CN114628012A - Emergency department's preliminary examination go-no-go system - Google Patents
Emergency department's preliminary examination go-no-go system Download PDFInfo
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Abstract
The invention provides a pre-inspection and sorting system for an emergency department, which comprises: the front-end subsystem, the data preprocessing subsystem, the intelligent analysis subsystem and the data storage subsystem can analyze the description of the patient on the state of an illness through a machine learning algorithm, and provide an analysis result for a doctor to refer, so that the speed of the inquiry of an emergency department can be greatly increased, and medical resources are saved.
Description
Technical Field
The invention relates to the technical field of medical treatment, in particular to a pre-inspection and sorting system for an emergency department.
Background
The inquiry refers to the process of inquiry and answering by doctors to understand the cause, onset course, relevant symptoms and other disease-related conditions of the user's disease. The main purpose of the inquiry is that the doctor obtains the information of the disease of the useful patient to determine the treatment direction and strategy, which is the basis of the follow-up work and has important significance in the disease diagnosis and treatment. In the medical diagnosis and treatment process, doctors want to know the conditions of the patients comprehensively to make an accurate treatment method, and the doctors want to obtain the treatment as soon as possible and effectively. However, since the medical resources of the emergency department are more tense than those of other departments, in order to alleviate the contradiction between the shortage of medical resources of the emergency department and the large number of doctors and patients, many medical institutions adopt a pre-examination mode to acquire the personal basic information and the illness state of the user in advance, and doctors can acquire the information of the patients before the inquiry, thereby accelerating the speed of seeing a doctor.
However, the current pre-examination method can only obtain the basic information of the patient, but the information is not analyzed and the result is directly referred to by the doctor, so that the doctor still needs to make a preliminary judgment on the disease condition according to the basic condition of the patient and adjust the disease condition in the subsequent inquiry and treatment processes.
The invention aims to provide a pre-examination and triage system for an emergency department, which can analyze the description of the patient on the state of an illness by a machine learning algorithm and give an analysis result for a doctor to refer to, thereby greatly accelerating the inquiry speed of the emergency department and saving medical resources.
Disclosure of Invention
In order to solve the problem, the invention provides an emergency department pre-examination sorting system, which comprises:
the system comprises a front-end 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, the front terminal system comprises a special terminal, a handheld device terminal App and a Web APP, and the framework of the Web APP is Angular;
the user health data module comprises a health information recording submodule and a statistical submodule, the health information recording submodule is used for recording the treatment information of a user, and the statistical submodule displays a statistical chart of the past inquiry condition 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 for 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 processing on the first illness state description information to obtain second illness state description information.
The second disease description information is transmitted to the intelligent analysis subsystem for processing, and the processing comprises the following steps:
extracting disease condition keywords from the second disease condition description information, and determining first disease related information keywords in the second disease condition description information from the disease condition keywords; coding each disease condition keyword according to the first disease-related information keyword, and obtaining a vector characteristic corresponding to the second disease condition description information according to the disease condition keyword characteristic corresponding to the taken disease condition keyword; performing cyclic decoding according to the vector characteristics to determine a disease, and acquiring a possibility of the disease facing a second disease-related information keyword; decoding according to the vector characteristics and the current decoding condition during each cyclic decoding, wherein the current decoding condition adopts the possibility of being taken out by the previous cyclic decoding; after the condition of ending the loop decoding is reached, determining a second disease-related information keyword which is related to the disease condition and is in the second disease description information according to the second disease-related information keywords towards which all the possibility conditions obtained by the previous loop decoding are oriented;
the data storage subsystem comprises a database, a database management system and a database management tool, wherein a medical information knowledge base is stored in the database, 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 encoding of the disease condition keywords according to the first disease-related information keywords and the obtaining of the vector characteristics corresponding to the second disease condition description information according to the disease condition keyword characteristics corresponding to the extracted disease condition keywords comprise:
performing property characteristic correspondence on the disease condition keywords to obtain property characteristics of the disease condition keywords;
performing category characteristic correspondence according to the category of the disease condition keywords to obtain category characteristics of the disease condition keywords;
performing part-of-speech characteristic correspondence on the condition keywords according to the part-of-speech of the first disease-related information keywords in the second condition description information to obtain part-of-speech characteristics of the condition keywords;
the disease condition keyword characteristics corresponding to the disease condition keywords comprise the property characteristics, the category characteristics and the part-of-speech characteristics;
encoding the disease condition keyword characteristics corresponding to each disease condition keyword to obtain vector characteristics corresponding to the second disease condition description information;
the obtaining of the part-of-speech characteristics of the condition keywords according to the part-of-speech characteristics of the first disease-related information keywords in the second condition description information comprises:
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 disease condition keyword in the second disease condition description information;
the encoding of the disease condition keyword characteristics corresponding to each disease condition keyword to obtain the vector characteristics corresponding to the second disease condition description information includes:
according to the sorting condition of the disease condition keyword characteristics corresponding to each disease condition keyword in the second disease condition description information, forward and backward coding is carried out to obtain first vector characteristics and second vector characteristics corresponding to each disease condition keyword; connecting the first vector characteristic and the second vector characteristic to obtain a vector characteristic corresponding to the disease condition keyword; obtaining the vector characteristics corresponding to the second illness state description information according to the vector characteristics corresponding to the illness state keywords;
the decoding according to the current decoding condition and the vector characteristic to obtain the possibility condition of the current cycle decoding towards the second disease related information keyword comprises:
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 towards the second disease related information keyword;
the characteristic combination according to the current decoding condition and the vector characteristic to obtain the decoding combination characteristic comprises:
determining vector characteristics corresponding to the disease keywords from the vector characteristics;
respectively carrying out characteristic combination on the current decoding condition and the vector characteristics corresponding to the disease condition keywords to obtain combination characteristics corresponding to the disease condition keywords;
obtaining decoding merging characteristics according to the merging characteristics corresponding to the disease keywords;
after the condition of ending the loop decoding is met, determining a second disease-related information keyword which is related to the disease condition and is in the second disease description information according to a second disease-related information keyword towards which the possibility condition is taken out by the loop decoding in the past comprises the following steps:
when the possibility of the cyclic decoding extraction is the decoding ending type distribution, determining the part of speech of the second disease related information keyword corresponding to the second disease related information keyword towards which the possibility of the cyclic decoding extraction is directed all the time;
determining a target disease condition keyword corresponding to the part of speech of the second disease related information keyword from the second disease description information;
obtaining a second disease-related information keyword which has disease correlation with the first disease-related information keyword in the second disease description information according to the target disease keyword;
the data subsystem comprises an SQLServer database and Ado.NET or EntityFramework, and the C # maps records in the SQLServer database into a C # object by using Ado.NET or EntityFramework mapping.
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FIG. 1 is a block diagram of the system of the present invention;
Detailed Description
As shown in fig. 1: the system comprises 4 parts: the system comprises a front-end subsystem, a data preprocessing subsystem, an intelligent analysis subsystem and a data storage subsystem;
the system comprises a front-end 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 and answer module, the front terminal system comprises a special terminal, a handheld device terminal App and a Web APP, and a framework of the Web APP is Angular;
the user health data module comprises a health information recording submodule and a statistical submodule, the health information recording submodule is used for recording the treatment information of a user, and the statistical submodule displays a statistical chart of the past inquiry condition 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 for 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 processing on the first illness state description information to obtain second illness state description information.
The second disease description information is transmitted to the intelligent analysis subsystem for processing, and the processing comprises the following steps:
extracting disease condition keywords from the second disease condition description information, and determining first disease related information keywords in the second disease condition description information from the disease condition keywords; coding each disease condition keyword according to the first disease-related information keyword, and obtaining a vector characteristic corresponding to the second disease condition description information according to the disease condition keyword characteristic corresponding to the taken disease condition keyword; performing cyclic decoding according to the vector characteristics to determine a disease, and acquiring a possibility of the disease facing a second disease-related information keyword; decoding according to the vector characteristics and the current decoding condition during each cyclic decoding, wherein the current decoding condition adopts the possibility of being taken out by the previous cyclic decoding; after the condition of ending the loop decoding is reached, determining a second disease-related information keyword which is related to the disease condition and is in the second disease description information according to the second disease-related information keywords towards which all the possibility conditions obtained by the previous loop decoding are oriented;
the data storage subsystem comprises a database, a database management system and a database management tool, wherein a medical information knowledge base is stored in the database, 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 encoding of the disease condition keywords according to the first disease-related information keywords and the obtaining of the vector characteristics corresponding to the second disease condition description information according to the disease condition keyword characteristics corresponding to the extracted disease condition keywords comprise:
performing property characteristic correspondence on the disease condition keywords to obtain property characteristics of the disease condition keywords;
performing category characteristic correspondence according to the category of the disease condition keywords to obtain category characteristics of the disease condition keywords;
performing part-of-speech characteristic correspondence on the condition keywords according to the part-of-speech of the first disease-related information keywords in the second condition description information to obtain part-of-speech characteristics of the condition keywords;
the disease condition keyword characteristics corresponding to the disease condition keywords comprise the property characteristics, the category characteristics and the part-of-speech characteristics;
encoding the disease condition keyword characteristics corresponding to each disease condition keyword to obtain vector characteristics corresponding to the second disease condition description information;
the obtaining of the part-of-speech characteristics of the condition keywords according to the part-of-speech characteristics of the first disease-related information keywords in the second condition description information comprises:
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 disease condition keyword in the second disease condition description information;
the encoding of the disease condition keyword characteristics corresponding to each disease condition keyword to obtain the vector characteristics corresponding to the second disease condition description information includes:
according to the sorting condition of the disease condition keyword characteristics corresponding to each disease condition keyword in the second disease condition description information, forward and backward coding is carried out to obtain first vector characteristics and second vector characteristics corresponding to each disease condition keyword; connecting the first vector characteristic and the second vector characteristic to obtain a vector characteristic corresponding to the disease condition keyword; obtaining the vector characteristics corresponding to the second illness state description information according to the vector characteristics corresponding to the illness state keywords;
the decoding according to the current decoding condition and the vector characteristic to obtain the possibility condition of the current cycle decoding towards the second disease related information keyword comprises:
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 towards the second disease related information keyword;
the characteristic combination according to the current decoding condition and the vector characteristic to obtain the decoding combination characteristic comprises:
determining vector characteristics corresponding to the disease keywords from the vector characteristics;
respectively carrying out characteristic combination on the current decoding condition and the vector characteristics corresponding to the disease condition keywords to obtain combination characteristics corresponding to the disease condition keywords;
obtaining decoding merging characteristics according to the merging characteristics corresponding to the disease keywords;
determining, according to a second disease-related information keyword to which a possibility taken out from the past loop decoding is directed after the loop decoding termination condition is satisfied, a second disease-related information keyword having a disease state correlation with the first disease-related information keyword in the second disease description information includes:
determining the part of speech of the second disease-related information keyword corresponding to the second disease-related information keyword towards which the cyclic decoding extraction possibility situation is directed when the cyclic decoding extraction possibility situation is the decoding end type distribution;
determining a target disease condition keyword corresponding to the part of speech of the second disease related information keyword from the second disease description information;
obtaining a second disease-related information keyword which has disease correlation with the first disease-related information keyword in the second disease description information according to the target disease condition keyword;
the data subsystem comprises an SQLServer database and Ado.NET or EntityFramework, and the C # maps records in the SQLServer database into a C # object by using Ado.NET or EntityFramework mapping.
The packaging type of the C # object is USER, adding and updating operations of USER information under corresponding accounts are packaged by DIALOGUE, ADVICE and REPORT, when the USER uses the system, a charu () method under the USER class needs to be called to input USER basic information, and the basic information comprises a unique identification number, a name, a gender and an age of the USER and login information such as an account and a password;
the addition, deletion, modification and check operations of the question and answer record list are packaged in DIALOGUE, and all voice records under the user name are stored in DIALOGUE.
When the question and answer is finished, the ADVICE calls an ADVICE _ user () method to give a medical suggestion and a print _ REPORT () method packaged in the REPORT to print out a medical REPORT, and returns to the front end for the user and the doctor to view.
Claims (10)
1. A pre-examination and sorting system for an emergency department is characterized by comprising the following subsystems: the system comprises a front-end 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 and answer module, the front terminal system comprises a special terminal, a handheld device terminal App and a Web APP, and a framework of the Web APP is Angular;
the user health data module comprises a health information recording submodule and a statistical submodule, the health information recording submodule is used for recording the treatment information of a user, and the statistical submodule displays a statistical chart of the previous inquiry condition 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 for data preprocessing to obtain second illness state description information;
the second disease description information is transmitted to the intelligent analysis subsystem for processing, and the processing comprises the following steps:
extracting disease condition keywords from the second disease condition description information, and determining first disease related information keywords in the second disease condition description information from the disease condition keywords; coding each disease condition keyword according to the first disease-related information keyword, and obtaining a vector characteristic corresponding to the second disease condition description information according to the disease condition keyword characteristic corresponding to the taken disease condition keyword; performing cyclic decoding according to the vector characteristics to determine a disease, and acquiring a possibility of the disease facing a second disease-related information keyword; decoding according to the vector characteristics and the current decoding condition during each cyclic decoding, wherein the current decoding condition adopts the possibility of being taken out by the previous cyclic decoding; after the cyclic decoding termination condition is reached, determining a second disease-related information keyword which has disease correlation with the first disease-related information keyword in the second disease description information according to second disease-related information keywords towards which all the previous cyclic decoding possibilities are oriented;
the data storage subsystem comprises a database, a database management system and a database management tool, wherein a medical information knowledge base is stored in the database, 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.
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 processing on the first disease description information to obtain second disease description information.
3. The system of claim 2, wherein: the encoding of the disease condition keywords according to the first disease-related information keywords and the obtaining of the vector characteristics corresponding to the second disease condition description information according to the disease condition keyword characteristics corresponding to the extracted disease condition keywords comprise: performing property characteristic correspondence on the disease condition keywords to obtain property characteristics of the disease condition keywords; performing category characteristic correspondence according to the category of the disease condition keywords to obtain category characteristics of the disease condition keywords; performing part-of-speech characteristic correspondence on the condition keywords according to the part-of-speech of the first disease-related information keywords in the second condition description information to obtain part-of-speech characteristics of the condition keywords; the disease condition keyword characteristics corresponding to the disease condition keywords comprise the property characteristics, the category characteristics and the part-of-speech characteristics; and coding the disease condition keyword characteristics corresponding to each disease condition keyword to obtain the vector characteristics corresponding to the second disease condition description information.
4. The system of claim 3, wherein: the obtaining of the part-of-speech characteristics of the condition keywords according to the part-of-speech characteristics of the first disease-related information keywords in the second condition description information comprises: determining the part of speech of the first disease-related information keywords in the second disease description information; and determining the part of speech of each disease condition keyword in the second disease condition description information.
5. The system of claim 4, wherein: the encoding of the disease condition keyword characteristics corresponding to each disease condition keyword to obtain the vector characteristics corresponding to the second disease condition description information includes: according to the sorting condition of the disease condition keyword characteristics corresponding to each disease condition keyword in the second disease condition description information, forward and backward coding is carried out to obtain a first vector characteristic and a second vector characteristic corresponding to each disease condition keyword; connecting the first vector characteristic and the second vector characteristic to obtain a vector characteristic corresponding to the disease condition keyword; and obtaining the vector characteristics corresponding to the second disease condition description information according to the vector characteristics corresponding to the disease condition keywords.
6. The system of claim 5, wherein: the possibility of obtaining the keyword which is oriented to the second disease related information and decoded in the current cycle according to the current decoding condition and the vector characteristic comprises the following steps: performing characteristic combination according to the current decoding condition and the vector characteristic to obtain a decoding combination characteristic; and performing characteristic correspondence on the decoding merging characteristics to obtain the possibility condition of the current cycle decoding towards the second disease related information keyword.
7. The system of claim 6, wherein: the characteristic combination according to the current decoding condition and the vector characteristic to obtain the decoding combination characteristic comprises: determining vector characteristics corresponding to the disease keywords from the vector characteristics; respectively carrying out characteristic combination on the current decoding condition and the vector characteristics corresponding to the disease condition keywords to obtain combination characteristics corresponding to the disease condition keywords; and obtaining decoding merging characteristics according to the merging characteristics corresponding to the disease keywords.
8. The system of claim 7, wherein: after the condition of ending the loop decoding is met, determining a second disease-related information keyword which is related to the disease condition and is in the second disease description information according to a second disease-related information keyword towards which the possibility condition is taken out by the loop decoding in the past comprises the following steps: determining the part of speech of the second disease-related information keyword corresponding to the second disease-related information keyword towards which the cyclic decoding extraction possibility situation is directed when the cyclic decoding extraction possibility situation is the decoding end type distribution; determining target disease condition keywords corresponding to the part of speech of the second disease related information keywords from the second disease condition description information; and obtaining a second disease-related information keyword which has disease correlation with the first disease-related information keyword in the second disease description information according to the target disease keyword.
9. The system of claim 8, wherein: 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 maps one record in the SQLServer database into a C # object.
10. The system of claim 9, wherein: the packaging type of the C # object is USER, adding and updating operations of USER information under a corresponding account are packaged by DIALOGUE, ADVICE and REPORT, when the system is used by a USER, a charu () method under the USER class needs to be called to input basic information of the USER, the basic information comprises a unique identification number, a name, a gender, an age and login information of the USER, adding and deleting operations of a question and answer record list are packaged in DIALOGUE, and all voice records under the USER name are stored in DIALOGUE; when the question and answer is finished, the ADVICE calls an ADVICE _ user () method to give a medical suggestion and a print _ REPORT () method packaged in the REPORT to print out a medical REPORT, and returns to the front end for the user and the doctor to view.
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