CN115223721A - Patient cardiovascular and cerebrovascular disease tube number similarity analysis system based on metadata - Google Patents
Patient cardiovascular and cerebrovascular disease tube number similarity analysis system 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 field of medical treatment and is used for solving the problem that a community hospital or a community clinic cannot detect and analyze potential cardiovascular and cerebrovascular disease personnel, and comprises a data screening module, a similarity analysis module, a monitoring setting module and a factor setting module, wherein the data screening module is used for screening disease sample data of a patient in combination with the disease sample data, the factor setting module is used for setting external factors of the patient according to a disease standard data set, and the monitoring setting module is used for setting a monitoring time period of clinical data for the patient in combination with a plurality of influence factors; the invention combines the factors of basic information, diet, sleep and the like of potential personnel, and is convenient for community hospitals and community clinics to carry out disease similarity analysis on potential personnel with 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 patient cardiovascular and cerebrovascular disease tube number similarity analysis system based on metadata.
Background
Cardiovascular and cerebrovascular diseases are the general names of cardiovascular and cerebrovascular diseases, and generally refer to ischemic or hemorrhagic diseases of heart, brain and systemic tissues caused by hyperlipidemia, blood viscosity, atherosclerosis, hypertension and the like. Cardiovascular and cerebrovascular diseases are common diseases seriously threatening the health of human beings, particularly the health of middle-aged and elderly people over 50 years old, have the characteristics of high morbidity, high disability rate and the like, and even if the most advanced and perfect treatment means at present is applied, more than 50 percent of cerebrovascular accident survivors can not completely take care of the life;
in the prior art, because medical facilities are simple and convenient in community hospitals or community clinics, detection and analysis of potential cardiovascular and cerebrovascular diseases cannot be performed, the potential cardiovascular and cerebrovascular diseases need to go to a designated hospital for detection, the detection of the cardiovascular and cerebrovascular diseases is inconvenient, and the detection efficiency is poor;
therefore, a system for analyzing the similarity of the number of the cardiovascular and cerebrovascular diseases of the patient based on metadata is provided.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a system for analyzing the similarity of the number of cardiovascular and cerebrovascular diseases of a patient based on metadata.
The technical problem to be solved by the invention is as follows: how to carry out similar analysis on potential people with cardiovascular and cerebrovascular diseases based on multiple factors.
The purpose of the invention can be realized by the following technical scheme:
a metadata-based patient cardiovascular and cerebrovascular disease management number similarity analysis system 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, wherein the medical care terminal comprises a registration login unit and a data transceiving unit, the data transceiving unit is used for medical care personnel to input patient information of a patient and send the patient information to the server, and the server sends the patient information to the data screening module and the monitoring setting module; the data receiving and sending 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 disease sample data of a plurality of patients with 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 which are fed back to the server, and the server marks the plurality of finally screened disease sample data as the disease standard data set adapted to 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 the patient according to the disease standard data set to obtain a plurality of influencing factors for the cardiovascular and cerebrovascular disease similarity analysis of the patient;
under a plurality of influence factor, the monitoring setting module combines a plurality of influence factor to be used for setting up the monitoring interval of clinical data to the patient, and the data acquisition module is used for a plurality of clinical data of gathering the patient in the monitoring interval based on a plurality of influence factor to send a plurality of clinical data to the server, the server sends a plurality of clinical data to similar analysis module, similar analysis module is used for carrying out similar analysis with the clinical data of patient under different influence factor and disease standard data set, generates disease difference signal or disease similar signal.
Further, the patient information is the sex, age, height and weight of the patient;
the disease sample data specifically includes: basic information and sign data of a patient, wherein the basic information of the patient comprises sex, age, height and weight, and the sign data comprises a blood pressure value, a heart rate value, an electrocardiogram, blood conventional data, urine conventional data, blood fat data and blood sugar data;
the factor sample data comprises the times of eating, the amount of sleeping and the type of eating of the patient;
the clinical data are blood pressure values, heart rate values, electrocardiograms, blood routine values, urine routine values, blood lipid values and blood glucose values of the patient.
Further, the screening process of the data screening module is as follows:
step SS1: acquiring patient information of a patient to obtain the sex, age, height and weight of the patient; acquiring disease sample data of a plurality of patients with cardiovascular and cerebrovascular diseases stored in a server;
step SS2: primarily screening the disease sample data according to gender, and screening to obtain a plurality of disease sample data subjected to primary screening;
step SS3: performing secondary screening on the disease sample data according to age, and screening to obtain a plurality of secondarily screened disease sample data;
and step SS4: screening the disease sample data for three times 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 finally screened disease sample data.
Further, the disease criteria data set comprises a 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 the factor sample data corresponding to the disease sample data as a plurality of influencing factors for the similar analysis of the cardiovascular and cerebrovascular diseases 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 influence factors and disease sample data in a disease standard data set corresponding to the patient, and acquiring values corresponding to blood pressure values, heart rate values, blood routine values, urine routine values, blood lipid values and blood glucose values of the plurality of clinical data and the plurality of disease sample data;
step P2: after traversing and comparing, calculating the numerical difference between 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 value XYC, a heart rate difference value XLC, a blood routine difference value XCC, a urine routine difference value NYC, a blood fat difference value XZC and a blood sugar difference value XTC;
step P3: corresponding weight coefficients are respectively distributed for a blood pressure difference value XYC, a heart rate difference value XLC, a blood conventional difference value XCC, a urine conventional difference value NYC, a blood fat difference value XZC and a blood sugar difference value XTC, and data difference values SC of a plurality of clinical data of the patient under different influence factors and disease sample data in a corresponding disease standard data set are obtained by calculation through a formula SC = XYC × a1+ XLC × a2+ XCC × a3+ NCC × a4+ XZC × a5+ XTC × a 6; in the formula, a1, a2, a3, a4, a5 and a6 are all 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 between all clinical data of the patient under different influence factors and disease sample data in the disease standard data set is greater than or equal to the data difference threshold value, generating a disease difference signal;
step P5: and if the data difference value between any clinical data of the patient under different influence 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 the disease difference signal or the disease similarity signal to the server;
if the server receives the disease difference signal, no operation is performed;
and if the server receives the disease similar signal, the server sends the corresponding disease sample data to the medical care terminal, and the medical care terminal receives the disease sample data through the data receiving and sending unit and refers.
Furthermore, the registration login unit is used for registering and logging in the server after the medical staff inputs personal information and sending the personal information to the server for storage;
the personal information comprises the name of the medical staff and the mobile phone number of real-name authentication.
Compared with the prior art, the invention has the beneficial effects that:
the patient information of a patient is input through the data receiving and sending unit, the patient information is sent to the data screening module and the monitoring setting module, meanwhile, disease sample data of a plurality of patients with cardiovascular and cerebrovascular diseases and factor sample data corresponding to the disease sample data are stored in the server and sent to the data screening module, the data screening module screens the disease sample data of the patient in combination with 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 patient and sent to the similar analysis module, meanwhile, the server sends the factors corresponding to the disease standard data set to the factor setting module, the factor setting module sets external factors of the patient according to the disease standard data set to obtain a plurality of influencing factors for similar analysis of cardiovascular and cerebrovascular diseases of the patient, the monitoring setting module sets a monitoring time period of clinical data of the patient in combination with the plurality of influencing factors, the similar analysis module performs similar analysis on the clinical data and the disease standard data set of the patient under different influencing factors in combination with the data to generate a disease difference signal or a disease similar signal.
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In order to facilitate understanding for those skilled in the art, the present invention will be further described with reference to the accompanying drawings.
FIG. 1 is an overall system block diagram of the present invention.
Detailed Description
The technical solutions of the present invention will be described below clearly and completely in conjunction with the embodiments, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, a system for analyzing similarity of cardiovascular and cerebrovascular disease tube count of a patient based on metadata includes 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 sending unit;
after the community hospital or the community clinic is equipped with the system, the registration login unit is used for registering the login server after medical personnel input personal information and sending the personal information to the server for storage; the personal information comprises the name of the medical staff, the mobile phone number of real-name authentication and the like;
the data receiving and transmitting unit is used for medical staff to input patient information of a patient and transmit 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 transceiving unit is also used for receiving similar analysis results of the patient fed back by the server by medical personnel;
specifically, the patient information includes sex, age, height, weight, etc. of the patient;
the server stores disease sample data of a plurality of patients with cardiovascular and cerebrovascular diseases and factor sample data corresponding to the disease sample data;
in specific implementation, the disease sample data may be past disease data of a plurality of patients collected by cardiovascular and cerebrovascular departments in a certain hospital used by the system, and the past 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 lipid data, blood glucose data, etc.), etc.; the factor sample data comprises the times of eating, the amount of sleeping, the type of eating and the like of the patient;
the server sends the disease sample data to a 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 specifically comprises the following steps:
step SS1: acquiring patient information of a patient to obtain the sex, the age, the height and the weight of the patient; acquiring disease sample data of a plurality of patients with cardiovascular and cerebrovascular diseases stored in a server;
step SS2: primarily screening the disease sample data according to gender, and screening to obtain a plurality of disease sample data subjected to primary screening;
step SS3: secondarily screening the disease sample data according to age, and screening to obtain a plurality of secondarily screened disease sample data;
and step SS4: screening the disease sample data for three times according to the height, and screening to obtain a plurality of disease sample data subjected to three-time screening;
and step SS5: finally screening the disease sample data according to the weight, and screening to obtain a plurality of finally screened disease sample data;
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 adapted to the patient and sends the disease standard data set to the similarity analysis module;
it can be understood that the disease standard data set is not limited to one group of disease sample data, and may be a plurality of groups of disease sample data, and the factors need further similar analysis by a subsequent similar analysis module;
the server sends factor sample data corresponding to the disease standard data set to the factor setting module, and the factor setting module sets external factors of the patient according to the disease standard data set, specifically as follows:
extracting a plurality of disease sample data in the disease standard data set, and setting the factor sample data corresponding to the disease sample data as a plurality of influencing factors for the similar analysis of the cardiovascular and cerebrovascular diseases of the patient according to the factor sample data corresponding to the plurality of disease sample data;
the monitoring setting module is used for setting a monitoring time period of clinical data for the patient according to the plurality of influence factors;
in specific implementation, the monitoring time interval is comprehensively set according to factors such as sex, age, weight and the like of a patient, for example, the probability of suffering from cardiovascular and cerebrovascular diseases is higher when the patient is older than when the patient is younger, so the monitoring time interval is naturally longer when the patient is older;
the data acquisition module is used for acquiring a plurality of clinical data of the patient in a monitoring period based on a plurality of influence factors and sending the plurality of clinical data to the server, and the server sends the plurality of clinical data to the similarity analysis module;
in specific implementation, the data acquisition module is a special medical device 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 regulation value (specifically including red blood cell number, hemoglobin content, hematocrit, white blood cell number, white blood cell classification number and platelet number), urine regulation value (specifically including 1, urine color, normal range: light 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), granular tube type, normal range: none, transparent tube type, normal range: none or sporadic; 5, protein, normal range: negative, sugar, normal range: negative, ketone body, normal range: negative, urogenic, <10mg/L (quantitative), bilirubin, normal range: negative), blood lipid value (specifically referring to blood glucose value such as total blood glucose, high density lipoprotein, low density lipoprotein and the like), cholesterol value of the patient;
the similar analysis module is used for performing similar analysis on clinical data of the patient under different influence factors and a disease standard data set, and the analysis process specifically comprises the following steps:
step P1: acquiring a plurality of clinical data of a patient under different influence factors and disease sample data in a disease standard data set corresponding to the patient, and acquiring values corresponding to blood pressure values, heart rate values, blood routine values, urine routine values, blood fat values and blood sugar values of the plurality of clinical data and the plurality of disease sample data;
step P2: calculating the numerical difference between 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 after traversing and comparing to obtain a blood pressure difference value XYC, a heart rate difference value XLC, a blood routine difference value XCC, a urine routine difference value NYC, a blood fat difference value XZC and a blood sugar difference value XTC;
step P3: corresponding weight coefficients are respectively distributed for a blood pressure difference value XYC, a heart rate difference value XLC, a blood conventional difference value XCC, a urine conventional difference value NYC, a blood fat difference value XZC and a blood sugar difference value XTC, and data difference values SC of a plurality of clinical data of the patient under different influence factors and disease sample data in a corresponding disease standard data set are obtained by calculation through a formula SC = XYC × a1+ XLC × a2+ XCC × a3+ NCC × a4+ XZC × a5+ XTC × a 6; in the formula, a1, a2, a3, a4, a5 and a6 are all 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 between all clinical data of the patient under different influence factors and disease sample data in the disease standard data set is greater than or equal to the data difference threshold value, generating a disease difference signal;
and step P5: if the data difference value between any clinical data of the patient under different influence factors and disease sample data in the disease standard data set is smaller than the data difference threshold value, generating a disease similar signal;
the similar analysis module feeds back the disease difference signal or the disease similar signal to the server;
if the server receives the disease difference signal, no operation is performed;
and if the server receives the disease similar signal, the server sends the corresponding disease sample data to the medical care terminal, and the medical care terminal receives the disease sample data through the data receiving and sending unit and refers.
When the system works, medical staff input patient information of a patient through a data receiving and sending unit and send the patient information to a server, the server sends the patient information to a data screening module and a monitoring setting module, and the medical staff also receives a patient similarity analysis result fed back by the server through the data receiving and sending unit;
meanwhile, the server stores a plurality of disease sample data of the patients with cardiovascular and cerebrovascular diseases and factor sample data corresponding to the disease sample data, the server sends the disease sample data to the data screening module, the data screening module is used for screening the disease sample data of the patients in combination with the disease sample data to obtain the patient information of the patients to obtain the sex, the age, the height and the weight of the patients, then the disease sample data of the patients with cardiovascular and cerebrovascular diseases stored in the server is obtained, the disease sample data is primarily screened according to the sex to obtain a plurality of disease sample data subjected to primary screening, the disease sample data is secondarily screened according to the age to obtain a plurality of disease sample data subjected to secondary screening, the disease sample data is subjected to tertiary screening according to the height to obtain a plurality of disease sample data subjected to tertiary screening, the disease sample data is finally screened according to the weight to obtain a plurality of disease sample data subjected to final screening, the data subjected to the final screening is fed back to the server by the data screening module, and the server marks the plurality of disease sample data subjected to the final screening as a disease standard data set adapted to the patients and sends the disease sample data set to the similar 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 influence factors for 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 combines the plurality of influence factors to set a monitoring period of clinical data for the patient;
the data acquisition module is also used for acquiring a plurality of clinical data of the patient in a monitoring period based on a plurality of influence factors and sending the plurality of clinical data to the server, and the server sends the plurality of clinical data to the similarity analysis module;
performing similar analysis on clinical data of a patient under different influence factors and a disease standard data set through a similar analysis module to obtain a plurality of clinical data of the patient under different influence factors and disease sample data in the disease standard data set corresponding to the patient, obtaining numerical values corresponding to blood pressure values, heart rate values, blood routine values, urine routine values, blood fat values and blood sugar values of the plurality of clinical data and the plurality of disease sample data, calculating the difference between the blood pressure values, the heart rate values, the blood routine values, the urine routine values, the blood fat values, the blood sugar values and the blood sugar values, the heart rate values, the blood routine values, the urine routine values, the blood fat values and the numerical values of each item in each clinical data after traversing comparison, obtaining a blood pressure difference value XYC, a heart rate difference value XLC, a blood routine difference value XCC, a urine routine difference value NYC, a blood fat difference value XZC and a blood sugar difference value XTC, corresponding weight coefficients are respectively distributed for a blood pressure difference value XYC, a heart rate difference value XLC, a blood conventional difference value XCC, a urine conventional difference value NYC, a blood fat difference value XZC and a blood sugar difference value XTC, a data difference value SC between a plurality of clinical data of a patient under different influence factors and disease sample data in a corresponding disease standard data set is obtained by calculation through a formula SC = XYC × a1+ XLC × a2+ XCC × a3+ NCC × a4+ XZC × a5+ XTC × a6, if the data difference value between all the clinical data of the patient under different influence factors and the disease sample data in the disease standard data set is more than or equal to a data difference threshold value, a disease difference signal is generated, if the data difference value between any clinical data of the patient under different influence factors and the disease sample data in the disease standard data set is less than the data difference threshold value, a disease similar signal is generated, a similar analysis module feeds the disease difference signal or the disease similar signal back to a 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 sending unit and refers.
The above formulas are all calculated by taking the numerical value of the dimension, the formula is a formula which is obtained by acquiring a large amount of data and performing software simulation to obtain the latest real situation, the preset parameters in the formula are set by the technical personnel in the field 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 coefficient can be calculated as long as the proportional relation between the parameter and the quantized numerical value is not influenced.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise forms 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 the invention for and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (8)
1. A metadata-based patient cardiovascular and cerebrovascular disease management number similarity analysis system is characterized by comprising 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, wherein the medical care terminal comprises a registration login unit and a data receiving and sending unit, the data receiving and sending unit is used for medical care personnel to input patient information of a patient and send the patient information to the server, and the server sends the patient information to the data screening module and the monitoring setting module; the data receiving and sending 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 disease sample data of a plurality of patients with 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 which are fed back to the server, and the server marks the plurality of finally screened disease sample data as the disease standard data set adapted to 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 the patient according to the disease standard data set to obtain a plurality of influencing factors for the cardiovascular and cerebrovascular disease similarity analysis of the patient;
under a plurality of influence factor, the monitoring setting module combines a plurality of influence factor to be used for setting up the monitoring interval of clinical data to the patient, and the data acquisition module is used for a plurality of clinical data of gathering the patient in the monitoring interval based on a plurality of influence factor to send a plurality of clinical data to the server, the server sends a plurality of clinical data to similar analysis module, similar analysis module is used for carrying out similar analysis with the clinical data of patient under different influence factor and disease standard data set, generates disease difference signal or disease similar signal.
2. The system for analyzing the similarity of the cardiovascular and cerebrovascular disease tube count of a patient based on metadata as claimed in claim 1, wherein the patient information is the sex, age, height and weight of the patient;
the disease sample data specifically includes: basic information and sign data of a patient, wherein the basic information of the patient comprises sex, age, height and weight, and the sign data comprises a blood pressure value, a heart rate value, an electrocardiogram, blood conventional data, urine conventional data, blood fat data and blood sugar data;
the factor sample data comprises the times of eating, the amount of sleeping and the type of eating of the patient;
the clinical data are blood pressure values, heart rate values, electrocardiograms, blood routine values, urine routine values, blood lipid values and blood glucose values of the patient.
3. The system for analyzing the similarity of the cardiovascular and cerebrovascular disease tube counts of the patient based on the metadata according to claim 1, wherein the screening process of the data screening module is as follows:
step SS1: acquiring patient information of a patient to obtain the sex, age, height and weight of the patient; acquiring disease sample data of a plurality of patients with cardiovascular and cerebrovascular diseases stored in a server;
step SS2: primarily screening the disease sample data according to gender, and screening to obtain a plurality of disease sample data subjected to primary screening;
and step SS3: performing secondary screening on the disease sample data according to age, and screening to obtain a plurality of secondarily screened disease sample data;
and step SS4: screening the disease sample data for three times according to the height, and screening to obtain a plurality of disease sample data subjected to three-time screening;
and step SS5: and finally screening the disease sample data according to the weight, and screening to obtain a plurality of finally screened disease sample data.
4. The system according to claim 1, wherein the disease criteria data set comprises one or more sets of disease sample data.
5. The system for analyzing the similarity of the cardiovascular and cerebrovascular disease tube count of the patient based on the metadata as claimed in claim 1, wherein the operation process of the factor setting module 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;
and setting the factor sample data corresponding to the disease sample data as a plurality of influencing factors for the similar analysis of the cardiovascular and cerebrovascular diseases of the patient.
6. The system for analyzing the similarity of the cardiovascular and cerebrovascular disease tube count of the patient based on the metadata as claimed in claim 1, wherein the analysis process of the similarity analysis module is as follows:
step P1: acquiring a plurality of clinical data of a patient under different influence factors and disease sample data in a disease standard data set corresponding to the patient, and acquiring values corresponding to blood pressure values, heart rate values, blood routine values, urine routine values, blood fat values and blood sugar values of the plurality of clinical data and the plurality of disease sample data;
and step P2: after traversing and comparing, calculating the numerical difference between 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 value XYC, a heart rate difference value XLC, a blood routine difference value XCC, a urine routine difference value NYC, a blood fat difference value XZC and a blood sugar difference value XTC;
and step P3: distributing corresponding weight coefficients for a blood pressure difference value XYC, a heart rate difference value XLC, a blood routine difference value XCC, a urine routine difference value NYC, a blood fat difference value XZC and a blood sugar difference value XTC respectively, and calculating data difference values SC of a plurality of clinical data of the patient under different influence factors and disease sample data in a corresponding disease standard data set through a formula SC = XYC × a1+ XLC × a2+ XCC × a3+ NCC × a4+ XZC × a5+ XTC × a 6; in the formula, a1, a2, a3, a4, a5 and a6 are all 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 between all clinical data of the patient under different influence factors and disease sample data in the disease standard data set is greater than or equal to the data difference threshold value, generating a disease difference signal;
step P5: and if the data difference value between any clinical data of the patient under different influence factors and the disease sample data in the disease standard data set is smaller than the data difference threshold, generating a disease similar signal.
7. The system for analyzing the similarity of the cardiovascular and cerebrovascular disease tube counts of the patient based on the metadata as claimed in claim 6, wherein the similarity analysis module feeds back a disease difference signal or a disease similarity signal to the server;
if the server receives the disease difference signal, no operation is performed;
and if the server receives the disease similar signal, the server sends the corresponding disease sample data to the medical care terminal, and the medical care terminal receives the disease sample data through the data receiving and sending unit and refers.
8. The system for analyzing the similarity of the cardiovascular and cerebrovascular disease tube count of the patient based on the metadata according to claim 1, wherein the registration login unit is used for registering a login server after a medical worker inputs personal information and sending the personal information to the server for storage;
the personal information comprises the name of the medical staff and the mobile phone number of real-name authentication.
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