Multi-label stomach disease classification method and device based on medical record text

A disease classification and multi-label technology, applied in neural learning methods, biological neural network models, patient-specific data, etc., can solve problems such as insufficient data sets, and alleviate the problem of insufficient data sets, reduce human factors, and shorten computing. effect of time

Pending Publication Date: 2021-05-14
紫东信息科技(苏州)有限公司
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Problems solved by technology

[0004] This application provides a multi-label gastric disease classification method and device based on medical record texts, which can alleviate the proble

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  • Multi-label stomach disease classification method and device based on medical record text
  • Multi-label stomach disease classification method and device based on medical record text
  • Multi-label stomach disease classification method and device based on medical record text

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Embodiment Construction

[0040] The specific implementation manners of the present application will be further described in detail below in conjunction with the drawings and embodiments. The following examples are used to illustrate the present application, but not to limit the scope of the present application.

[0041] First, some terms involved in this application are introduced.

[0042] Bidirectional Encoder Representations from Transformers (Bidirectional Encoder Representations from Transformers, BERT): It is a large-scale unsupervised pre-training language model. As a substitute for Word2vec, it refreshes the accuracy in the field of Natural Language Processing (NLP). One of the most groundbreaking techniques from residual networks in recent years. The essence of BERT is to learn a good feature representation for words by running a self-supervised learning method on the basis of massive corpus, and it provides a transferable model for other tasks. Its advantage is that it integrates the Trans...

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Abstract

The invention relates to a multi-label stomach disease classification method and device based on a medical record text, and belongs to the technical field of medical text intelligent processing. The method comprises the steps that multiple sets of training data are acquired, and each set of training data comprises the medical record text and a disease label corresponding to the medical record text; training a preset network structure based on the multiple groups of training data to obtain a disease classification model; using the disease classification model for identifying disease classification in the input medical record text, wherein the network structure is a combination of a pre-training model and a seq2seq model; and converting a multi-label classification problem into a sequence generation problem by utilizing a network of a pre-training model and a self-attention mechanism, so that very good multi-label classification performance is obtained on limited training samples. Besides, manual participation is not needed in the classification process, human factors are reduced, meanwhile, accurate diagnosis reference can be provided for doctors, and the working pressure of medical staff is relieved.

Description

【Technical field】 [0001] The application relates to a multi-label gastric disease classification method and device based on medical record texts, belonging to the technical field of medical text intelligent processing. 【Background technique】 [0002] Stomach disease is an organic or functional disease that occurs in the stomach. The etiology is very complex, including physical and chemical stimulation, infection, toxin, genetics, mental factors, developmental disorders, and surgical effects. Symptoms related to gastric diseases will be recorded in the medical record text, which will be used by medical staff to determine the classification of diseases. [0003] However, manual extraction of diseases in medical record texts will consume the time of medical staff, and the efficiency of gastric disease classification is low. 【Content of invention】 [0004] This application provides a multi-label gastric disease classification method and device based on medical record texts, w...

Claims

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Application Information

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IPC IPC(8): G16H10/60G16H50/20G06N3/08G06N3/04
CPCG16H10/60G16H50/20G06N3/08G06N3/044G06N3/045
Inventor 李寿山陆文捷谭惜姿朱苏阳周国栋
Owner 紫东信息科技(苏州)有限公司
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