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Electronic medical record entity relationship extraction method based on a convolutional recurrent neural network

A recurrent neural network, electronic medical record technology, applied in biological neural network model, neural architecture, electrical digital data processing, etc.

Active Publication Date: 2019-06-21
SOUTHWEST JIAOTONG UNIV
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Problems solved by technology

[0010] The purpose of the present invention is to solve the uneven extraction effect caused by the uneven distribution of the sentence length of the electronic medical record, the unbalanced relationship sample class, and the fact that the sentence contains multiple entities. optimal problem while avoiding any dependence on external resource features

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  • Electronic medical record entity relationship extraction method based on a convolutional recurrent neural network
  • Electronic medical record entity relationship extraction method based on a convolutional recurrent neural network
  • Electronic medical record entity relationship extraction method based on a convolutional recurrent neural network

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Embodiment Construction

[0063] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments of the drawings, so that those skilled in the art can better understand the present invention.

[0064] The method for extracting entity relations from electronic medical records based on the convolutional cyclic neural network proposed by the present invention comprises the following steps:

[0065] S1. Use the data constructor to reconstruct the statement to obtain a multi-dimensional hierarchical sequence:

[0066] First, extract sentences containing two or more entities from electronic medical records, and construct a relationship prediction instance for every two entities;

[0067] In the field of images, there are many excellent neural network models, but due to the limitations of different input data forms, these models are difficult to be used in the field of natural language processing to exert their unique advantages; this inventi...

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Abstract

The invention discloses an electronic medical record entity relationship extraction method based on a convolutional recurrent neural network. The method comprises the steps of using a data constructorto reconstruct natural statements to obtain a multi-dimensional hierarchical sequence; Mapping the multi-dimensional hierarchical sequence into an input feature vector by adopting a vector representation technology; capturing local and global semantic information of the statement simultaneously by adopting a convolutional recurrent neural network ConvLSTM to obtain an upper sentence vector; adopting a two-stage attention mechanism to capture text content closely associated with the semantic relation, and obtaining a high-stage sentence vector, so as to solve the problem that multiple instances are mistakenly labeled; and performing relation judgment according to the obtained high-level sentence vector to obtain a prediction label. The method does not depend on any external resource characteristics, and the entity relationship extraction performance is improved only by means of data reconstruction and improvement of a network model framework. Meanwhile, the method can be extended to other tasks with the problems of insufficient feature extraction, unbalanced samples and the like.

Description

technical field [0001] The invention belongs to the technical field of natural language processing, in particular to a method for extracting entity relations from electronic medical records based on a convolutional cyclic neural network. Background technique [0002] With the vigorous advancement of medical informatization, the extraction of structured information from medical data has become particularly important. Electronic medical records are a major data source in the medical field, and extracting structured information from them is an important way to realize medical informatization, and it is helpful for the construction of medical knowledge maps and the secondary research and use of electronic medical records. Entity relationship extraction is one of the core tasks of structured information extraction in electronic medical records. Its task is to automatically identify the semantic relationship between a given entity pair from a given electronic medical record text. ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/27G06N3/04G16H10/60
Inventor 滕飞唐莉马征黄路非李暄
Owner SOUTHWEST JIAOTONG UNIV
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