Medical question-answer semantic clustering method based on integrated convolutional encoding

A clustering method and integrated volume technology, which is applied in neural learning methods, instruments, biological neural network models, etc., can solve problems such as not very widely used and no reliable application of medical intelligent question answering workflow.

Active Publication Date: 2017-12-26
SOUTH CHINA UNIV OF TECH
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AI Technical Summary

Problems solved by technology

However, in the medical field, the related technologies of deep learning are not widely used, nor are they reliably applied to the workflow of medical intelligent question answering.

Method used

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  • Medical question-answer semantic clustering method based on integrated convolutional encoding
  • Medical question-answer semantic clustering method based on integrated convolutional encoding
  • Medical question-answer semantic clustering method based on integrated convolutional encoding

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Embodiment

[0097] This embodiment provides a medical question and answer semantic clustering method based on integrated convolutional coding. The method is based on the integrated convolutional coding model to implement semantic clustering of medical text data. The flow chart of the method is as follows figure 1 As shown, the architecture diagram is as follows figure 2 shown, including the following steps:

[0098] Step 1: Obtain the medical question answering data set from the medical platform, preprocess the medical question answering data set, and obtain the input matrix;

[0099] Specifically, preprocess the medical question-answer dataset, that is, perform word segmentation, remove stop words, and part-of-speech tagging on the medical question-answer dataset, and then form a matrix representation of the input medical question-answer dataset according to the representation of word vectors to obtain an input matrix .

[0100] Step 2: Use the convolutional encoding network to select...

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Abstract

The invention discloses a medical question-answer semantic clustering method based on integrated convolutional encoding and relates to the field of machine learning. The method comprises the following steps of collecting question-answer corpuses of medical consultation platform users; selecting convolution kernels; fusing characteristic representations of different convolution kernels; acquiring a final data representation by use of an own encoder; and carrying out medical consultation question-answer semantic clustering. Compared with the conventional deep learning method, different characteristics are extracted with different convolution kernels in the invention, the extracted characteristics are sufficient and diversified, different characteristic merging methods are adopted, the extracted characteristics are subject to fusion representation, thus the method is strong in generalization ability and high in semantic clustering accuracy rate; and based on the method, the own situation of the user can be better understood, the method can assist a doctor in detecting diseases, and great application values are provided for establishment of the automatic medical question-answer system.

Description

technical field [0001] The invention relates to the field of computer artificial intelligence, in particular to the field of machine learning, and in particular to a medical question-and-answer semantic clustering method based on integrated convolution coding. Background technique [0002] With the rapid development of the Internet, people's lifestyles have gradually changed. According to statistical surveys, when ordinary users feel unwell, 90% of them will search for relevant information on the Internet. The Internet is thus changing the medical ecology. In Internet medicine, online disease guidance is a very important and crucial step. As a result, many online disease question-and-answer websites have emerged in the health-related medical field. Patients communicate with doctors and obtain disease-related nursing knowledge by describing their own encounters, detailed conditions, medications, and treatment conditions. In these related disease questions and answers, the...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/045G06F18/23211G06F18/217
Inventor 余志文戴丹
Owner SOUTH CHINA UNIV OF TECH
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