Electronic medical record entity relation extraction method and apparatus

A technology of electronic medical records and entity relations, which is applied in the fields of electronic digital data processing, special data processing applications, instruments, etc., and can solve problems such as the lack of perfect basic processing tools in language

Active Publication Date: 2017-02-22
BEIJING QUALITY & ZEAL INFORMATION TECH CO LTD
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[0006] (3) Some languages ​​do not

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  • Electronic medical record entity relation extraction method and apparatus
  • Electronic medical record entity relation extraction method and apparatus
  • Electronic medical record entity relation extraction method and apparatus

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[0029] Specific embodiments of the present disclosure will be described in detail below in conjunction with the accompanying drawings. It should be understood that the specific embodiments described here are only used to illustrate and explain the present disclosure, and are not intended to limit the present disclosure.

[0030] The electronic medical record entity relationship extraction method and device proposed in the present disclosure are based on a convolutional neural network. The so-called convolutional neural network is a special deep neural network, and it is also the first deep network model successfully applied. Convolutional neural networks use spatial relative relationships to reduce the number of parameters and become the core part of many computer vision systems today, such as image recognition and automatic driving.

[0031] The concept of convolution comes from digital signal processing. The one-dimensional convolution is defined as follows:

[0032]

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Abstract

The invention discloses an electronic medical record entity relation extraction method and apparatus, and belongs to the field of medical data mining. The method comprises the steps of obtaining a matrix after electronic medical record natural statement mapping through a convolutional neural network model and word vectorization representation; inputting tested electronic medical record natural statements to the trained convolutional neural network model to obtain eigenvectors; and inputting the eigenvectors to a trained classifier, and extracting an entity relation of the tested electronic medical record natural statements. Therefore, the advantages of the convolutional neural network model are utilized, the relation among entities in the electronic medical record natural statements is mined, and a technical way is provided for automatically learning electronic medical record information.

Description

technical field [0001] The present disclosure relates to the field of medical data mining, in particular to a method and device for extracting entity relationships from electronic medical records. Background technique [0002] With the explosive growth of data volume in the information age, clinical medical data also shows the characteristics of large capacity, fast growth rate, various forms and high potential value. In the field of clinical medicine, electronic medical record data in the form of natural language text plays an important role. In this context, the use of computers to automatically extract structured information from unstructured electronic medical record text data, that is, information extraction technology, has received extensive attention and has important application value. Entity relationship extraction from electronic medical records is the core task of information extraction. [0003] At present, supervised methods are mainly used for text entity rel...

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Application Information

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IPC IPC(8): G06F19/00
Inventor 黄亦谦
Owner BEIJING QUALITY & ZEAL INFORMATION TECH CO LTD
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