Extraction method of entity relationship of electronic medical record, based on BLSTM and attention mechanism

A technology of entity relationship and electronic medical records, applied in neural learning methods, electrical digital data processing, special data processing applications, etc., can solve problems such as dependence

Inactive Publication Date: 2018-09-14
SICHUAN UNIV
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Problems solved by technology

[0006] (3) The model is overly dependent on the knowledge base and other NLP systems

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  • Extraction method of entity relationship of electronic medical record, based on BLSTM and attention mechanism
  • Extraction method of entity relationship of electronic medical record, based on BLSTM and attention mechanism
  • Extraction method of entity relationship of electronic medical record, based on BLSTM and attention mechanism

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Embodiment Construction

[0011] Below in conjunction with specific embodiment the present invention is described in further detail:

[0012] 1. Get the input basic feature vector representation

[0013] The basic feature vector is mainly composed of three parts of the input sentence itself (W), the relative distance between each word and the entity pair, and the word type.

[0014] 1) Features of the word itself (W):

[0015] For a given sentence S={x with n words 1 , x 2 ,...,x n}, we first convert each word into a low-dimensional real vector using the word2vec toolkit. The word representation is through the embedding matrix Encoded by column vectors in , where V is a fixed-size dictionary, d w is the size of the embedding matrix

[0016] 2) The relative distance feature of each word to entity pair:

[0017] we use Matrix to represent the distance of each word to entity pair, where d d is the dimension after each relative distance is mapped to a real vector, and is a hyperparameter that c...

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Abstract

The invention proposes an extraction method of an entity relationship of an electronic medical record, based on a BLSTM and an attention mechanism. The method comprises the following steps: mapping anatural sentence of the electronic medical record into basic feature vector through a word2vec toolkit, coding the basic feature vector into an upper layer feature vector through the BLSTM, then capturing an important text content representing the entity relationship through the attention mechanism based on word and sentence levels, so as to form a higher level of feature vector, finally inputtingthe obtained feature vector into a softmax classifier, and extracting the entity relationship among all entity pairs from the sentence. In addition, the method does not depend on any knowledge base and terminological dictionary to produce the basic feature, reduces the dependence on artificial feature engineering by a model, and provides a technological approach for automatically learning electronic medical record information.

Description

technical field [0001] The invention belongs to the field of natural language processing and is used for automatically extracting entity relationships between entity pairs in electronic medical records. Background technique [0002] With the advent of the information age, data in various fields has exploded. Specifically in the medical field, a large number of electronic medical record texts containing knowledge in the medical and health field have been accumulated. In this context, extracting relevant information from unstructured electronic medical records has become the key to obtaining medical knowledge and has important application value. The relationship extraction between electronic medical record entity pairs is one of its core tasks. [0003] At present, the entity relationship extraction of electronic medical records mainly adopts supervised machine learning. This method first selects the features of candidate entities, adds medical knowledge as auxiliary analysi...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30G06F17/27G06N3/04G06N3/08
CPCG06N3/08G06F40/30G06N3/048
Inventor 李智杨金山李健
Owner SICHUAN UNIV
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