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Chinese medical named entity identification method and system, storage medium and equipment

A named entity recognition, Chinese technology, applied in the direction of instruments, biological neural network models, electrical digital data processing, etc., can solve problems such as word segmentation difficulties, achieve the effect of enriching semantics, improving accuracy, and reducing ambiguity

Active Publication Date: 2021-04-02
SHANDONG NORMAL UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

Segmentation of these technical terms becomes very difficult without the guidance of medical domain knowledge

Method used

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  • Chinese medical named entity identification method and system, storage medium and equipment
  • Chinese medical named entity identification method and system, storage medium and equipment
  • Chinese medical named entity identification method and system, storage medium and equipment

Examples

Experimental program
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Embodiment 1

[0043] refer to figure 1 , a kind of Chinese medical named entity recognition method of the present embodiment, it comprises:

[0044] S101: Obtain clinical text data.

[0045] Specifically, clinical text data is obtained from electronic medical records. Electronic medical records are a collection of patient health information, including clinical texts, drug records, disease diagnosis records, physiological indicators, laboratory results, non-written records (medical images, electrocardiograms and audio recordings, etc.), surgical history, genetic medical history and medical expenses, etc.; According to the content in the plain text document of the electronic medical record, its entity content and corresponding location are extracted. The entity categories include: disease and diagnosis, examination, inspection, operation, drug, and anatomical part.

[0046] S102: Converting the clinical text data into character embedding representations of medical texts, medical concept emb...

Embodiment 2

[0105] refer to Figure 7 , the present embodiment provides a Chinese medical named entity recognition system, which includes:

[0106] A data acquisition module, which is used to acquire clinical text data;

[0107] The fusion feature module is used to convert clinical text data into medical text character embedding representation, medical concept embedding feature vector and cross-language Chinese embedding representation and splicing to obtain multivariate data fusion feature vector;

[0108] An entity recognition module, which is used to input multivariate data fusion feature vectors into a multi-graph-based named entity recognition model to identify Chinese medical named entity types;

[0109] Among them, the named entity recognition model based on multi-graph includes multi-graph network and LSTM-CRF model. The multi-graph network is used to receive the text graph composed of multivariate data fusion feature vectors as nodes, output the final state of the node and send ...

Embodiment 3

[0113] This embodiment provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps in the method for recognizing Chinese medical named entities as described in the first embodiment above are realized.

[0114] This embodiment introduces an entity dictionary, embeds semantics into entity representation, and can better understand the rich grammatical and semantic information in sentences; a set of compact medical concepts is learned as a link between hidden semantics and observed medical evidence Bridge, extracting fine-grained semantic information, reducing the ambiguity of polysemous words; adopting cross-lingual knowledge transfer method, transferring high-resource language knowledge to Chinese medical texts, supplementing knowledge, supervising Chinese named entity recognition tasks with the help of external language knowledge; extracting medical The multi-granularity features of the text fuse word ...

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Abstract

The invention belongs to the technical field of Chinese medical named entity identification, and provides a Chinese medical named entity identification method and system, a storage medium and equipment. The Chinese medical named entity recognition method comprises the steps of obtaining clinical text data; converting the clinical text data into a character embedding representation, a medical concept embedding feature vector and a cross-language Chinese embedding representation of a medical text respectively, and splicing the character embedding representation, the medical concept embedding feature vector and the cross-language Chinese embedding representation to obtain a multivariate data fusion feature vector; inputting the multivariate data fusion feature vector into a named entity recognition model based on multiple graphs, and recognizing a Chinese medical named entity type, wherein the named entity recognition model based on multiple graphs comprises a multi-graph network and an LSTM-CRF model, the multi-graph network is used for receiving a text graph formed by taking a multivariate data fusion feature vector as a node, outputting a final state of the node and transmitting the final state to the LSTM-CRF model, and the LSTM-CRF model outputs a recognition result. The Chinese medical named entity identification accuracy is improved.

Description

technical field [0001] The invention belongs to the technical field of Chinese medical named entity recognition, and in particular relates to a Chinese medical named entity recognition method, system, storage medium and equipment. Background technique [0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art. [0003] Named entity recognition refers to the recognition of entities with specific meanings in free text, such as names of people, places, proper nouns, etc. Medical named entity recognition identifies entities such as diseases, symptoms, treatments, etc. from physicians' treatment records. Medical named entity recognition is the basis and key to the semantic structuring of electronic medical records. Its task is to identify different entities from the medical text of electronic medical records, such as diseases, symptoms, treatments, drugs, operations, and analytical si...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F40/295G06F40/242G06F40/30G06N3/04
CPCG06F40/295G06F40/242G06F40/30G06N3/044G06N3/045Y02A90/10
Inventor 王红王正军杨杰王彩雨杨雪李刚滑美芳胡斌王吉华贾伟宽闫伟
Owner SHANDONG NORMAL UNIV
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