CN109741835B

CN109741835B - Chronic nephropathy supervision method, device, equipment and storage medium based on big data

Info

Publication number: CN109741835B
Application number: CN201811477186.4A
Authority: CN
Inventors: 吴潇诚
Original assignee: Ping An Technology Shenzhen Co Ltd
Current assignee: Ping An Technology Shenzhen Co Ltd
Priority date: 2018-12-04
Filing date: 2018-12-04
Publication date: 2024-05-03
Anticipated expiration: 2038-12-04
Also published as: CN109741835A

Abstract

The invention discloses a chronic kidney disease supervision method, device, equipment and storage medium based on big data. The method comprises the following steps: acquiring physical condition data of each object to be screened in the crowd to be screened; according to a pre-stored big data analysis model, analyzing the physical condition data of each object to be screened, and screening out the object suffering from chronic kidney disease; determining the chronic kidney disease stage corresponding to the object according to a pre-stored chronic kidney disease stage standard and physical condition data of the object; according to the physical condition data of the object and the chronic kidney disease stage corresponding to the object, a chronic kidney disease management scheme aiming at the object is formulated; and issuing the chronic kidney disease management scheme to the terminal equipment of the object, and/or issuing the chronic kidney disease management scheme to the terminal equipment of the medical staff. By the mode, the technical problem that monitoring and management cannot be carried out on each period of chronic kidney disease in the prior art is solved.

Description

Chronic nephropathy supervision method, device, equipment and storage medium based on big data

Technical Field

The invention relates to the technical field of big data processing, in particular to a chronic kidney disease supervision method, device, equipment and storage medium based on big data.

Background

Along with the improvement of living standard, people enjoy life and various problems are caused by the influences of various factors such as working pressure, life work and rest, eating habits and the like. Among them, chronic kidney disease (Chronic KIDNEY DISEASE, CKD) is particularly problematic.

According to statistics, 1.5 million (about 10.8% of the total population) adults in our country have different degrees of kidney function impairment, of which about 1.5% of chronic kidney disease patients develop end-stage kidney disease, i.e., about 200 ten thousand chronic kidney disease patients develop end-stage kidney disease. However, of the approximately 200 ten thousand end-stage renal patients, only 45 ten thousand patients are statistically treated for dialysis, and this accounts for only 0.03% of the total population of end-stage renal patients treated for dialysis and the total cost of the medical insurance fund is 3% of the total cost of the medical insurance fund in the current year. The burden pressure of the treatment expense on the medical insurance fund of China is becoming serious according to the current increasing speed of the chronic kidney disease.

Therefore, it is needed to provide a method for monitoring and managing each period of chronic kidney disease, so as to make a management and treatment scheme for each period of patient according to the monitoring results of different periods, thereby reducing the increase speed of the number of chronic kidney disease patients and reducing the burden of medical insurance funds.

The foregoing is provided merely for the purpose of facilitating understanding of the technical solutions of the present invention and is not intended to represent an admission that the foregoing is prior art.

Disclosure of Invention

The invention mainly aims to provide a chronic kidney disease supervision method, device, equipment and storage medium based on big data, and aims to solve the technical problems that monitoring and management of each period of chronic kidney disease cannot be carried out in the prior art, and the increase speed of the number of chronic kidney disease patients and the burden of medical insurance funds are fundamentally reduced.

In order to achieve the above object, the present invention provides a chronic kidney disease supervision method based on big data, the method comprising the steps of:

the chronic kidney disease management platform acquires physical condition data of each object to be screened in the crowd to be screened;

Analyzing the physical condition data of each object to be screened according to a pre-stored big data analysis model, and screening out objects with chronic kidney diseases;

determining the chronic kidney disease stage corresponding to the object according to pre-stored chronic kidney disease stage standard and physical condition data of the object;

According to the physical condition data of the subject and the chronic kidney disease stage corresponding to the subject, formulating a chronic kidney disease management scheme for the subject;

And issuing the chronic kidney disease management scheme to the terminal equipment of the object so that the object diagnoses according to the chronic kidney disease management scheme, and/or issuing the chronic kidney disease management scheme to the terminal equipment of medical staff so that the medical staff manages the object according to the chronic kidney disease management scheme.

Preferably, the formulating a chronic kidney disease management scheme for the subject according to the physical condition data of the subject and the chronic kidney disease stage corresponding to the subject includes:

determining the risk level of the object according to the chronic kidney disease stage corresponding to the object;

and according to the physical condition data of the subject and the risk level, formulating a chronic kidney disease management scheme aiming at the subject.

Preferably, the determining the risk level of the subject according to the chronic kidney disease stage corresponding to the subject includes:

If the chronic kidney disease stage corresponding to the subject is an inflammatory reaction stage or a renal function tasting stage, determining that the risk level facing the subject is a first risk level;

if the chronic kidney disease stage corresponding to the subject is a kidney function voyage period, determining that the risk level facing the subject is a second risk level;

If the chronic kidney disease stage corresponding to the subject is a kidney failure stage, determining that the risk level facing the subject is a third risk level;

The risk level of the second risk level is higher than the risk level of the first risk level, and the risk level of the third risk level is higher than the risk level of the second risk level and the risk level of the first risk level.

Preferably, the method further comprises, before analyzing the physical condition data of each subject to be screened according to a pre-stored big data analysis model and screening out the subject suffering from chronic kidney disease:

And constructing the big data analysis model according to the pre-stored international disease classification coding set and sample data.

Preferably, the constructing the big data analysis model according to the pre-stored international disease classification coding set and the sample data includes:

According to a preset screening rule, screening sample data conforming to the screening rule from the sample data as a punishment factor, and constructing a punishment function;

Performing residual square sum minimization calculation on the punishment function by using a statistical square tolerance method, and removing residual square sums greater than constraint conditions by taking a sparse rule operator as the constraint conditions in the calculation process to obtain a regression model;

and training the regression model according to a pre-stored international disease classification coding set and sample data to obtain the big data analysis model.

Preferably, after the issuing of the chronic kidney disease management scheme to the terminal device of the subject and the issuing of the chronic kidney disease management scheme to the terminal device of the medical staff, the method further comprises:

Receiving a medicine purchase request triggered by terminal equipment of the object, and distributing prescription medicine for the object, wherein the prescription medicine is prescribed for the object by a doctor according to the chronic kidney disease management scheme;

The receiving the medicine purchase request triggered by the terminal equipment of the object, and distributing prescription medicine to the object comprises the following steps:

receiving the medicine purchase request, and extracting first identification information for identifying the object from the medicine purchase request;

Searching prescription information corresponding to the first identification information in a pre-established prescription management library according to a pre-stored mapping relation table, wherein the prescription information at least comprises prescription drugs and second identification information for identifying the object, and the mapping relation is a corresponding relation between the first identification information and the second identification information;

Searching a patient file corresponding to the first identification information in a pre-established patient archive according to the first identification information, and extracting prescription drug delivery information and common prescription drugs of the object from the searched patient file;

and when the matching degree of the prescription medicine and the common prescription medicine is larger than a threshold value, sending the prescription medicine and the prescription medicine distribution information to a medical center pharmacy so that the medical center pharmacy can distribute the prescription medicine for the object according to the prescription medicine distribution information.

Preferably, before the prescription drug and the prescription drug delivery information are sent to a medical center pharmacy, the method further includes:

extracting a payment mode from the medicine purchase request, and if the payment mode is payment by using a medical insurance card, performing identity verification on an object initiating the medicine purchase request, and determining that the medicine purchase request is effective;

the step of verifying the identity of the object initiating the medicine purchase request, and determining that the medicine purchase request is valid comprises the following steps:

Acquiring first biometric information of the object using the medical insurance card and an identification number of the medical insurance card;

acquiring second biological characteristic information of the holder of the medical insurance card corresponding to the identification number from a social security platform according to the identification number;

Matching the first biometric information with the second biometric information;

and if the first biological characteristic information is matched with the second biological characteristic information, determining that the medicine purchase request is valid.

In addition, in order to achieve the above object, the present invention also provides a chronic kidney disease supervision apparatus based on big data, the apparatus comprising:

the physical condition data acquisition module is used for acquiring physical condition data of each object to be screened in the crowd to be screened;

the chronic kidney disease object screening module is used for analyzing the physical condition data of each object to be screened according to a pre-stored big data analysis model and screening out objects with chronic kidney disease;

the chronic kidney disease stage determining module is used for determining the chronic kidney disease stage corresponding to the object according to a pre-stored chronic kidney disease stage standard and physical condition data of the object;

A chronic kidney disease management scheme making module for making a chronic kidney disease management scheme for the subject according to the physical condition data of the subject and the chronic kidney disease stage corresponding to the subject;

A chronic kidney disease management scheme sending module, configured to issue the chronic kidney disease management scheme to a terminal device of the subject, so that the subject diagnoses according to the chronic kidney disease management scheme, and/or issue the chronic kidney disease management scheme to a terminal device of a medical staff, so that the medical staff manages the subject according to the chronic kidney disease management scheme

In addition, in order to achieve the above object, the present invention also proposes a chronic kidney disease supervision apparatus based on big data, the apparatus comprising: a memory, a processor, and a big data based chronic kidney disease monitor stored on the memory and executable on the processor, the big data based chronic kidney disease monitor configured to implement the steps of the big data based chronic kidney disease monitor method as described above.

In addition, in order to achieve the above object, the present invention also proposes a storage medium having stored thereon a big data based chronic kidney disease monitor program which, when executed by a processor, implements the steps of the big data based chronic kidney disease monitor method as described above.

According to the chronic kidney disease supervision scheme based on big data, various physical state data of objects to be screened are analyzed according to the pre-built big data analysis model, objects with chronic kidney disease are screened out, then a chronic kidney disease management scheme specially aiming at the objects is formulated according to the physical state data of the objects and corresponding chronic kidney disease stages, so that patients can be treated according to the chronic kidney disease management scheme suitable for the patients, potential chronic kidney disease patients can be screened out as early as possible, reasonable management scheme can be timely given to the potential chronic kidney disease patients, the development of the potential chronic kidney disease patients is prevented from being changed into chronic kidney disease as far as possible, and the requirements of reducing the increase speed of the number of the chronic kidney disease patients and relieving the burden of medical insurance funds are met.

Drawings

FIG. 1 is a schematic diagram of a configuration of a big data based chronic kidney disease supervision apparatus of a hardware operating environment according to an embodiment of the present invention;

FIG. 2 is a schematic flow chart of a first embodiment of a chronic kidney disease monitoring method based on big data according to the present invention;

FIG. 3 is a flow chart of a second embodiment of the method for monitoring chronic kidney disease based on big data according to the present invention;

Fig. 4 is a block diagram of a first embodiment of the chronic kidney disease monitor based on big data according to the present invention.

The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.

Detailed Description

It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.

Referring to fig. 1, fig. 1 is a schematic structural diagram of a chronic kidney disease supervision apparatus based on big data in a hardware operation environment according to an embodiment of the present invention.

As shown in fig. 1, the big data based chronic kidney disease supervision apparatus may include: a processor 1001, such as a central processing unit (Central Processing Unit, CPU), a communication bus 1002, a user interface 1003, a network interface 1004, a memory 1005. Wherein the communication bus 1002 is used to enable connected communication between these components. The user interface 1003 may include a Display, an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may further include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., a wireless FIdelity (WI-FI) interface). The Memory 1005 may be a high-speed random access Memory (Random Access Memory, RAM) Memory or a stable Non-Volatile Memory (NVM), such as a disk Memory. The memory 1005 may also optionally be a storage device separate from the processor 1001 described above.

Those skilled in the art will appreciate that the structure shown in fig. 1 does not constitute a limitation of the big data based chronic kidney disease monitoring device, and may include more or fewer components than shown, or certain components in combination, or a different arrangement of components.

As shown in fig. 1, an operating system, a data storage module, a network communication module, a user interface module, and a chronic kidney disease monitor based on big data may be included in the memory 1005 as one storage medium.

In the chronic kidney disease monitoring device based on big data shown in fig. 1, the network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 in the big data based chronic kidney disease monitoring device of the present invention may be disposed in the big data based chronic kidney disease monitoring device, and the big data based chronic kidney disease monitoring device calls the big data based chronic kidney disease monitoring program stored in the memory 1005 through the processor 1001 and executes the big data based chronic kidney disease monitoring method provided by the embodiment of the present invention.

The embodiment of the invention provides a chronic kidney disease supervision method based on big data, and referring to fig. 2, fig. 2 is a flow diagram of a first embodiment of the chronic kidney disease supervision method based on big data.

In this embodiment, the method for monitoring chronic kidney disease based on big data includes the following steps:

Step S10: the chronic kidney disease management platform acquires physical condition data of each object to be screened in the crowd to be screened.

Specifically, the crowd to be screened in this embodiment may be: healthy people, people with basic diseases, people with medium risk, people with high risk, people with severe complicated diseases, etc.

Further, the focus of screening work in this case is mainly on people with diabetes, hypertension, hyperuricemia, gout, family history of kidney disease, and elderly over 65 years old.

In addition, during the screening, screening may be performed in conjunction with a physical examination report before the subject satisfying the above conditions, such as urine routine, serum creatinine, microalbumin, etc. in the physical examination report.

Furthermore, the above mentioned physical condition data may comprise at least one of the following:

Blood pressure, blood lipid, blood glucose data, psychological mood data, lifestyle data, basic disease history data, diet preference data, general-purpose medicine data, etc., are not listed here, nor are any restrictions made thereto.

In addition, it should be understood that the above-mentioned manner of obtaining the physical condition data of each object to be screened in the population to be screened may be: the method comprises the steps of establishing communication connection between a chronic kidney disease management platform and an existing electronic health management platform, an electronic prescription management platform and various large data platforms in advance, and then acquiring physical condition data of objects meeting requirements from the various platforms.

It should be noted that the above is merely illustrative, and the technical solution of the present application is not limited in any way, and a person skilled in the art may set the subject to be screened, the physical condition data of the subject to be screened, and the conditions to be followed in the screening process according to the need, which is not limited herein.

Step S20: and analyzing the physical condition data of each object to be screened according to a pre-stored big data analysis model, and screening out the object suffering from chronic kidney disease.

It should be understood that the big data analysis model used in the present embodiment is constructed based on the pre-stored international disease classification code set and sample data before performing step S20.

In particular, the above-mentioned international classification of diseases (international Classification of diseases, ICD), in particular a system in which diseases are classified according to rules and expressed in terms of coding, based on certain characteristics of the diseases. Therefore, the accuracy of the big data analysis model can be effectively ensured by constructing the big data analysis model through the ICD code set and the sample data.

The sample data is ecological status data of different users, and may be specifically obtained from a hospital information system (Hospital Information System, HIS information system) of each large hospital, a health management platform, or other platform storing a large amount of physical status data of users.

In addition, in a specific implementation, in order to simplify the training process, a series of processes may be performed on the sample data, for example, a biased estimation process may be performed on the sample data based on a compression estimation method, to obtain a regression model, and then training is performed on the regression model according to the international disease classification code set and the sample data, to obtain the big data analysis model.

Specifically, in this embodiment, the compression estimation method (LASSO) is used to perform biased estimation on the sample data to obtain a regression model, specifically, a relatively refined model is obtained by constructing a penalty function, so that it compresses some coefficients, and at the same time, sets some coefficients to zero. The regression model thus obtained retains the advantage of sample data shrinkage, thereby making the process of obtaining a large data analysis model for subsequent training relatively simple.

The way to construct the regression model is roughly as follows:

firstly, according to a preset screening rule, screening sample data conforming to the screening rule from the sample data as a punishment factor, and constructing a punishment function.

Specifically, in the embodiment, when the penalty function is constructed, instead of using all sample data as penalty factors, some sample data conforming to the screening rule are selectively screened out to be used as penalty factors to construct the penalty function, so that the subsequently obtained regression model can have better performance parameters and can reduce the calculated amount.

Furthermore, it should be understood that the above screening rule may be data that specifies that the characteristics of the screening data are representative when screening the sample data, and the specific screening rule may be set by those skilled in the art as required, and is not limited thereto.

In addition, the penalty function is a kind of constraint function, namely the penalty function commonly known in the prior art. Regarding the construction of the function, a person skilled in the art can find related data to realize the function by himself, and only needs to construct sample data conforming to the screening rule as a penalty factor in the construction process.

And then, performing residual square Sum minimization calculation on the penalty function by using a statistical square tolerance method (RSS), and removing residual square Sum larger than constraint conditions by taking a sparse rule operator as the constraint conditions in the calculation process to obtain a regression model.

In this embodiment, the sparse rule operator (Lasso regularization) is also referred to as L1 norm regularization (L1 regularization or LASSO). It is an important means in machine learning (MACHINE LEARNING), and in the support vector machine (support vector machine) learning process, it is actually a process of solving the optimum for the objective function (i.e., the penalty function described above). Therefore, in the process of carrying out residual square sum minimization calculation on the punishment function by utilizing RSS, the L1 norm regularization is used as a constraint condition, and the residual square sum larger than the constraint condition is removed, so that the learning result meets sparsification, the constructed regression model is ensured to be a model with a characteristic feature (simple structure and good contractility), and the training process can be greatly simplified under the condition of ensuring the accuracy of a big data analysis model of subsequent training.

It should be noted that the above is only a specific implementation manner, and the technical solution of the present invention is not limited, and in a specific implementation manner, a person skilled in the art may select a training manner to construct the big data analysis model according to needs, which is not limited herein.

Step S30: and determining the chronic kidney disease stage corresponding to the subject according to the pre-stored chronic kidney disease stage standard and the physical condition data of the subject.

Specifically, the chronic kidney disease distribution criteria in this example are shown in table 1:

TABLE 1 chronic kidney disease staging table

Stage by stage	Description of the invention	Glomerular Filtration Rate (GFR)
			1	Renal injury index (+), normal GFR	>90
2	Renal injury index (+), slight decrease in GFR	60～89
			3	Moderate decrease in GFR	30～59
4	GFR severe drop	15～29
			5	Renal failure	<15 Or dialysis

As can be seen from Table 1, when the stage of chronic kidney disease is determined, it is mainly classified according to GFR (unit: ml/min.1.73 m ²) carried in the physical status data.

Step S40: and according to the physical condition data of the subject and the chronic kidney disease stage corresponding to the subject, formulating a chronic kidney disease management scheme aiming at the subject.

It will be appreciated that, due to different chronic kidney disease stages and different physical condition data, the risk faced by the patient will be different, and thus in formulating a chronic kidney disease management regimen for the subject, the risk level faced by the subject may be combined, and the specific actions may be: firstly, determining the risk level of the object according to the chronic kidney disease stage corresponding to the object; and then, according to the physical condition data of the subject and the risk level, formulating a chronic kidney disease management scheme aiming at the subject.

Further, for convenience of explanation, the risk levels are roughly divided into three levels, specifically, a first risk level, a second risk level and a third risk level in this embodiment. And, the risk level of the second risk level is higher than the risk level of the first risk level, and the risk level of the third risk level is higher than the risk level of the second risk level and the risk level of the first risk level.

Regarding the manner of determining the risk level faced by the subject according to the chronic kidney disease stage corresponding to the subject, the manner is specifically as follows:

And if the chronic kidney disease stage corresponding to the subject is an inflammatory reaction stage or a renal function tasting stage, determining that the risk level facing the subject is a first risk level.

The inflammatory reaction period is chronic kidney disease stage 1 (CKD stage 1), and the patient has normal creatinine and undamaged kidney function, and only has clinical indications such as urine protein, occult blood, edema, hypertension and the like. Urine proteins, occult blood, etc. are "silent" except for edema and hypertension which can cause obvious discomfort to the patient.

That is, this stage is the easiest to cure, but many patients miss the treatment time because of 'no pain and no itching', and the invention screens out the patients according to the big data analysis model and combines the physical condition data of the patients to develop a chronic kidney disease management scheme suitable for the patients, thereby controlling the worsening of the illness state of the patients in time and helping the patients to cure as early as possible.

The aforementioned renal function tasting period is chronic kidney disease stage 2 (CKD stage 2). The term "renal function compensation" means that the renal function is slightly impaired, and the daily needs of the body can be satisfied by the compensation function of the kidney itself. At this stage, the blood creatinine is substantially between 133 and 177. Mu. Mol/L. About half of the nephrons work properly and the patient himself has little sensation.

That is, if timely and regular treatment is obtained, clinical healing can be achieved in the renal function compensation period.

Thus, for a patient at a first risk level, when a chronic kidney disease management scheme conforming to the physical condition of the patient is formulated according to the physical condition data of the patient, reasonable nutrition guidance (such as a platform, by establishing a cooperative relationship with a professional nutrition, customizing a recipe conforming to the patient for the patient by the professional nutrition) exercise guidance (corresponding exercise planning is given, the exercise condition of the patient is monitored, exercise data is recorded), treatment guidance (such as external blood pressure and weight monitoring) and treatment emphasis is placed on the primary morbidity and cardiovascular diseases of the cordial patient mainly by strictly monitoring the anemia, the nutrition, the abnormal calcium-phosphorus metabolism condition and the thyroid function of the current patient.

Further, if the chronic kidney disease stage corresponding to the subject is a kidney function vectorial tasting stage, determining that the subject is at a second risk level.

The kidney function voicing period is chronic kidney disease period 3 and chronic kidney disease period 4 (CKD 3 period and CKD4 period). The decompensation period refers to a serious damage of kidney function, and the decompensation function of the kidney cannot meet the daily needs of the body. At this stage, the blood creatinine is substantially between 178 and 442. Mu. Mol/L, and the nephron is already damaged by more than two thirds or more. At this point, the patient has begun to feel debilitated, but the symptoms remain insignificant. Therefore, if timely and normal treatment is obtained, half of the probability still remains to achieve clinical cure.

Thus, for a patient at a second risk level, when formulating a chronic kidney disease management regimen that meets his or her physical condition based on his or her physical condition data, the given chronic kidney disease management regimen specifically requires a regimen that includes reasonably sophisticated medication, continuous dietary therapy, monitoring of various kidney indicators, and the like, and prepares to establish a dialysis pathway.

Further, if the chronic kidney disease stage corresponding to the subject is a renal failure stage, determining that the subject is at a third risk level.

The renal failure stage is chronic kidney disease stage 5 (CKD stage 5). That is, chronic kidney disease has developed as uremia, and thus chronic kidney disease management schemes are mainly based on dialysis treatment and kidney transplantation, and are supplemented with reasonable diet treatment, exercise treatment, and the like.

It should be noted that the foregoing is only a specific implementation manner, and the technical solution of the present invention is not limited to the specific implementation manner, and those skilled in the art may set the implementation manner as required, which is not limited herein.

Step S50: and issuing the chronic kidney disease management scheme to the terminal equipment of the object so that the object diagnoses according to the chronic kidney disease management scheme, and/or issuing the chronic kidney disease management scheme to the terminal equipment of medical staff so that the medical staff manages the object according to the chronic kidney disease management scheme.

Specifically, the terminal device of the object and the terminal device of the medical staff may be mobile devices such as smart phones, tablet computers, personal computers, and the like.

Furthermore, it should be understood that after receiving a chronic kidney disease management regimen for a patient himself, the patient may diagnose according to the chronic kidney disease management regimen, for example, go to a medical institution to visit a doctor for a doctor or perform preventive treatment according to diet or exercise planning in the chronic kidney disease management regimen.

In addition, the medical staff manages the object according to the chronic kidney disease management scheme, specifically, medical staff in family doctors or community hospitals can guide and track and evaluate the illness state of patients, and if the abnormality of the patients is found, the patients can be timely transferred to superior hospitals, such as district hospitals, trimethyl hospitals, dialysis centers and the like.

It should be noted that the foregoing is merely illustrative, and the technical solution of the present invention is not limited thereto, and in the specific implementation, those skilled in the art may set the solution as required, which is not limited thereto.

According to the chronic kidney disease supervision method based on big data, the physical state data of various objects to be screened are analyzed according to the pre-built big data analysis model, objects with chronic kidney disease are screened, then a chronic kidney disease management scheme specially aiming at the objects is formulated according to the physical state data of the objects and the corresponding chronic kidney disease stage, so that patients can be treated according to the chronic kidney disease management scheme suitable for the patients, potential chronic kidney disease patients can be screened as early as possible, reasonable management scheme is given to the potential chronic kidney disease patients in time, the development of the chronic kidney disease is prevented as far as possible, and the requirements of reducing the growth speed of the number of the chronic kidney disease patients and relieving the burden of medical insurance funds are met.

Referring to fig. 3, fig. 3 is a flow chart of a second embodiment of a chronic kidney disease supervision method based on big data according to the invention.

In this example, in the original step S50, "the chronic kidney disease management scheme is issued to the terminal device of the subject so that the subject diagnoses according to the chronic kidney disease management scheme, and/or the chronic kidney disease management scheme is issued to the terminal device of the medical staff so that the medical staff manages the subject according to the chronic kidney disease management scheme", which is specifically defined as the content in step S50'. The other steps are substantially the same, and will not be described in detail herein, except for the following main description:

based on the first embodiment, the chronic kidney disease monitoring method based on big data in this embodiment further includes, after the step S50':

step S60: and receiving a medicine purchase request triggered by the terminal equipment of the object, and distributing prescription medicines for the object.

Regarding the operation of "receiving the purchase request triggered by the terminal device of the subject and dispensing the prescription drug for the subject" in step S60, the following sub-steps may be specifically implemented:

(1) And receiving the medicine purchase request, and extracting first identification information for identifying the object from the medicine purchase request.

Specifically, in order to realize the full-period supervision of the chronic kidney disease patient, in practical application, the chronic kidney disease management platform can establish communication connection with a prescription sharing platform, hospital information systems (Hospital Information System, HIS information systems) of various big hospitals, medical center pharmacy, health management platform and other platforms which can relate to patient information, so that the patient or medical staff can conveniently guide prescription information into a prescription management library of the prescription sharing platform, and when receiving the medicine purchasing request, the chronic kidney disease management platform can acquire relevant information from each platform and each mechanism which are in communication connection with the chronic kidney disease management platform.

It should be understood that in a specific application, the medical center pharmacy may be a drug delivery facility for providing drugs to hospitals, or may be a top pharmacy or community hospital set up by related departments.

The terminal device to be used may be any device such as a smart phone, a tablet pc, or a personal computer that can be communicatively connected to the chronic kidney disease management platform or the prescription sharing platform, and is not limited thereto.

Correspondingly, the triggered medicine purchase request can be specifically a medicine purchase request made by an object (hereinafter referred to as a patient) suffering from chronic kidney disease through a monitoring event and a program corresponding to a function key when the function key binding the medicine purchase request is operated on an Application (APP) integrated with medicine purchase and prescription uploading and installed on the terminal equipment. When receiving a request sent by a monitoring event corresponding to the function key, the chronic kidney disease management platform determines that the terminal equipment triggers a medicine purchase request.

The first identification information for identifying the patient of the drug may specifically be an identification card number of the patient, a card number of a medical insurance card, etc. which are not listed here, nor are any restrictions imposed thereon.

(2) Searching prescription information corresponding to the first identification information in a pre-established prescription management library according to a pre-stored mapping relation table.

Specifically, the prescription information includes at least the prescription drug and second identification information for identifying the patient.

And, the prescription drug is specifically prescribed for the patient by a doctor according to the chronic kidney disease management scheme.

In addition, the mapping relationship is a correspondence relationship between the first identification information and the second identification information.

As can be seen from the above description, since the correspondence between the first identification information and the second identification information is stored in the mapping relation table, when the prescription information corresponding to the first identification information is searched in the prescription management library established in advance according to the pre-stored mapping relation table, specifically, the second identification information matched with the first identification information is searched in the prescription management library, and then the prescription information containing the second identification information is used as the prescription information corresponding to the first identification information.

That is, the second identification information included in the prescription information corresponds to the first identification information, i.e., if the first identification information is an identification card number, the second identification information should also include the identification card number. However, the first identification information is input by the patient when the patient initiates the purchase request, or is obtained from the pre-filled personal information by the terminal device when the patient initiates the purchase request, and the second identification information is input by the medical staff when the patient visits the hospital, or is input when the patient handles the health file, which is not listed here, and is not limited.

In addition, the prescription drugs mentioned above specifically refer to drugs that must be formulated, purchased and used by a practitioner or medical assistant.

Further, in order to ensure that when the chronic kidney disease management platform receives a drug purchase request triggered by a terminal device of a user, corresponding prescription information can be searched from a prescription management library of a prescription sharing platform in communication connection with the chronic kidney disease management platform, the prescription information needs to be added into the prescription management library before the substep (2) is executed.

The manner of adding the prescription information to the prescription management library is as follows:

First, a prescription uploaded by a terminal device of a medical staff or a terminal device of the patient needs to be received, and the prescription information is extracted from the prescription.

It should be noted that, in practical application, the received prescription may be an electronic prescription or an electronic picture of a traditional paper prescription. Therefore, when extracting the prescription information from the prescription, a corresponding extraction mode is selected according to the type of the prescription to extract the prescription information, including:

If the prescription is an electronic prescription, traversing characters in the electronic prescription to obtain prescription information; if the prescription is an electronic picture of a paper prescription, identifying and judging characters in the electronic picture based on a high-order neural network algorithm to obtain the prescription information.

Specifically, before the identification and judgment are performed on the characters in the electronic picture based on the higher-order neural network algorithm to obtain the prescription information, in order to ensure that useful contents can be quickly and accurately extracted, and further the prescription information is obtained, the characteristic information, such as stroke characteristic information, communication characteristic information, closed area characteristic information and the like, of each character in the electronic picture needs to be extracted by using an edge detection method, and the method is not limited one by one.

It should be understood that, due to the use of edge detection, the contrast of the electronic picture is adjusted first when extracting the feature information, because the higher the contrast, the clearer and more accurate the extracted feature.

Therefore, the accuracy of the prescription information can be effectively ensured when the prescription information is acquired, and after the characteristic information of each character in the electronic picture is extracted by utilizing an edge detection method, the character in the electronic picture is identified and judged based on a higher-order neural network algorithm, so that the prescription information is obtained by the following specific steps: calculating a weight value corresponding to the characteristic information of each character according to the characteristic information of each character and a weight coefficient preset for the characteristic information of each character based on the high-order neural network algorithm; and comparing the weight value corresponding to the characteristic information of each character with a threshold value, and outputting the characters with the weight values larger than the threshold value according to an initial sequence to obtain the prescription information.

It should be noted that the above threshold may be set by those skilled in the art according to need, and is not limited herein.

In addition, since the use of the edge detection method is mature, the feature information of each character in the electronic picture is extracted by the edge detection method, which is not described here again, and the person skilled in the art can realize the method by looking up the related data.

It should be noted that, the above is only one way to add the prescription information to the prescription management library, and the technical solution of the present invention is not limited, and in a specific implementation, those skilled in the art may set the method as required, which is not limited herein.

(3) And searching a patient file corresponding to the first identification information in a pre-established patient archive according to the first identification information, and extracting prescription drug delivery information and common prescription drugs of the object from the searched patient file.

Specifically, the patient profile may include the patient's name, identification number, social or medical insurance card number, contact phone, drug delivery address, chronic name of the patient, general drug, etc.

Accordingly, the prescription drug delivery information may include the patient's name, contact phone, delivery address, etc.

It should be noted that the foregoing is merely illustrative, and the technical solution of the present invention does not constitute any modern, and in a specific implementation, those skilled in the art may set the implementation as required, which is not limited herein.

(4) And when the matching degree of the prescription medicine and the common prescription medicine is larger than a threshold value, sending the prescription medicine and the prescription medicine distribution information to a medical center pharmacy so that the medical center pharmacy can distribute the prescription medicine for the object according to the prescription medicine distribution information.

Specifically, the threshold may be reasonably set by a plurality of factors such as the illness state and the dosage of the patient, the medication ingredients, the effect of the replaced medicine, the price and the like, for example, the threshold is set to be more than 85%, which is considered to be reasonable.

Accordingly, the matching manner may be determined by comparing the components, effects, etc. of the medicines.

In addition, the medical center pharmacy can be a mechanism for managing and coordinating organizations by a medicine welfare management mechanism, so that the insurance mechanism, a pharmacy, a hospital, a pharmacy and other mechanisms can be connected together, effective management of medical expenses is realized, the purposes of saving social medical insurance expenditure, increasing medicine benefits and controlling the increase of medicine price are achieved, and common paramedics can better enjoy basic medical services.

Because of adopting the medicine welfare management, the patient can directly purchase prescription medicines from the pharmacy, thereby saving each circulation link in the middle of the medicines, reducing the medicine price and relieving the burden of the patient.

In addition, in practical application, in order to further reduce the expense of seeking medical advice of the patient, an integral system can be set, so that the patient purchasing the medicine can use integral to replace the medicine or replace the red packet, and the like, and the aim of reducing the expense of the patient can be achieved in a long term.

Further, in order to prevent others from stealing the medical insurance account of the patient, the medical insurance fund of the patient needs to be maliciously used to purchase the medicine, before the sub-step (4) is executed, a payment mode needs to be extracted from the medicine purchase request, whether the payment mode is to pay by using the medical insurance card is determined, if the payment mode is to pay by using the medical insurance card, the identity of the object initiating the medicine purchase request is verified, that is, whether the user initiating the medicine purchase request is the holder of the medical insurance card is required to be verified, and whether the purchased medicine is the medicine which the holder of the medical insurance card needs to take is determined, if the medicine purchase request is valid, the sub-step (4) is executed.

Regarding to the authentication of the object initiating the medicine purchase request, the operation of determining that the medicine purchase request is valid may be specifically implemented by the following steps:

(01) First biometric information of the subject using the medical insurance card and an identification number of the medical insurance card are acquired.

It should be understood that the above-mentioned first biometric information may be facial features, fingerprint feature information, iris feature information, voiceprint feature information, etc., which are not listed here, nor are any limitations imposed thereon, and those skilled in the art may set the first biometric information to be obtained as needed.

Correspondingly, when the first biological characteristic information is different, the selected equipment for acquiring the first biological characteristic information is also different, for example, when the first biological characteristic information is facial characteristic information and iris characteristic information, the equipment for acquiring the first biological characteristic information needs to adopt image acquisition equipment; when the first biological characteristic information is fingerprint characteristic information, acquiring the first biological characteristic information by adopting a fingerprint module; when the first biometric information is voiceprint feature information, a voice device is required to obtain the first biometric information.

In addition, the medical insurance card, namely the social medical insurance card, is also called as a medical insurance card. The current social security card can integrate social security and medical security. Correspondingly, the identification number is specifically ID (Identification) number for identifying the uniqueness of the medical insurance card or the identification card number of the participant.

In order to facilitate understanding of a specific implementation manner of acquiring the first biometric information of the user using the medical insurance card, the following will specifically describe taking the first biometric information as the first facial feature information of the user using the medical insurance card, and the second biometric information as the second facial feature information of the holder of the medical insurance card as an example.

(02) And acquiring second biological characteristic information of the holder of the medical insurance card corresponding to the identification number from a social security platform according to the identification number.

Specifically, the holder of the medical insurance card refers to the actual participant corresponding to the medical insurance card. Therefore, according to the identification number, the second biological characteristic information of the holder of the medical insurance card corresponding to the identification number is obtained from the social security platform, namely the face characteristic, the fingerprint characteristic information, the iris characteristic information, the voiceprint characteristic information and the like which are input when the medical insurance card is handled by the insurer.

(03) Matching the first biometric information with the second biometric information.

Taking the first biological characteristic information as the first face characteristic information and the second biological characteristic information as the second face characteristic information as an example, the specific operation of matching the first face characteristic information with the second face characteristic information in the specific implementation is approximately as follows:

Firstly, the first face feature information and the second face feature information are matched one by one, and cosine similarity between the first face feature information and the second face feature information is determined.

And then comparing the cosine similarity with a preset similarity threshold.

It should be noted that the above is only a specific comparison mode, and the technical solution of the present invention is not limited in any way, and in a specific implementation, a person skilled in the art may select an appropriate comparison mode according to needs, which is not limited herein.

(04) And if the first biological characteristic information is matched with the second biological characteristic information, determining that the medicine purchase request is valid.

In addition, it should be noted that, in practical application, since the patient who needs to take the purchased medicine may involve a crowd inconvenient for operating the terminal device to purchase the medicine online, such as children and old people, in order to ensure that such crowd can also enjoy the medicine purchasing and dispensing modes provided in the embodiment, after comparing the second biometric information with the first biometric information, the method may further include:

If the two biological characteristic information is not matched with the first biological characteristic information, acquiring third biological characteristic information of a guardian reserved for a holder of the medical insurance card;

Comparing the first biological characteristic information with the third biological characteristic information, and if the first biological characteristic information is matched with the third biological characteristic information, determining that the medicine purchasing request is effective; and if the first biological characteristic information is not matched with the third biological characteristic information, determining that the medicine purchase request is invalid.

It should be noted that the foregoing is merely illustrative, and the technical solution of the present invention is not limited in any way, and in a specific implementation, one skilled in the art may set the verification rule and the verification manner according to the need, which is not limited herein.

According to the chronic kidney disease supervision method based on big data, after the chronic kidney disease management scheme is issued to the terminal equipment of the object and the chronic kidney disease management scheme is issued to the terminal equipment of medical staff, if a medicine purchasing request triggered by the terminal equipment of the object is received, the first identification information for identifying the object is extracted from the medicine purchasing request, prescription information corresponding to the first identification information is searched in a pre-established prescription management library according to a pre-stored mapping relation table, and a patient archive corresponding to the first identification information is searched in a pre-established patient archive, so that a medical center pharmacy can distribute prescription medicines prescribed in the prescription information for the object according to prescription medicine distribution information in the patient archive, the object can take the prescription medicines required by the patient at home, the hospital does not need to be queued in the hospital, and the flow rate and time spent on taking medicines of the hospital are effectively relieved.

In addition, according to the chronic kidney disease supervision method based on big data, before prescription information and prescription medicine distribution information aiming at a current medicine purchase request are sent to a medical center pharmacy, the prescription medicine in the prescription information is compared with common prescription medicines of patients, and when the matching degree of the prescription medicine to be purchased at present and the common prescription medicine is larger than a preset threshold value, the medical center pharmacy is informed to arrange distribution of the prescription medicines for the patients, so that the situation that the patients are provided with unsuitable prescription medicines due to errors of medical staff is effectively avoided, and the patients can better enjoy basic medical services.

In addition, the embodiment of the invention also provides a storage medium, wherein the storage medium is stored with a chronic kidney disease supervision program based on big data, and the chronic kidney disease supervision program based on big data realizes the steps of the chronic kidney disease supervision method based on big data when being executed by a processor.

Referring to fig. 4, fig. 4 is a block diagram showing the structure of a first embodiment of the chronic kidney disease monitor apparatus based on big data according to the present invention.

As shown in fig. 4, the chronic kidney disease supervision apparatus based on big data according to the embodiment of the present invention includes: the physical status data acquisition module 4001, the chronic kidney disease subject screening module 4002, the chronic kidney disease stage determination module 4003, the chronic kidney disease management scheme formulation module 4004, and the chronic kidney disease management scheme transmission module 4005.

The physical condition data acquisition module 4001 is configured to acquire physical condition data of each object to be screened in the crowd to be screened.

The chronic kidney disease object screening module 4002 is configured to analyze the physical status data of each object to be screened according to a pre-stored big data analysis model, and screen out an object suffering from chronic kidney disease.

The chronic kidney disease stage determining module 4003 is configured to determine a chronic kidney disease stage corresponding to the subject according to a pre-stored chronic kidney disease stage standard and physical status data of the subject.

A chronic kidney disease management scheme formulation module 4004 for formulating a chronic kidney disease management scheme for the subject based on the physical status data of the subject and the chronic kidney disease stage corresponding to the subject.

Further, for convenience of explanation, the risk level is roughly divided into three levels in this embodiment, specifically:

And if the chronic kidney disease stage corresponding to the subject is the kidney function voyage period, determining the risk level facing the subject as a second risk level.

And if the chronic kidney disease stage corresponding to the subject is the kidney failure stage, determining that the risk level facing the subject is a third risk level.

And, the risk level of the second risk level is higher than the risk level of the first risk level, and the risk level of the third risk level is higher than the risk level of the second risk level and the risk level of the first risk level.

It should be noted that the foregoing is only a specific implementation manner, and the technical solution of the present invention is not limited to the specific implementation manner, and in the specific implementation manner, a person skilled in the art may set appropriate reference conditions according to needs, which is not limited herein.

The chronic kidney disease management scheme sending module 4005 is configured to issue the chronic kidney disease management scheme to a terminal device of the subject, so that the subject diagnoses according to the chronic kidney disease management scheme, and/or issue the chronic kidney disease management scheme to a terminal device of a medical staff, so that the medical staff manages the subject according to the chronic kidney disease management scheme.

In addition, it should be noted that, in practical application, in order to ensure that the chronic kidney disease object screening module 4002 can analyze the physical status data of each object to be screened according to a pre-stored big data analysis model, the big data analysis model needs to be built in advance to screen out the object suffering from chronic kidney disease. Thus, the chronic kidney disease supervision apparatus based on big data provided in the present embodiment may further include: and a big data analysis model construction module.

Specifically, the big data analysis model construction module is used for constructing the big data analysis model according to a pre-stored international disease classification coding set and sample data.

Further, in order to briefly describe the training process, a series of processes may be performed on the sample data, for example, a biased estimation process is performed on the sample data based on a compression estimation method, to obtain a regression model, and then training is performed on the regression model according to the international disease classification coding set and the sample data, to obtain the big data analysis model.

According to the chronic kidney disease monitoring device based on big data, the physical state data of various objects to be screened are analyzed according to the pre-built big data analysis model, objects with chronic kidney disease are screened, then a chronic kidney disease management scheme specially aiming at the objects is formulated according to the physical state data of the objects and the corresponding chronic kidney disease stage, so that patients can be treated according to the chronic kidney disease management scheme suitable for the patients, potential chronic kidney disease patients can be screened as early as possible, reasonable management scheme is given to the potential chronic kidney disease patients in time, the development of the chronic kidney disease is prevented as far as possible, and the requirements of reducing the growth speed of the number of the chronic kidney disease patients and relieving the burden of medical insurance funds are met.

It should be noted that the above-described working procedure is merely illustrative, and does not limit the scope of the present invention, and in practical application, a person skilled in the art may select part or all of them according to actual needs to achieve the purpose of the embodiment, which is not limited herein.

In addition, technical details not described in detail in this embodiment may refer to the chronic kidney disease monitoring method based on big data provided in any embodiment of the present invention, which is not described herein.

Based on the first embodiment of the chronic kidney disease monitoring device based on big data, a second embodiment of the chronic kidney disease monitoring device based on big data is provided.

In a specific implementation, after the chronic kidney disease management scheme is issued to the terminal equipment of the subject and the chronic kidney disease management scheme is issued to the terminal equipment of the medical staff, a medicine purchase request triggered by the terminal equipment of the subject can be received, and prescription medicines can be distributed to the subject. Therefore, in this embodiment, the chronic kidney disease monitoring device based on big data further includes a first extraction module, a prescription information searching module, a patient file searching module, a second extraction module, and a delivery information issuing module.

The first extraction module is used for receiving the medicine purchase request and extracting first identification information for identifying the object from the medicine purchase request.

And the prescription information searching module is used for searching prescription information corresponding to the first identification information in a pre-established prescription management library according to a pre-stored mapping relation table.

The prescription information includes at least the prescription drug and second identification information identifying the subject.

In addition, the prescription drug is specifically prescribed for the subject by a doctor according to the chronic kidney disease management scheme.

And the patient archive searching module is used for searching the patient archive corresponding to the first identification information in a pre-established patient archive according to the first identification information.

And the second extraction module is used for extracting prescription drug delivery information and common prescription drugs of the object from the searched patient file.

And the delivery information issuing module is used for sending the prescription medicine and the prescription medicine delivery information to a medical center pharmacy when the matching degree of the prescription medicine and the common prescription medicine is larger than a threshold value, so that the medical center pharmacy can deliver the prescription medicine to the object according to the prescription medicine delivery information.

In addition, in a specific implementation, before the prescription drug and the prescription drug delivery information are sent to a pharmacy of a medical center, a payment mode can be extracted from the drug purchase request, if the payment mode is payment using a medical insurance card, an identity of an object initiating the drug purchase request is verified, and the delivery information delivery module is notified to deliver relevant delivery information when the drug purchase request is determined to be valid.

Thus, the chronic kidney disease supervision apparatus based on big data provided in the present embodiment may further include an authentication module. And the identity verification module performs identity verification on the object, and determines that the medicine purchase request is effective.

Specifically, when the identity verification module performs identity verification on the object initiating the medicine purchase request, the medicine purchase request is determined to be valid mainly by the following manner:

first, first biometric information of the subject using the medical insurance card and an identification number of the medical insurance card are acquired.

Then, according to the identification number, second biometric information of the holder of the medical insurance card corresponding to the identification number is acquired from a social security platform.

Next, the first biometric information is matched with the second biometric information.

And finally, if the first biological characteristic information is matched with the second biological characteristic information, determining that the medicine purchase request is valid.

It should be noted that the foregoing is only a specific implementation manner, and the technical solution of the present invention is not limited in any way, and in a specific implementation manner, those skilled in the art may set the implementation manner as required, and the implementation manner is not limited herein.

As described above, it is easy to find that, in the chronic kidney disease monitoring apparatus based on big data provided in this embodiment, after the chronic kidney disease management scheme is issued to the terminal device of the subject and the chronic kidney disease management scheme is issued to the terminal device of the medical staff, if a drug purchase request triggered by the terminal device of the subject is received, by extracting first identification information for identifying the subject from the drug purchase request, according to a pre-stored mapping relation table, prescription information corresponding to the first identification information is searched in a pre-established prescription management library, and a patient archive corresponding to the first identification information is searched in a pre-established patient archive, so that a medical center pharmacy can distribute prescription drugs prescribed in the prescription information for the subject according to prescription drug distribution information in the patient archive, so that the subject can take the prescription drugs required in person at home without queuing in the hospital, thereby effectively relieving the flow rate of people in the hospital and the time spent on taking drugs.

In addition, the chronic kidney disease supervision device based on big data provided by the invention is used for informing the medical center pharmacy to arrange the distribution of the prescription medicine for the patient when the matching degree of the prescription medicine to be purchased at present and the common prescription medicine of the patient is larger than the preset threshold value by comparing the prescription medicine in the prescription information with the common prescription medicine of the patient before the prescription information and the prescription medicine distribution information aiming at the current medicine purchase request are sent to the medical center pharmacy, so that the situation that the patient is provided with unsuitable prescription medicine due to the errors of medical staff is effectively avoided, and the patient can better enjoy basic medical services.

Furthermore, it should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.

The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.

From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. Read Only Memory)/RAM, magnetic disk, optical disk) and including several instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the method according to the embodiments of the present invention.

The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.

Claims

1. A method for supervising chronic kidney disease based on big data, the method comprising:

Acquiring physical condition data of each object to be screened in the people to be screened, wherein the people to be screened are people suffering from diabetes, hypertension, hyperuricemia, gout and family diseases with kidney diseases or people with ages larger than preset age values;

Analyzing the physical condition data of each object to be screened according to a pre-stored big data analysis model, and screening out objects with chronic kidney diseases, wherein the big data analysis model is a model constructed according to a pre-stored international disease classification coding set and sample data;

Issuing the chronic kidney disease management scheme to terminal equipment of the object so that the object diagnoses according to the chronic kidney disease management scheme, and/or issuing the chronic kidney disease management scheme to terminal equipment of medical staff so that the medical staff manages the object according to the chronic kidney disease management scheme;

The step of formulating a chronic kidney disease management scheme for the subject according to the physical condition data of the subject and the chronic kidney disease stage corresponding to the subject, comprising:

wherein the risk level of the second risk level is higher than the risk level of the first risk level, and the risk level of the third risk level is higher than the risk level of the second risk level and the risk level of the first risk level;

Formulating a chronic kidney disease management regimen for the subject according to the subject's physical condition data and the risk level;

After the issuing of the chronic kidney disease management scheme to the terminal device of the subject and the issuing of the chronic kidney disease management scheme to the terminal device of the medical staff, the method further comprises:

when the matching degree of the prescription drug and the common prescription drug is larger than a threshold value, sending the prescription drug and the prescription drug distribution information to a medical center pharmacy so that the medical center pharmacy can distribute the prescription drug for the object according to the prescription drug distribution information;

Before the prescription drug and the prescription drug delivery information are sent to a medical center pharmacy, the method further comprises:

Matching the first biometric information with the second biometric information;

2. The method of claim 1, wherein the formulating a chronic kidney disease management regimen for the subject based on the subject's physical condition data and the subject's corresponding chronic kidney disease stage comprises:

3. The method according to any one of claims 1 to 2, wherein the analyzing the physical condition data of each subject to be screened according to a pre-stored big data analysis model, the method further comprising, before screening out the subject suffering from chronic kidney disease:

4. The method of claim 3, wherein constructing the big data analysis model from the pre-stored international disease classification code sets and sample data comprises:

5. A chronic kidney disease supervision apparatus based on big data, the apparatus comprising:

The physical condition data acquisition module is used for acquiring physical condition data of each object to be screened in the group to be screened, wherein the group to be screened is a group with diabetes, hypertension, hyperuricemia, gout and family diseases or a group with age greater than a preset age value;

The chronic kidney disease object screening module is used for analyzing the physical condition data of each object to be screened according to a pre-stored big data analysis model, and screening out objects with chronic kidney disease, wherein the big data analysis model is a model constructed according to a pre-stored international disease classification coding set and sample data;

A chronic kidney disease management scheme sending module, configured to send the chronic kidney disease management scheme to a terminal device of the subject, so that the subject diagnoses according to the chronic kidney disease management scheme, and/or send the chronic kidney disease management scheme to a terminal device of a medical staff, so that the medical staff manages the subject according to the chronic kidney disease management scheme;

the chronic kidney disease management scheme making module is further configured to determine that a risk level faced by the subject is a first risk level if a chronic kidney disease stage corresponding to the subject is an inflammatory reaction stage or a renal function tasting stage; if the chronic kidney disease stage corresponding to the subject is a kidney function voyage period, determining that the risk level facing the subject is a second risk level; if the chronic kidney disease stage corresponding to the subject is a kidney failure stage, determining that the risk level facing the subject is a third risk level; wherein the risk level of the second risk level is higher than the risk level of the first risk level, and the risk level of the third risk level is higher than the risk level of the second risk level and the risk level of the first risk level; formulating a chronic kidney disease management regimen for the subject according to the subject's physical condition data and the risk level;

The chronic kidney disease management scheme sending module is further used for receiving a medicine purchasing request, and extracting first identification information for identifying the object from the medicine purchasing request; searching prescription information corresponding to the first identification information in a pre-established prescription management library according to a pre-stored mapping relation table, wherein the prescription information at least comprises prescription drugs and second identification information for identifying the object, and the mapping relation is a corresponding relation between the first identification information and the second identification information; searching a patient file corresponding to the first identification information in a pre-established patient archive according to the first identification information, and extracting prescription drug delivery information and common prescription drugs of the object from the searched patient file; when the matching degree of the prescription drug and the common prescription drug is larger than a threshold value, sending the prescription drug and the prescription drug distribution information to a medical center pharmacy so that the medical center pharmacy can distribute the prescription drug to the object according to the prescription drug distribution information, wherein the prescription drug is issued to the object by a doctor according to the chronic kidney disease management scheme;

The chronic kidney disease management scheme sending module is further used for extracting a payment mode from the medicine purchasing request, and if the payment mode is payment by using a medical insurance card, acquiring first biological characteristic information of the object using the medical insurance card and an identification number of the medical insurance card; acquiring second biological characteristic information of the holder of the medical insurance card corresponding to the identification number from a social security platform according to the identification number; matching the first biometric information with the second biometric information; and if the first biological characteristic information is matched with the second biological characteristic information, determining that the medicine purchase request is valid.

6. A big data based chronic kidney disease supervision apparatus, the apparatus comprising: a memory, a processor, and a big data based chronic kidney disease monitor stored on the memory and executable on the processor, the big data based chronic kidney disease monitor configured to implement the steps of the big data based chronic kidney disease monitor method of any of claims 1 to 4.

7. A storage medium having stored thereon a big data based chronic kidney disease monitor, which when executed by a processor, implements the steps of the big data based chronic kidney disease monitor method of any of claims 1 to 4.