛
Owner DALIAN UNIV OF TECH