CN113140270A - Data analysis method, device and equipment and computer storage medium - Google Patents
Data analysis method, device and equipment and computer storage medium Download PDFInfo
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Abstract
The invention discloses a data analysis method, a data analysis device, data analysis equipment and a computer storage medium. The method comprises the following steps: acquiring CRF data of a first user and CRF data of a second user from a pre-configured CRF database of a case report form, wherein the CRF data comprises first physiological characteristic data; determining physiological characteristic distinguishing information between the first user and the second user according to the CRF data of the first user and the CRF data of the second user; wherein the first user has accepted the designated glycemic control therapy. According to the embodiment of the invention, the CRF data of the first user who receives the appointed blood sugar control treatment is compared with the CRF data of the second user to determine the physiological characteristic distinguishing information between the first user and the second user, so that the relationship between the blood sugar value of the patient and other complications can be determined to be analyzed quickly and accurately.
Description
Technical Field
The invention belongs to the field of information processing, and particularly relates to a data analysis method, a data analysis device, data analysis equipment and a computer storage medium.
Background
In the last 20 years, the incidence of diabetes in China has increased rapidly, and epidemiological survey data show that the prevalence rate of diabetes in cities and towns in China is 11.6%, but the awareness rate and the diagnosis rate are low, wherein the undiagnosed rate reaches 56%, and the diabetes prevention and treatment still faces a severe situation.
With the advance of modern medicine, diabetes treatment is no longer a pure pursuit for lowering blood sugar, and the long-term benefits of preventing and treating complications should be paid attention to. Among the common complications of diabetes, cardiovascular complications are especially vigilant. Therefore, in order to ensure the physiological health of the patient, it is important to analyze the relationship between the blood glucose information of the patient and the complications of the patient.
Therefore, how to quickly and accurately analyze the relationship between the blood glucose level of the patient and other complications becomes a problem to be solved.
Disclosure of Invention
Embodiments of the present invention provide a data analysis method, apparatus, device, and computer storage medium, which can quickly and accurately analyze a relationship between a blood glucose level of a patient and other complications.
In a first aspect, a data analysis method is provided, which includes: acquiring CRF data of a first user and CRF data of a second user from a pre-configured CRF database of a case report form, wherein the CRF data comprises first physiological characteristic data; determining physiological characteristic distinguishing information between the first user and the second user according to the CRF data of the first user and the CRF data of the second user; wherein the first user has accepted the designated glycemic control therapy.
In one possible implementation, the second user has not received the specified glycemic control therapy.
In one possible implementation, the second user is located in a second geographic area that is different from the first geographic area in which the first user is located.
In one possible implementation, prior to obtaining CRF data for the first user and CRF data for the second user from the preconfigured case report form CRF database, the method further comprises: configuring CRF data of a plurality of first users based on second physiological characteristic data filled by a plurality of filling persons; wherein the filler comprises at least one of the following: a first user, a nurse, a doctor, a department master, an expert, and a first user's family.
In one possible implementation, in the case where the filling out is a first user, a nurse and a doctor, configuring CRF data of the first user, including: acquiring first CRF data based on a medical case system; filling first characteristic data by a nurse based on the first CRF data to obtain second CRF data; filling second characteristic data by the first user based on the second CRF data to obtain third CRF data; filling third characteristic data by the nurse based on the third CRF data to obtain fourth CRF data; and filling fourth characteristic data by the doctor based on the fourth CRF data to obtain CRF data of the first user.
In one possible implementation, the first CRF data, the second CRF data, the third CRF data and the fourth CRF data are encrypted according to the identification information of any writer, and the first encrypted CRF data, the second encrypted CRF data, the third encrypted CRF data and the fourth CRF data are respectively determined; and storing the first encrypted CRF data, the second encrypted CRF data, the third encrypted CRF data and the fourth encrypted CRF data into a block chain according to the first filling time information of the first CRF data, the second filling time information of the second CRF data, the third filling time information of the third CRF data and the fourth filling time information of the fourth CRF data.
In one possible implementation, a data calling application sent by an access user is received, wherein the data calling application comprises account information of the access user; verifying whether account information of an access user is consistent with preset account information; under the condition that the account information of the access user is consistent with the preset account information, acquiring target CRF data which are applied and called by the access user from a CRF database; and sending the target CRF data to a terminal corresponding to the access user.
In a second aspect, there is provided a data analysis apparatus, the apparatus comprising: the acquisition module is used for acquiring CRF data of a first user and CRF data of a second user from a pre-configured CRF database of a case report table; the analysis module is used for determining physiological characteristic distinguishing information of the first user and the second user according to the CRF data of the first user and the CRF data of the second user; wherein the first user has accepted the designated glycemic control therapy.
In a third aspect, a computing device is provided, the device comprising: a processor and a memory storing computer program instructions; the processor, when executing the computer program instructions, implements a method as in any possible implementation of the first aspect.
In a fourth aspect, there is provided a computer storage medium having computer program instructions stored thereon that, when executed by a processor, implement a method as in any one of the possible implementations of the first aspect.
Based on the data analysis method, the data analysis device, the data analysis equipment and the computer storage medium provided by the embodiment of the invention, the CRF data of the first user who receives the appointed blood glucose control treatment and the CRF data of the second user are compared to determine the physiological characteristic distinguishing information between the first user and the second user, so that the relationship between the blood glucose value of the patient and other complications can be determined to be analyzed quickly and accurately.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required to be used in the embodiments of the present invention will be briefly described below, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart of a data analysis method according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a data analysis apparatus according to an embodiment of the present invention;
fig. 3 is a schematic diagram of an exemplary hardware architecture provided by an embodiment of the present invention.
Detailed Description
Features and exemplary embodiments of various aspects of the present invention will be described in detail below, and in order to make objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not to be construed as limiting the invention. It will be apparent to one skilled in the art that the present invention may be practiced without some of these specific details. The following description of the embodiments is merely intended to provide a better understanding of the present invention by illustrating examples of the present invention.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus 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 apparatus. Without further limitation, an element defined by the phrase "comprising … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
In order to quickly and accurately analyze the relationship between the blood glucose value of the patient and other complications, the embodiment of the invention provides a data analysis method, and the data analysis method provided by the embodiment of the invention is described below.
Fig. 1 is a schematic flow chart of a data analysis method according to an embodiment of the present invention. As shown in fig. 1, the execution subject of the method is a server, and the method may include S101-S102, which are specifically as follows:
s101, CRF data of a first user and CRF data of a second user are obtained from a pre-configured Case Report Form (CRF) database, wherein the CRF data comprise first physiological characteristic data.
The case report refers to a file designed according to the protocol of the test to record the data of each subject during the test. The CRF data of the first subscriber and the CRF data of the second subscriber are obtained here for the purpose of subsequent comparison of distinctive features between the CRF data of the first subscriber and the CRF data of the second subscriber.
As an implementation manner of the present application, in order to improve the data validity and reliability of the CRF data of the first user and the CRF data of the second user, before S101, the following steps may be further included:
configuring CRF data of a plurality of first users based on second physiological characteristic data filled by a plurality of filling persons; wherein the filler comprises at least one of the following: a first user, a nurse, a doctor, a department master, an expert, and a first user's family.
In the case that the filler is a first user, a nurse and a doctor, the step of configuring the CRF data of the first user may specifically include: acquiring first CRF data based on a medical case system; filling first characteristic data by a nurse based on the first CRF data to obtain second CRF data; filling second characteristic data by the first user based on the second CRF data to obtain third CRF data; filling third characteristic data by the nurse based on the third CRF data to obtain fourth CRF data; and filling fourth characteristic data by the doctor based on the fourth CRF data to obtain CRF data of the first user.
First, acquiring first CRF data based on a medical case system comprises: basic data, diabetes diagnosis history, physical examination and test results.
Wherein, the above mentioned basic data include: name, identification card, date of birth, gender, cell phone number, place of residence, and cultural level.
Wherein the above mentioned diagnostic history of diabetes includes: whether diabetes, time to diagnose diabetes, family history of diabetes, emergency contact telephone number, and type of diabetes.
Wherein the aforementioned physical examination includes: height, weight/height (BMI), blood pressure, abdominal type, and sleep quality.
Wherein the above mentioned test results include: blood glucose recording, self-monitoring blood glucose, glycated hemoglobin, pulse, plasma cholesterol, triglycerides, high density lipoprotein, low density lipoprotein, whether smoking, chronic complications, acute complications, and foot assessment. The foot assessment is filled in with a history of diabetes. The foot assessment includes: appearance 1 (skin tone), appearance 2 (deformity), appearance 3 (dry crack), appearance 4 (onychomycosis), subjective foot symptoms (pain/numbness/prickling/others), dorsal artery pulsation, daily foot examination, daily foot cleaning.
Secondly, the filling of the first characteristic data by the nurse based on the first CRF data comprises the following steps: adverse events, severe adverse events.
Wherein the adverse events mentioned above include: number of hypoglycemic episodes, number of hyperglycemic episodes, and other events (vomiting, dizziness, nausea, diarrhea).
Wherein the serious adverse events referred to above include: death, myocardial infarction, cerebral infarction and shock.
Thirdly, filling out second characteristic data by the first user based on the second CRF data comprises the following steps: the combination of the disease and medication, the cognitive level of the diabetes treatment and the presence or absence of a predetermined condition.
Wherein, the related complicated diseases and medication conditions comprise: disease name, time of diagnosis, and current medication.
Wherein the cognitive levels involved in the treatment of diabetes include: diet therapy understanding, exercise therapy understanding, medication understanding, blood glucose monitoring understanding, and diabetes and complications education understanding.
Wherein, the existence of the preset condition comprises the following steps: dizziness, headache and palpitation.
And fourthly, filling third characteristic data by the nurse based on the third CRF data to obtain fourth CRF data, wherein the nurse finishes and confirms the third CRF data obtained after the filling of the first user is finished, namely, the CRF data is perfected according to the information provided by the patient.
Fifthly, the doctor fills in fourth feature data based on the fourth CRF data, and the fourth feature data comprise: fundus examination, neuropathy screening and arterial ultrasound screening. Wherein, the artery ultrasonic screening comprises a carotid artery ultrasonic examination, a coronary artery examination and a lower limb artery examination.
In one embodiment, the first CRF data, the second CRF data, the third CRF data and the fourth CRF data are encrypted according to the identification information of any writer, and the first encrypted CRF data, the second encrypted CRF data, the third encrypted CRF data and the fourth CRF data are respectively determined; and storing the first encrypted CRF data, the second encrypted CRF data, the third encrypted CRF data and the fourth encrypted CRF data into a block chain according to the first filling time information of the first CRF data, the second filling time information of the second CRF data, the third filling time information of the third CRF data and the fourth filling time information of the fourth CRF data.
The CRF data is encrypted according to the identity identification information of any writer, and the identity identification information of the modifier is added to the first CRF data, the second CRF data, the third CRF data and the fourth CRF data respectively, so that the CRF data is stored by using a block chain, a modification record is reserved after the CRF data is modified each time, modification time and a modifier can be found by tracing to the root of historical modification conditions, tampering can be effectively prevented, and the credibility of the CRF data can be improved.
In one embodiment, a data calling application sent by an access user is received, wherein the data calling application comprises account information of the access user; verifying whether account information of an access user is consistent with preset account information; under the condition that the account information of the access user is consistent with the preset account information, acquiring target CRF data which are applied and called by the access user from a CRF database; and sending the target CRF data to a terminal corresponding to the access user. In this case, it is possible to effectively prevent the CRF data from being tampered with and improve the security of the CRF data.
S102, determining physiological characteristic distinguishing information between a first user and a second user according to CRF data of the first user and CRF data of the second user; wherein the first user has accepted the designated glycemic control therapy.
By analyzing CRF data and physiological characteristic distinguishing information of a first user who has received a prescribed blood glucose control treatment, it is possible to determine a rapid and accurate analysis of the relationship between the blood glucose level of the patient and other complications. For example, the time of onset of a disease in his disease history is closely related to the fluctuation of blood glucose level for user X, so that the disease of user X can be analyzed based on CRF data. And in the event that the second user has not received the designated glycemic control therapy, the effectiveness of the glycemic control therapy can be determined by analyzing the CRF data for the first user who received the designated glycemic control therapy and the CRF data for the second user who has not received the designated glycemic control therapy.
In one embodiment, the second user has not received the specified glycemic control therapy.
In the event that the second user has not received the designated glycemic control therapy, the effectiveness of the glycemic control therapy can be determined by analyzing the CRF data of the first user who received the designated glycemic control therapy and the CRF data of the second user who has not received the designated glycemic control therapy. For example, user a has been receiving glycemic control therapy for two years to date, and user B has never received glycemic control therapy, by analyzing CRF data of user a and user B, the difference in physiological characteristic data of user a and user B can be determined to gauge the effect and effectiveness of glycemic control therapy.
In another embodiment, the second user is located in a second geographic area different from the first geographic area in which the first user is located.
For example, the second geographic area where the user a is located is a southern city in china, the second geographic area where the user B is located is a northern city in china, and by analyzing CRF data of the user a and the user B, the difference of physiological characteristic data of the user a and the user B can be determined, so as to estimate the difference of the geographic areas to the blood sugar of the user, and further determine whether the difference is caused by eating habits or regional water quality conditions, and the difference is caused by the blood sugar conditions of people in different areas.
In yet another embodiment, physiological characteristic distinguishing information for a first user at different time periods is determined based on first historical CRF data for the first user and second historical CRF data for the first user, wherein the first user has received a specified glycemic control therapy. Thus, the effect and the effect of the blood sugar control treatment can be evaluated by analyzing the change of the physical condition of the user after receiving the blood sugar control treatment for a period of time.
Based on the data analysis method provided by the embodiment of the invention, the CRF data of the first user who receives the appointed blood glucose control treatment and the CRF data of the second user are compared to determine the physiological characteristic distinguishing information between the first user and the second user, so that the relationship between the blood glucose value of the patient and other complications can be determined to be analyzed quickly and accurately. And in the case that the second user has not received the designated blood glucose control treatment, the effect and effect of the blood glucose control treatment can be evaluated by analyzing the difference of the physiological characteristic data of the first user and the second user.
In addition, based on the information processing method, an embodiment of the present invention further provides a data analysis apparatus, which is described below with reference to fig. 2.
Fig. 2 is a schematic structural diagram of a data analysis apparatus according to an embodiment of the present invention, and as shown in fig. 2, the apparatus 200 may include:
an obtaining module 210, configured to obtain CRF data of the first user and CRF data of the second user from a pre-configured case report table CRF database.
As an example, the obtaining module 210 is further configured to configure CRF data of a plurality of first users based on the second physiological characteristic data filled by a plurality of filling persons; wherein the filler comprises at least one of the following: a first user, a nurse, a doctor, a department master, an expert, and a first user's family.
As one example, the acquisition module 210 is further configured to acquire first CRF data based on a medical case system; filling first characteristic data by a nurse based on the first CRF data to obtain second CRF data; filling second characteristic data by the first user based on the second CRF data to obtain third CRF data; filling third characteristic data by the nurse based on the third CRF data to obtain fourth CRF data; and filling fourth characteristic data by the doctor based on the fourth CRF data to obtain CRF data of the first user.
As an example, the obtaining module 210 is further configured to perform encryption processing on the first CRF data, the second CRF data, the third CRF data and the fourth CRF data according to the identification information of any writer, and determine first encrypted CRF data, second encrypted CRF data, third encrypted CRF data and encrypted fourth CRF data, respectively; and storing the first encrypted CRF data, the second encrypted CRF data, the third encrypted CRF data and the fourth encrypted CRF data into a block chain according to the first filling time information of the first CRF data, the second filling time information of the second CRF data, the third filling time information of the third CRF data and the fourth filling time information of the fourth CRF data.
As an example, the obtaining module 210 is further configured to receive a data call application sent by the access user, where the data call application includes account information of the access user; verifying whether account information of an access user is consistent with preset account information; under the condition that the account information of the access user is consistent with the preset account information, acquiring target CRF data which are applied and called by the access user from the CRF database; and sending the target CRF data to a terminal corresponding to the access user.
The analysis module 220 is used for determining physiological characteristic distinguishing information of the first user and the second user according to the CRF data of the first user and the CRF data of the second user; wherein the first user has accepted the designated glycemic control therapy.
Wherein the second user involved in embodiments of the present invention has not received the designated glycemic control therapy.
The second geographical area where the second user is located in the embodiment of the present invention is different from the first geographical area where the first user is located.
Each module of the data analysis apparatus provided in this embodiment may implement the method in the example shown in fig. 1, and for brevity, will not be described again here. Based on the data analysis device provided by the embodiment of the invention, the CRF data of the first user who receives the appointed blood glucose control treatment and the CRF data of the second user are compared to determine the physiological characteristic distinguishing information between the first user and the second user, so that the relationship between the blood glucose value of the patient and other complications can be determined to be analyzed quickly and accurately. And in the case that the second user has not received the designated blood glucose control treatment, the effect and effect of the blood glucose control treatment can be evaluated by analyzing the difference of the physiological characteristic data of the first user and the second user.
Fig. 3 is a schematic diagram of an exemplary hardware architecture provided by an embodiment of the present invention.
The processing device may include a processor 301 and a memory 302 in which computer program instructions are stored.
The processor 301 may include a Central Processing Unit (PU), an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits configured to implement an embodiment of the present invention.
The processor 301 implements the data analysis method in the example shown in fig. 1 described above by reading and executing computer program instructions stored in the memory 302.
In one example, the processing device may also include a communication interface 303 and a bus 310. As shown in fig. 3, the processor 301, the memory 302, and the communication interface 303 are connected via a bus 310 to complete communication therebetween.
The communication interface 303 is mainly used for implementing communication between modules, apparatuses, units and/or devices in the embodiment of the present invention.
The processing device may perform the method in an embodiment of the invention to implement the method described in connection with the example shown in fig. 1.
In addition, in combination with the methods in the above embodiments, the embodiments of the present invention may be implemented by providing a computer storage medium. The computer storage medium having computer program instructions stored thereon; the computer program instructions, when executed by a processor, implement any of the methods in the above embodiments.
It is to be understood that the invention is not limited to the specific arrangements and instrumentality described above and shown in the drawings. A detailed description of known methods is omitted herein for the sake of brevity. In the above embodiments, several specific steps are described and shown as examples. However, the method processes of the present invention are not limited to the specific steps described and illustrated, and those skilled in the art can make various changes, modifications and additions or change the order between the steps after comprehending the spirit of the present invention.
The functional blocks shown in the above-described structural block diagrams can be implemented in software, and the elements of the present invention are programs or code segments used to perform desired tasks. The program or code segments may be stored in a machine-readable medium or transmitted by a data signal carried in a carrier wave over a transmission medium or a communication link. A "machine-readable medium" may include any medium that can store or transfer information. Examples of a machine-readable medium include electronic circuits, semiconductor memory devices, ROM, flash memory, Erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, Radio Frequency (RF) links, and so forth. The code segments may be downloaded via computer networks such as the internet, intranet, etc.
It should also be noted that the exemplary embodiments mentioned in this patent describe some methods or systems based on a series of steps or devices. However, the present invention is not limited to the order of the above-described steps, that is, the steps may be performed in the order mentioned in the embodiments, may be performed in an order different from the order in the embodiments, or may be performed simultaneously.
As described above, only the specific embodiments of the present invention are provided, and it can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the system, the module and the unit described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again. It should be understood that the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive various equivalent modifications or substitutions within the technical scope of the present invention, and these modifications or substitutions should be covered within the scope of the present invention.
Claims (10)
1. A data analysis method, characterized in that the data analysis method comprises:
acquiring CRF data of a first user and CRF data of a second user from a pre-configured CRF database of a case report form, wherein the CRF data comprises first physiological characteristic data;
determining physiological characteristic distinguishing information between the first user and the second user according to the CRF data of the first user and the CRF data of the second user;
wherein the first user has accepted a specified glycemic control therapy.
2. The method of claim 1, wherein the second user has not received the specified glycemic control therapy.
3. The method of claim 1, wherein the second geographic region in which the second user is located is different from the first geographic region in which the first user is located.
4. The method of claim 1, wherein prior to said obtaining CRF data for a first user and CRF data for a second user from a preconfigured case report form CRF database, the method further comprises:
configuring CRF data of a plurality of the first users based on second physiological characteristic data filled by a plurality of filling persons;
wherein the filler comprises at least one of: the first user, a nurse, a doctor, a department master, an expert, and a family member of the first user.
5. The method of claim 4, wherein in the case where the filling out persons are a first user, a nurse and a doctor, configuring CRF data of the first user comprises:
acquiring first CRF data based on a medical case system;
filling first characteristic data by the nurse based on the first CRF data to obtain second CRF data;
filling second characteristic data by the first user based on the second CRF data to obtain third CRF data;
filling third characteristic data by the nurse based on the third CRF data to obtain fourth CRF data;
and filling fourth characteristic data by the doctor based on the fourth CRF data to obtain CRF data of the first user.
6. The method of claim 5, further comprising:
encrypting the first CRF data, the second CRF data, the third CRF data and the fourth CRF data according to the identification information of any writer, and respectively determining first encrypted CRF data, second encrypted CRF data, third encrypted CRF data and fourth encrypted CRF data;
and storing the first encrypted CRF data, the second encrypted CRF data, the third encrypted CRF data and the fourth encrypted CRF data into a block chain according to the first filling time information of the first CRF data, the second filling time information of the second CRF data, the third filling time information of the third CRF data and the fourth filling time information of the fourth CRF data.
7. The method of claim 1, further comprising:
receiving a data calling application sent by an access user, wherein the data calling application comprises account information of the access user;
verifying whether the account information of the access user is consistent with preset account information;
under the condition that the account information of the access user is consistent with the preset account information, acquiring target CRF data which is applied and called by the access user from the CRF database;
and sending the target CRF data to a terminal corresponding to the access user.
8. A data analysis apparatus, characterized in that the apparatus comprises:
the acquisition module is used for acquiring CRF data of a first user and CRF data of a second user from a pre-configured CRF database of a case report table;
the analysis module is used for determining physiological characteristic distinguishing information of the first user and the second user according to the CRF data of the first user and the CRF data of the second user;
wherein the first user has accepted a specified glycemic control therapy.
9. A computing device, the device comprising: a processor and a memory storing computer program instructions; the processor, when executing the computer program instructions, implements a data analysis method as claimed in any one of claims 1 to 7.
10. A computer storage medium having computer program instructions stored thereon which, when executed by a processor, implement a data analysis method as claimed in any one of claims 1 to 7.
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| CN116153450A (en) * | 2023-04-13 | 2023-05-23 | 合肥科颖医药科技有限公司 | Access content data comparison method and system based on intelligent analysis |
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