CN115223721B - System for analyzing similarity of cardiovascular and cerebrovascular disease tube numbers of patient based on metadata - Google Patents
System for analyzing similarity of cardiovascular and cerebrovascular disease tube numbers of patient based on metadata Download PDFInfo
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Abstract
The invention discloses a metadata-based patient cardiovascular and cerebrovascular disease tube number similarity analysis system, which belongs to the medical field and is used for solving the problem that potential personnel of cardiovascular and cerebrovascular diseases cannot be detected and analyzed by a community hospital or community clinic; the invention combines the basic information, diet, sleep and other factors of potential personnel, and is convenient for community hospitals and community clinics to carry out similar analysis on the potential personnel for cardiovascular and cerebrovascular diseases.
Description
Technical Field
The invention belongs to the field of medical treatment, relates to a disease similarity analysis technology, and particularly relates to a system for analyzing the similarity of cardiovascular and cerebrovascular disease tubes of a patient based on metadata.
Background
Cardiovascular and cerebrovascular diseases are the general terms of cardiovascular and cerebrovascular diseases, and refer broadly to ischemic or hemorrhagic diseases of heart, brain and systemic tissues caused by hyperlipidemia, blood viscosity, atherosclerosis, hypertension, etc. Cardiovascular and cerebrovascular diseases are common diseases seriously threatening the health of human beings, especially middle-aged and elderly people over 50 years old, and have the characteristics of high prevalence, high disability rate and the like, and even if the most advanced and perfect treatment means are applied at present, more than 50 percent of cerebrovascular accident survivors still can not take care of themselves completely;
in the prior art, due to the simple medical facilities in community hospitals or community clinics, potential personnel for cardiovascular and cerebrovascular diseases cannot be detected and analyzed, the potential personnel for cardiovascular and cerebrovascular diseases need to be detected in a designated hospital, the detection of cardiovascular and cerebrovascular diseases is inconvenient, and the detection efficiency is poor;
therefore, we propose a system for analyzing the similarity of cardiovascular and cerebrovascular disease tube numbers of patients based on metadata.
Disclosure of Invention
Aiming at the defects existing in the prior art, the invention aims to provide a metadata-based analysis system for similarity of cardiovascular and cerebrovascular disease tubes of patients.
The technical problems to be solved by the invention are as follows: how to perform similar analysis on potential personnel of cardiovascular and cerebrovascular diseases based on multiple factors.
The aim of the invention can be achieved by the following technical scheme:
the system comprises a data acquisition module, a medical terminal, a data screening module, a similarity analysis module, a monitoring setting module, a factor setting module and a server, wherein the medical terminal comprises a registration login unit and a data receiving and transmitting unit, the data receiving and transmitting unit is used for inputting patient information of a patient by medical staff and transmitting the patient information to the server, and the server transmits the patient information to the data screening module and the monitoring setting module; the data receiving and transmitting unit is also used for receiving the similar analysis result of the patient fed back by the server by the medical staff;
the server stores a plurality of disease sample data of patients suffering from cardiovascular and cerebrovascular diseases and factor sample data corresponding to the disease sample data, and sends the disease sample data to the data screening module;
the data screening module is used for screening the disease sample data of the patient by combining the disease sample data to obtain a plurality of finally screened disease sample data, the finally screened disease sample data are fed back to the server, and the server marks the finally screened disease sample data as a disease standard data set matched with the patient and sends the disease standard data set to the similarity analysis module;
the server sends factor sample data corresponding to the disease standard data set to a factor setting module, and the factor setting module sets external factors of a patient according to the disease standard data set to obtain a plurality of influence factors of similar analysis of cardiovascular and cerebrovascular diseases of the patient;
under a plurality of influence factors, the monitoring setting module combines the plurality of influence factors to set a monitoring period of clinical data for the patient, the data acquisition module is used for acquiring the plurality of clinical data of the patient in the monitoring period based on the plurality of influence factors and transmitting the plurality of clinical data to the server, the server transmits the plurality of clinical data to the similarity analysis module, and the similarity analysis module is used for carrying out similarity analysis on the clinical data of the patient under different influence factors and the disease standard data set to generate a disease difference signal or a disease similarity signal.
Further, the patient information is the sex, age, height and weight of the patient;
the disease sample data specifically includes: basic information of a patient and sign data, wherein the basic information of the patient comprises gender, age, height and weight, and the sign data comprises blood pressure value, heart rate value, electrocardiogram, blood routine data, urine routine data, blood fat data and blood sugar data;
factor sample data includes patient's number of diets, meal size, sleep size, and diet type;
the clinical data are blood pressure value, heart rate value, electrocardiogram, blood routine value, urine routine value, blood fat value and blood sugar value of the patient.
Further, the screening process of the data screening module is specifically as follows:
step SS1: patient information of a patient is obtained, and the sex, age, height and weight of the patient are obtained; obtaining disease sample data of a plurality of patients suffering from cardiovascular and cerebrovascular diseases stored in a server;
step SS2: preliminary screening is carried out on the disease sample data according to gender, and a plurality of disease sample data subjected to primary screening are obtained through screening;
step SS3: performing secondary screening on the disease sample data according to the age, and screening to obtain a plurality of disease sample data subjected to secondary screening;
step SS4: carrying out three-time screening on the disease sample data according to the height, and screening to obtain a plurality of disease sample data subjected to three-time screening;
step SS5: and finally screening the disease sample data according to the weight, and screening to obtain a plurality of disease sample data subjected to final screening.
Further, the disease standard dataset comprises one set of disease sample data or a plurality of sets of disease sample data.
Further, the working process of the factor setting module is specifically as follows:
extracting a plurality of disease sample data in a disease standard data set;
according to factor sample data corresponding to a plurality of disease sample data;
and setting factor sample data corresponding to the disease sample data as a plurality of influencing factors of the cardiovascular and cerebrovascular disease similar analysis of the patient.
Further, the analysis process of the similarity analysis module is specifically as follows:
step P1: acquiring a plurality of clinical data of a patient under different influencing factors and disease sample data in a disease standard data set corresponding to the patient, and acquiring a plurality of clinical data and numerical values corresponding to blood pressure values, heart rate values, blood routine values, urine routine values, blood lipid values and blood sugar values of the plurality of disease sample data;
step P2: after traversing comparison, calculating the numerical differences of the blood pressure value, the heart rate value, the blood routine value, the urine routine value, the blood fat value and the blood sugar value in each clinical data and the blood pressure value, the heart rate value, the blood routine value, the urine routine value, the blood fat value and the blood sugar value in each disease sample data to obtain a blood pressure difference XYC, a heart rate difference XLC, a blood routine difference XCC, a urine routine difference NYC, a blood fat difference XZC and a blood sugar difference XTC;
step P3: respectively distributing corresponding weight coefficients for a blood pressure difference XYC, a heart rate difference XLC, a blood routine difference XCC, a urine routine difference NYC, a blood lipid difference XZC and a blood glucose difference XTC, and calculating to obtain a plurality of clinical data of a patient under different influence factors and data difference SC of disease sample data in corresponding disease standard data sets through a formula SC=XYCXa1+XLC xa2+XCxa3+NCC xa4+XZC xa5+XTC xa 6; wherein a1, a2, a3, a4, a5 and a6 are weight coefficients with fixed values, and the values of a1, a2, a3, a4, a5 and a6 are all larger than zero;
step P4: if the data difference value of all clinical data of the patient under different influencing factors and disease sample data in the disease standard data set is greater than or equal to a data difference threshold value, generating a disease difference signal;
step P5: and if the data difference value of any clinical data of the patient under different influencing factors and the disease sample data in the disease standard data set is smaller than the data difference threshold value, generating a disease similar signal.
Further, the similarity analysis module feeds back a disease difference signal or a disease similarity signal to a server;
if the server receives the disease difference signal, no operation is performed;
if the server receives the disease similar signal, the corresponding disease sample data is sent to the medical care terminal, and the medical care terminal receives the disease sample data through the data receiving and transmitting unit and refers to the disease sample data.
Further, the registration login unit is used for registering the login server after the medical staff inputs personal information, and sending the personal information to the server for storage;
the personal information includes the name of the medical staff, the mobile phone number of the real name authentication.
Compared with the prior art, the invention has the beneficial effects that:
the invention inputs patient information of a patient through a data receiving and transmitting unit, and transmits the patient information to a data screening module and a monitoring setting module, meanwhile, a server stores a plurality of disease sample data of patients with cardiovascular and cerebrovascular diseases and factor sample data corresponding to the disease sample data, and transmits the disease sample data to the data screening module, the data screening module screens the disease sample data of the patients by combining the disease sample data to obtain a plurality of finally screened disease sample data, the finally screened disease sample data are calibrated into a disease standard data set matched with the patients and transmitted to a similar analysis module, meanwhile, the server transmits the factor sample data corresponding to the disease standard data set to the factor setting module, the factor setting module sets external factors of the patients according to the disease standard data set to obtain a plurality of influencing factors of similar analysis of the cardiovascular and cerebrovascular diseases of the patients, the monitoring setting module combines the plurality of influencing factors to set clinical data of the patients, and the similar analysis module combines the clinical data of the patients under different influencing factors to carry out similar analysis on the disease standard data set to generate a disease difference signal or a disease similar signal.
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The present invention is further described below with reference to the accompanying drawings for the convenience of understanding by those skilled in the art.
Fig. 1 is an overall system block diagram of the present invention.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, a metadata-based analysis system for similarity of cardiovascular and cerebrovascular disease tube numbers of patients comprises a data acquisition module, a medical care terminal, a data screening module, a similarity analysis module, a monitoring setting module, a factor setting module and a server;
the medical care terminal comprises a registration login unit and a data receiving and transmitting unit;
when the system is equipped in a community hospital or community clinic, the registration and login unit is used for inputting personal information by medical staff, registering and logging in the server, and sending the personal information to the server for storage; the personal information comprises the name of medical staff, the mobile phone number of real name authentication and the like;
the data receiving and transmitting unit is used for inputting patient information of a patient by medical staff, sending the patient information to the server, and sending the patient information to the data screening module and the monitoring setting module by the server; the data receiving and transmitting unit is also used for receiving the similar analysis result of the patient fed back by the server by the medical staff;
the specific explanation is that the patient information is the sex, age, height, weight and the like of the patient;
the server stores a plurality of pieces of disease sample data of patients suffering from cardiovascular and cerebrovascular diseases and factor sample data corresponding to the disease sample data;
in specific implementation, the disease sample data may be previous disease data of a plurality of patients collected by cardiovascular and cerebrovascular departments of a certain hospital used by the system, where the previous disease data specifically includes: basic information of the patient (sex, age, height, weight, etc.), physical sign data (blood pressure value, heart rate value, electrocardiogram, blood routine data, urine routine data, blood fat data, blood sugar data, etc.), etc.; factor sample data includes patient's number of diets, meal size, sleep size, diet type, etc.;
the server sends the disease sample data to a data screening module, and the data screening module combines the disease sample data to screen the disease sample data of the patient, wherein the screening process is specifically as follows:
step SS1: patient information of a patient is obtained, and the sex, age, height and weight of the patient are obtained; obtaining disease sample data of a plurality of patients suffering from cardiovascular and cerebrovascular diseases stored in a server;
step SS2: preliminary screening is carried out on the disease sample data according to gender, and a plurality of disease sample data subjected to primary screening are obtained through screening;
step SS3: performing secondary screening on the disease sample data according to the age, and screening to obtain a plurality of disease sample data subjected to secondary screening;
step SS4: carrying out three-time screening on the disease sample data according to the height, and screening to obtain a plurality of disease sample data subjected to three-time screening;
step SS5: finally screening the disease sample data according to the weight to obtain a plurality of disease sample data subjected to final screening;
the data screening module feeds back the finally screened plurality of disease sample data to the server, and the server marks the finally screened plurality of disease sample data as a disease standard data set matched with the patient and sends the disease standard data set to the similarity analysis module;
it is understood that the disease standard data set is not limited to one set of disease sample data, and may be multiple sets of disease sample data, and the factors require a subsequent similarity analysis module to perform further similarity analysis;
the server sends factor sample data corresponding to the disease standard data set to a factor setting module, and the factor setting module sets external factors of a patient according to the disease standard data set, specifically as follows:
extracting a plurality of disease sample data in a disease standard data set, and setting the factor sample data corresponding to the disease sample data as a plurality of influencing factors of the cardiovascular and cerebrovascular disease similar analysis of the patient according to the factor sample data corresponding to the plurality of disease sample data;
in a plurality of influencing factors, the monitoring setting module combines the plurality of influencing factors to set a monitoring period of clinical data for a patient;
in the implementation, the monitoring period is comprehensively set according to the factors such as the sex, the age, the weight and the like of the patient, for example, the probability of suffering from cardiovascular and cerebrovascular diseases is larger than the probability of suffering from cardiovascular and cerebrovascular diseases when the age is smaller, so that the monitoring period naturally becomes longer when the age of the patient is larger;
the data acquisition module is used for acquiring a plurality of clinical data of a patient in a monitoring period based on a plurality of influencing factors, sending the plurality of clinical data to the server, and sending the plurality of clinical data to the similarity analysis module by the server;
in specific implementation, the data acquisition module is special medical equipment and medical equipment for acquiring cardiovascular and cerebrovascular diseases, such as a cardiovascular and cerebrovascular detector and the like;
the clinical data are blood pressure value, heart rate value, electrocardiogram, blood routine value (specifically comprising red blood cell number, hemoglobin content, hematocrit, white blood cell number, white blood cell classification number, platelet number), urine routine value (specifically comprising: 1, urine color, normal range: pale yellow; 2, urine transparency, normal range: clear; 3, uric acid alkalinity (urine pH value), normal range: 6.5;4, red blood cell normal range: male: 0, female: 0-2 (high-power visual field), white blood cell normal range: male: 0-3, female: 0-5 (high-power visual field), particle tube type, normal range: none, transparent tube type, normal range: none or even; 5, protein, normal range: negative, sugar, normal range: negative, ketone body, normal range: negative, urobilinogen, <10mg/L (quantitative), biliary value, normal range: negative), blood lipid value (specifically: total cholesterol, triglyceride, high-density lipoprotein and low-density lipoprotein content), blood glucose value, etc.;
the similarity analysis module is used for carrying out similarity analysis on clinical data of patients under different influence factors and a disease standard data set, and the analysis process is specifically as follows:
step P1: acquiring a plurality of clinical data of a patient under different influencing factors and disease sample data in a disease standard data set corresponding to the patient, and acquiring a plurality of clinical data and numerical values corresponding to blood pressure values, heart rate values, blood routine values, urine routine values, blood lipid values and blood sugar values of the plurality of disease sample data;
step P2: after traversing comparison, calculating the numerical differences of the blood pressure value, the heart rate value, the blood routine value, the urine routine value, the blood fat value and the blood sugar value in each clinical data and the blood pressure value, the heart rate value, the blood routine value, the urine routine value, the blood fat value and the blood sugar value in each disease sample data to obtain a blood pressure difference XYC, a heart rate difference XLC, a blood routine difference XCC, a urine routine difference NYC, a blood fat difference XZC and a blood sugar difference XTC;
step P3: respectively distributing corresponding weight coefficients for a blood pressure difference XYC, a heart rate difference XLC, a blood routine difference XCC, a urine routine difference NYC, a blood lipid difference XZC and a blood glucose difference XTC, and calculating to obtain a plurality of clinical data of a patient under different influence factors and data difference SC of disease sample data in corresponding disease standard data sets through a formula SC=XYCXa1+XLC xa2+XCxa3+NCC xa4+XZC xa5+XTC xa 6; wherein a1, a2, a3, a4, a5 and a6 are weight coefficients with fixed values, and the values of a1, a2, a3, a4, a5 and a6 are all larger than zero;
step P4: if the data difference value of all clinical data of the patient under different influencing factors and disease sample data in the disease standard data set is greater than or equal to a data difference threshold value, generating a disease difference signal;
step P5: if the data difference value of any clinical data of the patient under different influencing factors and the disease sample data in the disease standard data set is smaller than a data difference threshold value, generating a disease similar signal;
the similarity analysis module feeds back the disease difference signal or the disease similarity signal to the server;
if the server receives the disease difference signal, no operation is performed;
if the server receives the disease similar signal, the corresponding disease sample data is sent to the medical care terminal, and the medical care terminal receives the disease sample data through the data receiving and transmitting unit and refers to the disease sample data.
A system for analyzing the similarity of cardiovascular and cerebrovascular diseases of patient based on metadata features that the medical personnel input the patient information to server via data transceiver, the server sends the patient information to the data screening module and the monitoring setting module, and medical staff also receives the similar analysis result of the patient fed back by the server through the data receiving and transmitting unit;
meanwhile, disease sample data of a plurality of patients suffering from cardiovascular and cerebrovascular diseases and factor sample data corresponding to the disease sample data are stored in a server, the server sends the disease sample data to a data screening module, the data screening module combines the disease sample data to screen the disease sample data of the patients to obtain patient information of the patients to obtain gender, age, height and weight of the patients, then the disease sample data of a plurality of patients suffering from cardiovascular and cerebrovascular diseases stored in the server are obtained, the disease sample data are primarily screened according to gender to obtain a plurality of disease sample data subjected to primary screening, the disease sample data are secondarily screened according to age to obtain a plurality of disease sample data subjected to secondary screening, the disease sample data are subjected to tertiary screening according to height to obtain a plurality of disease sample data subjected to tertiary screening, the disease sample data subjected to final screening are finally screened according to weight, the data screening module feeds back the plurality of disease sample data subjected to final screening to the server, and the server marks the plurality of disease sample data subjected to final screening to the patient adaptation disease standard data set and sends the disease sample data subjected to the final screening to the similarity analysis module;
the server sends factor sample data corresponding to the disease standard data set to a factor setting module, the factor setting module sets external factors of a patient according to the disease standard data set, extracts a plurality of disease sample data in the disease standard data set, sets the factor sample data corresponding to the disease sample data as a plurality of influencing factors of cardiovascular and cerebrovascular disease similar analysis of the patient according to the factor sample data corresponding to the plurality of disease sample data, and the monitoring setting module sets a monitoring period of clinical data for the patient by combining the plurality of influencing factors;
the data acquisition module is also used for acquiring a plurality of clinical data of a patient in a monitoring period based on a plurality of influencing factors, sending the plurality of clinical data to the server, and sending the plurality of clinical data to the similarity analysis module by the server;
the clinical data of the patient under different influencing factors and the disease standard data set are subjected to similar analysis through a similar analysis module, a plurality of clinical data of the patient under different influencing factors and disease sample data in the disease standard data set corresponding to the patient are obtained, a plurality of blood pressure values, heart rate values, blood routine values, urine routine values, blood lipid values and blood glucose values corresponding to the plurality of clinical data and the plurality of disease sample data are obtained, after traversing comparison, the numerical differences of the blood pressure values, heart rate values, blood routine values, urine routine values, blood lipid values, blood glucose values and the blood pressure values, heart rate values, blood routine values, urine routine values, blood lipid values and blood glucose values in each disease sample data are calculated, and blood pressure difference XYC, heart rate difference XLC, blood routine difference XCC, urine routine difference NYC, blood lipid difference XZC and blood glucose difference XTC are obtained, respectively distributing corresponding weight coefficients for a blood pressure difference XYC, a heart rate difference XLC, a blood routine difference XCC, a urine routine difference NYC, a blood lipid difference XZC and a blood glucose difference XTC, calculating according to the formula SC=XYCXa1+XLC xa2+XCxa3+NCC xa4+XZC xCx5+XTC xa 6 to obtain data differences SC of a plurality of clinical data of a patient under different influencing factors and corresponding disease sample data in the disease standard data set, generating a disease difference signal if the data differences of all the clinical data of the patient under different influencing factors and the disease sample data in the disease standard data set are larger than or equal to a data difference threshold, generating a disease similarity signal if the data differences of any clinical data of the patient under different influencing factors and the disease sample data in the disease standard data set are smaller than the data difference threshold, feeding back the disease difference signal or the disease similarity signal to a server by a similarity analysis module, if the server receives the disease difference signal, no operation is performed, and if the server receives the disease similar signal, the corresponding disease sample data is sent to the medical care terminal, and the medical care terminal receives the disease sample data through the data receiving and transmitting unit and refers to the disease sample data.
The above formulas are all formulas with dimensions removed and numerical calculation, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, preset parameters in the formulas are set by a person skilled in the art according to the actual situation, the weight coefficient and the scale coefficient are specific numerical values obtained by quantizing each parameter, the subsequent comparison is convenient, and the scale relation between the parameters and the quantized numerical values is not influenced.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (4)
1. The system is characterized by comprising a data acquisition module, a medical terminal, a data screening module, a similarity analysis module, a monitoring setting module, a factor setting module and a server, wherein the medical terminal comprises a registration login unit and a data receiving and transmitting unit, the data receiving and transmitting unit is used for inputting patient information of a patient by medical staff and transmitting the patient information to the server, and the server transmits the patient information to the data screening module and the monitoring setting module; the data receiving and transmitting unit is also used for receiving the similar analysis result of the patient fed back by the server by the medical staff;
the server stores a plurality of disease sample data of patients suffering from cardiovascular and cerebrovascular diseases and factor sample data corresponding to the disease sample data, and sends the disease sample data to the data screening module;
the data screening module is used for screening the disease sample data of the patient by combining the disease sample data, and the screening process is specifically as follows:
step SS1: patient information of a patient is obtained, and the sex, age, height and weight of the patient are obtained; obtaining disease sample data of a plurality of patients suffering from cardiovascular and cerebrovascular diseases stored in a server;
step SS2: preliminary screening is carried out on the disease sample data according to gender, and a plurality of disease sample data subjected to primary screening are obtained through screening;
step SS3: performing secondary screening on the disease sample data according to the age, and screening to obtain a plurality of disease sample data subjected to secondary screening;
step SS4: carrying out three-time screening on the disease sample data according to the height, and screening to obtain a plurality of disease sample data subjected to three-time screening;
step SS5: finally screening the disease sample data according to the weight to obtain a plurality of disease sample data subjected to final screening;
the data screening module feeds back a plurality of finally screened disease sample data to the server, and the server marks the plurality of finally screened disease sample data as a disease standard data set matched with a patient and sends the disease standard data set to the similarity analysis module;
the server sends factor sample data corresponding to the disease standard data set to a factor setting module, and the factor setting module sets external factors of a patient according to the disease standard data set, and the working process is as follows:
extracting a plurality of disease sample data in a disease standard data set;
according to factor sample data corresponding to a plurality of disease sample data;
setting factor sample data corresponding to the disease sample data as a plurality of influencing factors of the cardiovascular and cerebrovascular disease similar analysis of the patient;
under a plurality of influence factors, the monitoring setting module combines the plurality of influence factors to set a monitoring period of clinical data for a patient, the data acquisition module is used for acquiring the plurality of clinical data of the patient in the monitoring period based on the plurality of influence factors and transmitting the plurality of clinical data to the server, the server transmits the plurality of clinical data to the similarity analysis module, and the similarity analysis module is used for carrying out similarity analysis on the clinical data of the patient under different influence factors and a disease standard data set, wherein the analysis process is specifically as follows:
step P1: acquiring a plurality of clinical data of a patient under different influencing factors and disease sample data in a disease standard data set corresponding to the patient, and acquiring a plurality of clinical data and numerical values corresponding to blood pressure values, heart rate values, blood routine values, urine routine values, blood lipid values and blood sugar values of the plurality of disease sample data;
step P2: after traversing comparison, calculating the numerical differences of the blood pressure value, the heart rate value, the blood routine value, the urine routine value, the blood fat value and the blood sugar value in each clinical data and the blood pressure value, the heart rate value, the blood routine value, the urine routine value, the blood fat value and the blood sugar value in each disease sample data to obtain a blood pressure difference XYC, a heart rate difference XLC, a blood routine difference XCC, a urine routine difference NYC, a blood fat difference XZC and a blood sugar difference XTC;
step P3: respectively distributing corresponding weight coefficients for a blood pressure difference XYC, a heart rate difference XLC, a blood routine difference XCC, a urine routine difference NYC, a blood lipid difference XZC and a blood glucose difference XTC, and calculating to obtain a plurality of clinical data of a patient under different influence factors and data difference SC of disease sample data in corresponding disease standard data sets through a formula SC=XYCXa1+XLC xa2+XCxa3+NCC xa4+XZC xa5+XTC xa 6; wherein a1, a2, a3, a4, a5 and a6 are weight coefficients with fixed values, and the values of a1, a2, a3, a4, a5 and a6 are all larger than zero;
step P4: if the data difference value of all clinical data of the patient under different influencing factors and disease sample data in the disease standard data set is greater than or equal to a data difference threshold value, generating a disease difference signal;
step P5: if the data difference value of any clinical data of the patient under different influencing factors and the disease sample data in the disease standard data set is smaller than a data difference threshold value, generating a disease similar signal;
the similarity analysis module feeds back the disease difference signal or the disease similarity signal to the server, if the server receives the disease difference signal, no operation is performed, if the server receives the disease similarity signal, the corresponding disease sample data is sent to the medical care terminal, and the medical care terminal receives the disease sample data through the data receiving and transmitting unit and refers to the disease sample data;
the method for using the system is also included, and specifically comprises the following steps:
step S100, patient information of a patient is input through a data receiving and transmitting unit, the patient information is transmitted to a data screening module and a monitoring setting module, and a server stores a plurality of disease sample data of patients suffering from cardiovascular and cerebrovascular diseases and factor sample data corresponding to the disease sample data and transmits the disease sample data to the data screening module;
step S200, a data screening module screens disease sample data of a patient by combining the disease sample data to obtain a plurality of finally screened disease sample data, the plurality of finally screened disease sample data are marked as disease standard data sets matched with the patient and sent to a similar analysis module, and meanwhile, a server sends factor sample data corresponding to the disease standard data sets to a factor setting module;
step S300, a factor setting module sets external factors of a patient according to a disease standard data set to obtain a plurality of influence factors of similar analysis of cardiovascular and cerebrovascular diseases of the patient, and a monitoring setting module sets a monitoring period of clinical data of the patient by combining the plurality of influence factors;
in step S400, the similarity analysis module performs similarity analysis on clinical data of the patient under different influencing factors and the disease standard data set by combining the data to generate a disease difference signal or a disease similarity signal.
2. The metadata-based cardiovascular and cerebrovascular disease tube number similarity analysis system for patients according to claim 1, wherein the patient information is the sex, age, height and weight of the patient;
the disease sample data specifically includes: basic information of a patient and sign data, wherein the basic information of the patient comprises gender, age, height and weight, and the sign data comprises blood pressure value, heart rate value, electrocardiogram, blood routine data, urine routine data, blood fat data and blood sugar data;
factor sample data includes patient's number of diets, meal size, sleep size, and diet type;
the clinical data are blood pressure value, heart rate value, electrocardiogram, blood routine value, urine routine value, blood fat value and blood sugar value of the patient.
3. The metadata-based cardiovascular and cerebrovascular disease tube number similarity analysis system of claim 1, wherein the disease standard dataset comprises one or more sets of disease sample data.
4. The metadata-based patient cardiovascular and cerebrovascular disease management similarity analysis system according to claim 1, wherein the registration login unit is used for registering the login server after the medical staff inputs personal information, and transmitting the personal information to the server for storage;
the personal information includes the name of the medical staff, the mobile phone number of the real name authentication.
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