Method for extracting causal relationship in medical field by fusing radical information

A technology of causality and radicals, applied in the field of causality extraction in the medical field, can solve problems such as insufficient semantic information acquisition, achieve the effect of enriching text semantic information and improving accuracy

Pending Publication Date: 2022-06-03
ANHUI UNIV OF SCI & TECH
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  • Description
  • Claims
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Problems solved by technology

However, the existing methods seldom take into account the radical characteristics of characters, resulting in insufficient semantic information acquisition, which brings risks to the application of causality extraction models in the medical field

Method used

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  • Method for extracting causal relationship in medical field by fusing radical information
  • Method for extracting causal relationship in medical field by fusing radical information
  • Method for extracting causal relationship in medical field by fusing radical information

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Embodiment Construction

[0028] The present invention is further described below:

[0029] The purpose of the present invention is to provide a causal relationship extraction method in the medical field that integrates radical information. This is a method based on the existing causal relationship extraction, by fusing the radical information of Chinese characters and enriching the semantic information of the text to achieve a better extraction effect.

[0030] combine figure 1 , 2 3, the medical field causal relationship extraction method of fusion radical information of the present invention is carried out according to the following steps:

[0031] Step 1: Data acquisition. Obtain the text data set D={D in the medical field 1 ,D 2 ...D n },D i Represents the i-th text, 1≤i≤n, n is the total number of texts in the set D;

[0032] Step 2: Preprocess the acquired text data. The basic steps are as follows:

[0033] Step 2.1: Remove stop words, webpage tags, etc. in the text, and perform word se...

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Abstract

The invention discloses a medical field causal relationship extraction method fusing radical information, and relates to the technical field of data mining, and the method comprises the following steps: obtaining a Chinese medical field text data set through a web crawler, preprocessing the obtained data, converting English professional nouns in a text into Chinese by adopting a Google translation technology, and extracting the Chinese medical field text data set; the method comprises the following steps: acquiring radicals of all characters by using an online Xinhua dictionary, performing incremental training on the radicals by using a Word2Vec architecture to obtain a radical feature representation, and splicing a radical feature vector and a character feature vector to obtain character features fused with radical information.

Description

technical field [0001] The invention relates to causal relationship extraction in the medical field, in particular to a causal relationship extraction method in the medical field that integrates radical information. Background technique [0002] At present, the informatization construction in the medical field is progressing steadily, and the modern medical information system has accumulated massive medical data. With the continuous accumulation of data, the use of natural language processing technology and deep learning methods to mine the rich information contained in the text data in the medical field has become a hot spot of cross-research in the field of medicine and artificial intelligence. Text data in the medical field contains a large number of records of medical activities, including diseases, drugs, examination and treatment results, etc. These information are important clinical data, and accurate and efficient analysis and mining can provide theoretical and tech...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F40/279G06F40/216G06F40/30G06F40/58G06F40/242G06F16/31G06N3/04G16H50/70
CPCG06F40/279G06F40/216G06F40/30G06F40/58G06F40/242G06F16/313G16H50/70G06N3/047G06N3/048G06N3/044
Inventor 李晓庆朱广丽张顺香吴厚月许鑫苏明星李健黄菊魏苏波孙争艳张镇江赵彤
Owner ANHUI UNIV OF SCI & TECH
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