CN114628012B - Emergency department's preliminary examination sorting system - Google Patents
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Abstract
The invention provides an emergency department pre-examination and sorting system, which comprises: the front subsystem, the data preprocessing subsystem, the intelligent analysis subsystem and the data storage subsystem can analyze the description of the patient on the illness state through a machine learning algorithm, give an analysis result for a doctor to refer, greatly accelerate the speed of inquiry of emergency department and save medical resources.
Description
Technical Field
The invention relates to the technical field of medical treatment, in particular to a pre-examination and sorting system for emergency department.
Background
The inquiry refers to the course of questions and answers by a physician in order to solve the cause, the course of the illness, the related symptoms and other conditions related to the illness of the user. The main purpose of inquiry is that doctors acquire useful patient disease information to determine the treatment direction and strategy, which is the basis of follow-up work and has important significance in disease diagnosis. In the medical diagnosis and treatment process, it is desirable for a doctor to comprehensively understand the condition of a user to formulate an accurate treatment method, and for the user to obtain treatment as quickly and effectively as possible. However, since medical resources of the emergency department are more tense than those of common other departments, in order to alleviate the contradiction between shortage of medical resources of the emergency department and numerous doctors and patients, many medical institutions adopt a pre-examination mode, so that personal basic information and illness states of users are obtained in advance, and doctors can obtain information of patients before inquiry, so that the speed of seeing a doctor can be increased.
However, the current pre-examination mode only can obtain basic information of the patient, but the information is not analyzed and the obtained result is directly provided for doctors to reference, and the doctor still needs to make preliminary judgment on the condition of the patient according to the basic condition of the patient and then adjust the condition in the follow-up inquiry and treatment processes.
The invention aims to provide a pre-examination and sorting system for emergency department, which can analyze the description of the patient on the illness state through a machine learning algorithm, give an analysis result for a doctor to refer to, greatly accelerate the inquiry speed of the emergency department and save medical resources.
Disclosure of Invention
In order to solve the problem, the invention provides an emergency department pre-examination and sorting system, which comprises the following components:
the system comprises a front subsystem, a data preprocessing subsystem, an intelligent analysis subsystem and a data storage subsystem;
the front terminal system comprises a user health data module and a question-answering module, and comprises a special terminal, a handheld device terminal App and a Web APP, wherein the frame of the Web APP is Angular;
the user health data module comprises a health information recording sub-module and a statistics sub-module, wherein the health information recording sub-module is used for recording the treatment information of a user, and the statistics sub-module displays a statistical chart of the past inquiry conditions of the user through a front-end data visualization component library;
the question-answering module collects first illness state voice description information through voice acquisition equipment;
the first illness state voice description information is transmitted to a data preprocessing subsystem to perform data preprocessing to obtain second illness state description information;
the data preprocessing comprises the following steps: converting the first illness state voice description information into first illness state description information through voice recognition; and performing word segmentation processing and stop word removal processing on the first illness state description information to obtain second illness state description information.
The second condition description information is transmitted to the intelligent analysis subsystem for processing, and the processing comprises the following steps:
extracting a disease keyword from the second disease description information, and determining a first disease related information keyword in the second disease description information from the disease keyword; encoding each disease keyword according to the first disease related information keywords, and obtaining vector characteristics corresponding to the second disease description information according to the characteristics of the disease keywords corresponding to the extracted disease keywords; performing cyclic decoding according to the vector characteristics to determine the diseases, and acquiring the possibility of the second disease related information keyword; decoding according to the vector characteristics and the current decoding condition when each cycle is decoded, wherein the current decoding condition adopts the possibility condition of taking out the previous cycle decoding; after the cycle decoding termination condition is reached, determining a second disease-related information keyword which has disease relevance with the first disease-related information keyword in the second disease description information according to the second disease-related information keyword which is oriented by the possibility conditions acquired by all previous cycle decoding;
the data storage subsystem comprises a database, a database management system and a database management tool, wherein the database stores a medical information knowledge base, the medical information knowledge base stores trained and to-be-trained medical corpus data, and the medical corpus data comprises voice data and text data.
The step of encoding each disease condition keyword according to the first disease related information keyword, and obtaining the vector characteristic corresponding to the second disease description information according to the disease condition keyword characteristic corresponding to the extracted disease condition keyword comprises the following steps:
performing property characteristic correspondence on the condition keywords to obtain property characteristics of the condition keywords;
the category characteristics are corresponding according to the category of the illness state keywords, and the category characteristics of the illness state keywords are obtained;
performing part-of-speech characteristic correspondence on the illness state keywords according to the part of speech of the first illness related information keywords in the second illness state description information to obtain the part-of-speech characteristic of the illness state keywords;
the condition keyword characteristics corresponding to the condition keywords comprise the property characteristics, the category characteristics and the part-of-speech characteristics;
encoding the disease keyword characteristics corresponding to the disease keywords to obtain vector characteristics corresponding to the second disease description information;
the step of performing part-of-speech feature correspondence on the illness state keywords according to the part-of-speech of the first illness related information keywords in the second illness state description information to obtain the part-of-speech feature of the illness state keywords comprises the following steps:
determining the part of speech of the first disease-related information keyword in the second disease description information;
determining the part of speech of each illness state keyword in the second illness state description information;
encoding the condition keyword characteristics corresponding to the condition keywords, and obtaining the vector characteristics corresponding to the second condition description information includes:
according to the ordering condition of the condition keyword characteristics corresponding to the condition keywords in the second condition description information, forward and backward encoding to obtain first vector characteristics and second vector characteristics corresponding to the condition keywords; connecting the first vector characteristic with the second vector characteristic to obtain a vector characteristic corresponding to the disease keyword; obtaining the vector characteristics corresponding to the second illness state description information according to the vector characteristics corresponding to each illness state keyword;
the decoding according to the current decoding condition and the vector characteristic, and obtaining the likelihood condition of the current cycle decoding towards the second disease related information keyword comprises the following steps:
performing characteristic combination according to the current decoding condition and the vector characteristic to obtain a decoding combination characteristic;
performing characteristic correspondence on the decoding merging characteristics to obtain the possibility condition of the current cycle decoding of the second disease related information keyword;
and performing characteristic combination according to the current decoding condition and the vector characteristic, wherein obtaining a decoding combination characteristic comprises the following steps:
determining the vector characteristics corresponding to each disease keyword from the vector characteristics;
combining the current decoding condition with the vector characteristics corresponding to each illness state keyword respectively to obtain the combined characteristics corresponding to each illness state keyword;
obtaining decoding merging characteristics according to merging characteristics corresponding to the illness state keywords;
after the cyclic decoding termination condition is met, determining a second disease-related information keyword having disease relevance with the first disease-related information keyword in the second disease description information according to a second disease-related information keyword oriented by the possibility condition extracted by cyclic decoding in the past comprises:
when the possibility condition of cyclic decoding and extraction is decoding end type distribution, determining the part of speech of a second disease related information keyword corresponding to the second disease related information keyword which is oriented by the possibility condition of cyclic decoding and extraction in the past;
determining a target illness state keyword corresponding to the second illness related information keyword part of speech from the second illness state description information;
obtaining second disease related information keywords which have disease relevance with the first disease related information keywords in the second disease description information according to the target disease keywords;
the data subsystem comprises an SQLServer database and an Ado.NET or EntityFramework, wherein the C# maps records in the SQLServer database into C# objects by using the Ado.NET or EntityFramework mapping.
Drawings
FIG. 1 is a system block diagram of the present invention;
Detailed Description
As shown in fig. 1: the system comprises 4 parts: the system comprises a front subsystem, a data preprocessing subsystem, an intelligent analysis subsystem and a data storage subsystem;
the system comprises a front subsystem, a data preprocessing subsystem, an intelligent analysis subsystem and a data storage subsystem;
the front terminal system comprises a user health data module and a question-answering module, and comprises a special terminal, a handheld device terminal App and a Web APP, wherein the frame of the Web APP is Angular;
the user health data module comprises a health information recording sub-module and a statistics sub-module, wherein the health information recording sub-module is used for recording the treatment information of a user, and the statistics sub-module displays a statistical chart of the past inquiry conditions of the user through a front-end data visualization component library;
the question-answering module collects first illness state voice description information through voice acquisition equipment;
the first illness state voice description information is transmitted to a data preprocessing subsystem to perform data preprocessing to obtain second illness state description information;
the data preprocessing comprises the following steps: converting the first illness state voice description information into first illness state description information through voice recognition; and performing word segmentation processing and stop word removal processing on the first illness state description information to obtain second illness state description information.
The second condition description information is transmitted to the intelligent analysis subsystem for processing, and the processing comprises the following steps:
extracting a disease keyword from the second disease description information, and determining a first disease related information keyword in the second disease description information from the disease keyword; encoding each disease keyword according to the first disease related information keywords, and obtaining vector characteristics corresponding to the second disease description information according to the characteristics of the disease keywords corresponding to the extracted disease keywords; performing cyclic decoding according to the vector characteristics to determine the diseases, and acquiring the possibility of the second disease related information keyword; decoding according to the vector characteristics and the current decoding condition when each cycle is decoded, wherein the current decoding condition adopts the possibility condition of taking out the previous cycle decoding; after the cycle decoding termination condition is reached, determining a second disease-related information keyword which has disease relevance with the first disease-related information keyword in the second disease description information according to the second disease-related information keyword which is oriented by the possibility conditions acquired by all previous cycle decoding;
the data storage subsystem comprises a database, a database management system and a database management tool, wherein the database stores a medical information knowledge base, the medical information knowledge base stores trained and to-be-trained medical corpus data, and the medical corpus data comprises voice data and text data.
The step of encoding each disease condition keyword according to the first disease related information keyword, and obtaining the vector characteristic corresponding to the second disease description information according to the disease condition keyword characteristic corresponding to the extracted disease condition keyword comprises the following steps:
performing property characteristic correspondence on the condition keywords to obtain property characteristics of the condition keywords;
the category characteristics are corresponding according to the category of the illness state keywords, and the category characteristics of the illness state keywords are obtained;
performing part-of-speech characteristic correspondence on the illness state keywords according to the part of speech of the first illness related information keywords in the second illness state description information to obtain the part-of-speech characteristic of the illness state keywords;
the condition keyword characteristics corresponding to the condition keywords comprise the property characteristics, the category characteristics and the part-of-speech characteristics;
encoding the disease keyword characteristics corresponding to the disease keywords to obtain vector characteristics corresponding to the second disease description information;
the step of performing part-of-speech feature correspondence on the illness state keywords according to the part-of-speech of the first illness related information keywords in the second illness state description information to obtain the part-of-speech feature of the illness state keywords comprises the following steps:
determining the part of speech of the first disease-related information keyword in the second disease description information;
determining the part of speech of each illness state keyword in the second illness state description information;
encoding the condition keyword characteristics corresponding to the condition keywords, and obtaining the vector characteristics corresponding to the second condition description information includes:
according to the ordering condition of the condition keyword characteristics corresponding to the condition keywords in the second condition description information, forward and backward encoding to obtain first vector characteristics and second vector characteristics corresponding to the condition keywords; connecting the first vector characteristic with the second vector characteristic to obtain a vector characteristic corresponding to the disease keyword; obtaining the vector characteristics corresponding to the second illness state description information according to the vector characteristics corresponding to each illness state keyword;
the decoding according to the current decoding condition and the vector characteristic, and obtaining the likelihood condition of the current cycle decoding towards the second disease related information keyword comprises the following steps:
performing characteristic combination according to the current decoding condition and the vector characteristic to obtain a decoding combination characteristic;
performing characteristic correspondence on the decoding merging characteristics to obtain the possibility condition of the current cycle decoding of the second disease related information keyword;
and performing characteristic combination according to the current decoding condition and the vector characteristic, wherein obtaining a decoding combination characteristic comprises the following steps:
determining the vector characteristics corresponding to each disease keyword from the vector characteristics;
combining the current decoding condition with the vector characteristics corresponding to each illness state keyword respectively to obtain the combined characteristics corresponding to each illness state keyword;
obtaining decoding merging characteristics according to merging characteristics corresponding to the illness state keywords;
after the cyclic decoding termination condition is met, determining a second disease-related information keyword having disease relevance with the first disease-related information keyword in the second disease description information according to a second disease-related information keyword oriented by the possibility condition extracted by cyclic decoding in the past comprises:
when the possibility condition of cyclic decoding and extraction is decoding end type distribution, determining the part of speech of a second disease related information keyword corresponding to the second disease related information keyword which is oriented by the possibility condition of cyclic decoding and extraction in the past;
determining a target illness state keyword corresponding to the second illness related information keyword part of speech from the second illness state description information;
obtaining second disease related information keywords which have disease relevance with the first disease related information keywords in the second disease description information according to the target disease keywords;
the data subsystem comprises an SQLServer database and an Ado.NET or EntityFramework, wherein the C# maps records in the SQLServer database into C# objects by using the Ado.NET or EntityFramework mapping.
The encapsulation type of the C# object is USER, DIALOGUE and ADVICE, REPORT encapsulate the operation of adding and updating USER information under the corresponding account, when the USER uses the system, the USER needs to call a char () method under the USER class to input basic information of the USER, wherein the basic information comprises unique identification number, name, gender, age and login information such as account and password of the USER;
and adding, deleting and modifying operation of the question-answer record list is packaged in DIALOGUE, and all voice records under the user name are stored in DIALOGUE.
When the question and answer are finished, the ADVICE calls the ad device_user () method to give the medical ADVICE and prints out the medical REPORT by the print_report () method packaged in the REPORT, and returns to the front end for the user and doctor to check.
Claims (5)
1. The emergency department pre-examination and sorting system is characterized by comprising the following subsystems: the system comprises a front subsystem, a data preprocessing subsystem, an intelligent analysis subsystem and a data storage subsystem; the front terminal system comprises a user health data module and a question-answering module, and comprises a special terminal, a handheld device terminal App and a Web APP, wherein the frame of the Web APP is Angular; the user health data module comprises a health information recording sub-module and a statistics sub-module, wherein the health information recording sub-module is used for recording the treatment information of a user, and the statistics sub-module displays a statistical chart of the past inquiry conditions of the user through a front-end data visualization component library; the question-answering module collects first illness state voice description information through voice acquisition equipment; the first illness state voice description information is transmitted to a data preprocessing subsystem to perform data preprocessing to obtain second illness state description information; the second condition description information is transmitted to the intelligent analysis subsystem for processing, and the processing comprises the following steps: extracting a disease keyword from the second disease description information, and determining a first disease related information keyword in the second disease description information from the disease keyword; encoding each disease keyword according to the first disease related information keyword, and obtaining the vector characteristic corresponding to the second disease description information according to the disease keyword characteristic corresponding to the extracted disease keyword, wherein the vector characteristic specifically comprises: performing property characteristic correspondence on the condition keywords to obtain property characteristics of the condition keywords; the category characteristics are corresponding according to the category of the illness state keywords, and the category characteristics of the illness state keywords are obtained; performing part-of-speech characteristic correspondence on the illness state keywords according to the part of speech of the first illness related information keywords in the second illness state description information to obtain the part-of-speech characteristic of the illness state keywords; the condition keyword characteristics corresponding to the condition keywords comprise the property characteristics, the category characteristics and the part-of-speech characteristics; encoding the disease keyword characteristics corresponding to the disease keywords to obtain vector characteristics corresponding to the second disease description information; performing cyclic decoding according to the vector characteristics to determine the disease, and obtaining the possibility of the second disease related information keyword, specifically: according to the ordering condition of the condition keyword characteristics corresponding to the condition keywords in the second condition description information, forward and backward encoding to obtain first vector characteristics and second vector characteristics corresponding to the condition keywords; connecting the first vector characteristic with the second vector characteristic to obtain a vector characteristic corresponding to the disease keyword; obtaining the vector characteristics corresponding to the second illness state description information according to the vector characteristics corresponding to each illness state keyword; decoding is carried out according to the vector characteristics and the current decoding condition when each cycle is decoded, and the current decoding condition adopts the possibility condition of taking out the previous cycle decoding, specifically: performing characteristic combination according to the current decoding condition and the vector characteristic to obtain a decoding combination characteristic; performing characteristic correspondence on the decoding merging characteristics to obtain the possibility condition of the current cycle decoding of the second disease related information keyword; after the cycle decoding termination condition is reached, determining a second disease-related information keyword which has disease relevance with the first disease-related information keyword in the second disease description information according to the second disease-related information keyword which is oriented by the possibility conditions acquired by all previous cycle decoding; the data storage subsystem comprises a database, a database management system and a database management tool, wherein the database stores a medical information knowledge base, the medical information knowledge base stores medical corpus data which are trained and to be trained, and the medical corpus data comprises voice data and text data; the data storage subsystem comprises an SQLServer database, an SSMS database management system, a C# ADODOTNET database management tool and an Entity Framework database management tool, wherein the Entity Framework database management tool utilizes mapping one record in the SQLServer database to form a C# object; the encapsulation type of the C# object is USER, DIALOGUE and ADVICE, REPORT encapsulate the operations of adding and updating USER information under the corresponding account, when the USER uses the system, the USER needs to call a method of charu () under the USER class to input basic information of the USER, the basic information comprises unique identification numbers, names, sexes, ages and login information of the USER, the account and passwords, the DIALOGUE encapsulates the operations of adding, deleting and modifying a question-answer record list, and the DIALOGUE stores all voice records under the USER name; when the question and answer are finished, the ADVICE calls the ad device_user () method to give the medical ADVICE and prints out the medical REPORT by the print_report () method packaged in the REPORT, and returns to the front end for the user and doctor to check.
2. The system of claim 1, wherein: the data preprocessing comprises the following steps: converting the first illness state voice description information into first illness state description information through voice recognition; and performing word segmentation processing and stop word removal processing on the first illness state description information to obtain second illness state description information.
3. The system according to claim 2, wherein: the step of performing part-of-speech feature correspondence on the illness state keywords according to the part-of-speech of the first illness related information keywords in the second illness state description information to obtain the part-of-speech feature of the illness state keywords comprises the following steps: determining the part of speech of the first disease-related information keyword in the second disease description information; and determining the part of speech of each illness state keyword in the second illness state description information.
4. A system according to claim 3, characterized in that: and performing characteristic combination according to the current decoding condition and the vector characteristic, wherein obtaining a decoding combination characteristic comprises the following steps: determining the vector characteristics corresponding to each disease keyword from the vector characteristics; combining the current decoding condition with the vector characteristics corresponding to each illness state keyword respectively to obtain the combined characteristics corresponding to each illness state keyword; and obtaining decoding merging characteristics according to the merging characteristics corresponding to the illness state keywords.
5. The system according to claim 4, wherein: after the cycle decoding termination condition is met, determining a second disease-related information keyword having disease relevance with the first disease-related information keyword in the second disease description information according to a second disease-related information keyword oriented by the possibility condition extracted by the cycle decoding in the past comprises: when the possibility condition of cyclic decoding and extraction is decoding end type distribution, determining the part of speech of a second disease related information keyword corresponding to the second disease related information keyword which is oriented by the possibility condition of cyclic decoding and extraction in the past; determining a target illness state keyword corresponding to the second illness related information keyword part of speech from the second illness state description information; and obtaining second disease related information keywords which have disease relevance with the first disease related information keywords in the second disease description information according to the target disease keywords.
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Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106874643A (en) * | 2016-12-27 | 2017-06-20 | 中国科学院自动化研究所 | Build the method and system that knowledge base realizes assisting in diagnosis and treatment automatically based on term vector |
| CN107545148A (en) * | 2017-09-30 | 2018-01-05 | 旗瀚科技有限公司 | A kind of intelligent robot diagnoses question and answer interactive system |
| CN108182262A (en) * | 2018-01-04 | 2018-06-19 | 华侨大学 | Intelligent Answer System construction method and system based on deep learning and knowledge mapping |
| KR101887521B1 (en) * | 2017-06-21 | 2018-08-10 | 경북대학교 산학협력단 | System and method for management electronic medical record based on voice recognition, recording medium for performing the method |
| CN108920634A (en) * | 2018-06-30 | 2018-11-30 | 天津大学 | The skin disease characteristic analysis system of knowledge based map |
| CN110111887A (en) * | 2019-05-15 | 2019-08-09 | 清华大学 | Clinical aid decision-making method and device |
| CN110364234A (en) * | 2019-06-26 | 2019-10-22 | 浙江大学 | Electronic health record intelligent storage analyzing search system and method |
| CN110442732A (en) * | 2019-07-24 | 2019-11-12 | 万达信息股份有限公司 | A kind of intelligent medical guide method, system and storage medium |
| CN111462896A (en) * | 2020-03-31 | 2020-07-28 | 重庆大学 | Real-time intelligent auxiliary ICD coding system and method based on medical record |
| CN112164476A (en) * | 2020-09-28 | 2021-01-01 | 华南理工大学 | Medical consultation conversation generation method based on multitask and knowledge guidance |
| CN112507696A (en) * | 2021-02-04 | 2021-03-16 | 湖南大学 | Human-computer interaction diagnosis guiding method and system based on global attention intention recognition |
| CN112735597A (en) * | 2020-12-31 | 2021-04-30 | 荆门汇易佳信息科技有限公司 | Medical text disorder identification method driven by semi-supervised self-learning |
| CN112786194A (en) * | 2021-01-28 | 2021-05-11 | 北京一脉阳光医学信息技术有限公司 | Medical image diagnosis guide inspection system, method and equipment based on artificial intelligence |
| CN114171176A (en) * | 2021-12-15 | 2022-03-11 | 华中科技大学同济医学院附属协和医院 | Hospital triage data processing method and system based on Internet |
-
2022
- 2022-03-21 CN CN202210281171.0A patent/CN114628012B/en active Active
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106874643A (en) * | 2016-12-27 | 2017-06-20 | 中国科学院自动化研究所 | Build the method and system that knowledge base realizes assisting in diagnosis and treatment automatically based on term vector |
| KR101887521B1 (en) * | 2017-06-21 | 2018-08-10 | 경북대학교 산학협력단 | System and method for management electronic medical record based on voice recognition, recording medium for performing the method |
| CN107545148A (en) * | 2017-09-30 | 2018-01-05 | 旗瀚科技有限公司 | A kind of intelligent robot diagnoses question and answer interactive system |
| CN108182262A (en) * | 2018-01-04 | 2018-06-19 | 华侨大学 | Intelligent Answer System construction method and system based on deep learning and knowledge mapping |
| CN108920634A (en) * | 2018-06-30 | 2018-11-30 | 天津大学 | The skin disease characteristic analysis system of knowledge based map |
| CN110111887A (en) * | 2019-05-15 | 2019-08-09 | 清华大学 | Clinical aid decision-making method and device |
| CN110364234A (en) * | 2019-06-26 | 2019-10-22 | 浙江大学 | Electronic health record intelligent storage analyzing search system and method |
| CN110442732A (en) * | 2019-07-24 | 2019-11-12 | 万达信息股份有限公司 | A kind of intelligent medical guide method, system and storage medium |
| CN111462896A (en) * | 2020-03-31 | 2020-07-28 | 重庆大学 | Real-time intelligent auxiliary ICD coding system and method based on medical record |
| CN112164476A (en) * | 2020-09-28 | 2021-01-01 | 华南理工大学 | Medical consultation conversation generation method based on multitask and knowledge guidance |
| CN112735597A (en) * | 2020-12-31 | 2021-04-30 | 荆门汇易佳信息科技有限公司 | Medical text disorder identification method driven by semi-supervised self-learning |
| CN112786194A (en) * | 2021-01-28 | 2021-05-11 | 北京一脉阳光医学信息技术有限公司 | Medical image diagnosis guide inspection system, method and equipment based on artificial intelligence |
| CN112507696A (en) * | 2021-02-04 | 2021-03-16 | 湖南大学 | Human-computer interaction diagnosis guiding method and system based on global attention intention recognition |
| CN114171176A (en) * | 2021-12-15 | 2022-03-11 | 华中科技大学同济医学院附属协和医院 | Hospital triage data processing method and system based on Internet |
Non-Patent Citations (1)
| Title |
|---|
| 智能导诊服务机器人的设计与实现;崔浩;刘丰源;;计算机应用与软件(第07期);全文 * |
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