Auxiliary registration method based on Bayes text classification model

A text classification and model technology, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve problems such as unsatisfactory requirements and unsatisfactory results, and achieve improved efficiency, optimized accuracy and performance, and good classification effect of effect

Inactive Publication Date: 2014-09-03
BEIJING UNIV OF POSTS & TELECOMM
View PDF4 Cites 23 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0015] The Naive Bayesian classification model has the advantages of simplicity, high efficiency, and strong stability, but it requires conditional attributes to meet the conditional independence assumption. Many scholars study the improvement method of the Naive Bayesian classification model, mostly by relaxing the conditional independence assumption. In order to improve the...

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Auxiliary registration method based on Bayes text classification model
  • Auxiliary registration method based on Bayes text classification model
  • Auxiliary registration method based on Bayes text classification model

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0025] The specific implementation manners of the present invention will be further described below.

[0026] The text classification model based on the Bayesian algorithm mainly includes the following processes: text data preprocessing, text representation, feature extraction, data training, model evaluation, and model application.

[0027] First of all, data preprocessing is performed on the acquired text case data. Each disease requires data for training. A piece of text is split into a word or word through word segmentation, and some words that are not representative of the category are removed, such as "of, In, has, is" and so on;

[0028] Then use the vector space model to represent the preprocessed data as a vector consisting of word and weight pairs, and then calculate the weight of each feature word separately;

[0029] The calculation method of the characteristic weight that the present invention adopts is the TFIDF algorithm proposed by Salton and 1973, and has car...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention provides an auxiliary registration method based on a Bayes text classification model. The method comprises the step of obtaining symptom chief complaint text data and preprocessing the data, the step of training the preprocessed data based on the Bayes text classifying algorithm, and the step of assessing auxiliary registration classification models on which training is carried out and applying the model with the performance meeting the requirement to the auxiliary network appointment registration. In the process of Bayes theory training, optimization is carried out from two aspects according to the characteristics of symptom main complaint data, and in one aspect, absolute weight is given based on a core symptom word list in the vector weight calculating process; in the other aspect, the layering Bayes model is used for carrying out training according to the illness large classes and characteristics. According to the auxiliary registration method, the auxiliary classification model is obtained by training the symptom chief complaint text data, and the processing capacity of patient input is improved under the disease consulting, registration consulting and other typical scenes.

Description

technical field [0001] The invention relates to an auxiliary registration method based on a Bayesian text classification model Background technique [0002] With the continuous improvement of the performance of computer software and hardware, people expect computers to provide more intelligent human-computer interaction methods, especially in the aspect of online reservation and user self-service registration. By mining text data in the medical field, it can assist network users to self-register. , improve outpatient efficiency. The realization of this machine-assisted registration function requires the support of text mining technology at the bottom layer. The quality of text mining technology directly determines the performance of the system and also determines the quality of human-computer interaction. [0003] In order to realize the auxiliary registration function, the core is a classification model based on the text data of the patient's symptom chief complaint. Feat...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06F19/00
Inventor 崔晓艳王枞徐冉韩旭古恒
Owner BEIJING UNIV OF POSTS & TELECOMM
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap