Data processing method, system and terminal based on stroke individuals

Abstract

The invention discloses a data processing method, system and terminal based on a stroke individual, and the method comprises the steps: collecting variable data of a target stroke individual, carrying out the assignment of the variable data according to a variable assignment rule, and obtaining the assignment information; performing calculation according to the assignment information to obtain an intermediate value, and performing calculation according to the intermediate value to obtain probability information; and performing grade judgment on the probability information according to a preset grade division standard to obtain the grade of the variable data. According to the method, the specific variable data of the cerebral apoplexy individual can be analyzed, the grade of the variable data can be calculated through the preset formula, and according to the grade of the variable data, medical workers can be helped to provide reference for formulating a personalized prevention and intervention scheme for the cerebral apoplexy individual.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to a data processing method, system, terminal and computer-readable storage medium based on stroke individuals. Background Art

[0002] Stroke is divided into two types: ischemic stroke and hemorrhagic stroke. It is a disease caused by various reasons that damage cerebral blood vessels and cause focal or global brain tissue damage. Deep vein thrombosis (DVT) is one of the common complications after stroke, which seriously affects the patient's recovery and prognosis. The symptoms of DVT include swelling, heaviness, pain, redness and tenderness of the limbs, but these clinical manifestations are not specific.

[0003] At present, the risk assessment of DVT mainly relies on the experience of doctors and some universal scoring tools, which lack accuracy and personalization. In addition, the specific data of stroke individuals are not fully utilized and are not further processed, so an accurate classification cannot be obtained, resulting in a lack of reference indicators for medical staff to judge the development of DVT in stroke individuals.

[0004] Therefore, the prior art still needs to be improved and developed. Summary of the invention

[0005] The main purpose of the present invention is to provide a data processing method, system, terminal and computer-readable storage medium based on stroke individuals, aiming to solve the problem that the specific data of stroke individuals are not fully utilized in the prior art, the specific data of stroke individuals are not further processed, and an accurate grade classification cannot be obtained, resulting in medical staff lacking reference indicators to judge the development of DVT in stroke individuals.

[0006] To achieve the above object, the present invention provides a method for processing data based on stroke individuals, the method comprising the following steps:

[0007] Collecting variable data of a target stroke individual, and assigning values ​​to the variable data according to a variable assignment rule to obtain assignment information;

[0008] Calculating according to the assignment information to obtain an intermediate value, and calculating according to the intermediate value to obtain probability information;

[0009] The probability information is graded according to a preset grade classification standard to obtain the grade of the variable data.

[0010] Optionally, in the data processing method based on stroke individuals, the step of collecting variable data of target stroke individuals specifically includes:

[0011] Initial variable data of the target stroke individuals to be treated were collected from the hospital's electronic case management system;

[0012] The initial variable data is cleaned, processed for missing data and standardized to obtain variable data.

[0013] Optionally, in the data processing method based on stroke individuals, the variable data include: age, gender, stroke subtype, D-dimer concentration, deep vein catheterization status and DVT history.

[0014] Optionally, in the data processing method based on stroke individuals, the assignment information includes: age assignment information X1, gender assignment information X2, stroke subtype assignment information X3, D-dimer assignment information X4, deep vein catheterization assignment information X5 and DVT medical history assignment information X6.

[0015] Optionally, the data processing method based on stroke individuals, wherein the step of assigning values ​​to the variable data according to a variable assignment rule to obtain assignment information, specifically includes:

[0016] When the age is less than or equal to the age threshold, the age assignment information X1 is set to 0; when the age is greater than the age threshold, the age assignment information X1 is set to 1;

[0017] When the gender is male, the gender assignment information X2 is set to 0, and when the gender is female, the gender assignment information X2 is set to 1;

[0018] When the stroke subtype is ischemic stroke, the stroke subtype assignment information X3 is set to 0; when the stroke subtype is hemorrhagic stroke, the stroke subtype assignment information X3 is set to 1;

[0019] When the D-dimer concentration is less than or equal to the concentration threshold, the D-dimer assignment information X4 is set to 0; when the D-dimer concentration is greater than the concentration threshold, the D-dimer assignment information X4 is set to 1;

[0020] When the deep vein catheterization situation does not exist, the deep vein catheterization assignment information X5 is set to 0; when the deep vein catheterization situation exists, the deep vein catheterization assignment information X5 is set to 1;

[0021] When the DVT medical history does not exist, the DVT medical history assignment information X6 is set to 0; when the DVT medical history does exist, the DVT medical history assignment information X6 is set to 1.

[0022] Optionally, the data processing method based on stroke individuals, wherein the intermediate value is calculated according to the assignment information, and the probability information is calculated according to the intermediate value, specifically includes:

[0023] Based on the first preset formula, the intermediate value z is calculated according to the age assignment information X1, the gender assignment information X2, the stroke subtype assignment information X3, the D-dimer assignment information X4, the deep vein catheter assignment information X5 and the DVT history assignment information X6, and the coefficients corresponding to each assignment information. The first preset formula is:

[0024] z=(-6.321+β1×X1+β2×X2+β3×X3+β4×X4+β5×X5+β6×X6);

[0025] Among them, β1 represents the coefficient corresponding to the age assignment information X1, β2 represents the coefficient corresponding to the gender assignment information X2, β3 represents the coefficient corresponding to the stroke subtype assignment information X3, β4 represents the coefficient corresponding to the D-dimer assignment information X4, β5 represents the coefficient corresponding to the deep vein catheterization assignment information X5, and β6 represents the coefficient corresponding to the DVT history assignment information X6;

[0026] Substitute the intermediate value into the second preset formula for calculation to obtain probability information P, where the second preset formula is:

[0027]

[0028] Here, e represents a natural constant.

[0029] Optionally, in the data processing method based on stroke individuals, the preset grade classification standard is:

[0030] If the probability information is less than a first preset threshold, the level of the variable data is a low level;

[0031] If the probability information is greater than or equal to the first preset threshold and less than or equal to the second preset threshold, the level of the variable data is medium level;

[0032] If the probability information is greater than the second preset threshold, the level of the variable data is a high level.

[0033] In addition, to achieve the above-mentioned purpose, the present invention further provides a data processing system based on stroke individuals, wherein the data processing system based on stroke individuals comprises:

[0034] A data collection and assignment module is used to collect variable data of a target stroke individual, and assign values ​​to the variable data according to a variable assignment rule to obtain assignment information;

[0035] A probability information calculation module, used to calculate an intermediate value according to the assignment information, and to calculate probability information according to the intermediate value;

[0036] The level classification module is used to perform level determination on the probability information according to a preset level classification standard to obtain the level of the variable data.

[0037] In addition, to achieve the above-mentioned purpose, the present invention also provides a terminal, wherein the terminal includes: a memory, a processor, and a data processing program based on stroke individuals stored in the memory and executable on the processor, wherein the data processing program based on stroke individuals implements the steps of the data processing method based on stroke individuals as described above when executed by the processor.

[0038] In addition, to achieve the above-mentioned purpose, the present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a data processing program based on stroke individuals, and when the data processing program based on stroke individuals is executed by a processor, the steps of the data processing method based on stroke individuals as described above are implemented.

[0039] In the present invention, variable data of target stroke individuals are collected, and the variable data are assigned according to variable assignment rules to obtain assignment information; intermediate values ​​are calculated according to the assignment information, and probability information is calculated according to the intermediate values; and the probability information is graded according to a preset grade classification standard to obtain the grade of the variable data. The present invention can analyze the variable data specific to the stroke individual, calculate the grade of the variable data through a preset formula, and can help medical workers provide references for formulating personalized prevention and intervention plans for stroke individuals based on the grade of the variable data. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 is a flow chart of a preferred embodiment of the data processing method based on stroke individuals of the present invention;

[0041] Figure 2 It is the overall architecture diagram of the data processing method based on stroke individuals of the present invention;

[0042] Figure 3It is a structural diagram of a preferred embodiment of the data processing system based on stroke individuals of the present invention;

[0043] Figure 4 It is a structural diagram of a preferred embodiment of the terminal of the present invention. DETAILED DESCRIPTION

[0044] The present application provides a method, system and terminal for data processing based on stroke individuals. To make the purpose, technical solution and effect of the present application clearer and more specific, the present application is further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0045] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as those generally understood by those skilled in the art to which this application belongs. It should also be understood that terms such as those defined in general dictionaries should be understood to have meanings consistent with those in the context of the prior art, and will not be interpreted with idealized or overly formal meanings unless specifically defined as here.

[0046] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present invention, the descriptions of "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or suggesting their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0047] The data processing method based on stroke individuals described in the preferred embodiment of the present invention is as follows: Figure 1 As shown, the data processing method based on stroke individuals includes the following steps:

[0048] Step S10: collecting variable data of the target stroke individual, and assigning values ​​to the variable data according to variable assignment rules to obtain assignment information.

[0049] The variable data of the target stroke individual is collected, specifically including:

[0050] Initial variable data of the target stroke individuals to be treated were collected from the hospital's electronic case management system;

[0051] The initial variable data is cleaned, processed for missing data and standardized to obtain variable data.

[0052] In this embodiment, after obtaining the target stroke individual to be processed, the initial variable data (without patient privacy information) of the target stroke individual is automatically captured from the electronic case management system of the hospital, and the initial variable data is preprocessed, and the preprocessing process includes data cleaning, data missing processing and data standardization processing, wherein the main purpose of data cleaning is to improve the quality and accuracy of the data, so that it is more suitable for subsequent data analysis and data mining; the main purpose of data missing processing is to ensure the accuracy and reliability of data analysis. Data missing will lead to incomplete data analysis results or erroneous conclusions, so it must be processed, and the main purpose of data standardization is to eliminate the influence of different dimensions and sizes between different data, thereby improving the consistency, comparability and reliability of the data. Further, the variable data include: age, gender, stroke subtype, D-dimer concentration (D-dimer is a soluble degradation product produced by cross-linked fibrin under the action of the fibrinolytic system, a specific fibrinolytic process marker, and has a high sensitivity to thrombosis), deep vein catheterization and DVT history. It can be understood that the variable data obtained in the present invention is specific medical information obtained from stroke individuals, wherein age and gender are obtained from patient identity information registered in the electronic case management system.

[0053] The assignment information corresponds to the above-mentioned variable data respectively, and the assignment information includes: age assignment information X1, gender assignment information X2, stroke subtype assignment information X3, D-dimer assignment information X4, deep vein catheterization assignment information X5 and DVT medical history assignment information X6.

[0054] Furthermore, assigning the variable data according to the variable assignment rule to obtain the assignment information specifically includes:

[0055] When the age is less than or equal to the age threshold, the age assignment information X1 is set to 0; when the age is greater than the age threshold, the age assignment information X1 is set to 1;

[0056] When the gender is male, the gender assignment information X2 is set to 0, and when the gender is female, the gender assignment information X2 is set to 1;

[0057] When the stroke subtype is ischemic stroke, the stroke subtype assignment information X3 is set to 0; when the stroke subtype is hemorrhagic stroke, the stroke subtype assignment information X3 is set to 1;

[0058] When the D-dimer concentration is less than or equal to the concentration threshold, the D-dimer assignment information X4 is set to 0; when the D-dimer concentration is greater than the concentration threshold, the D-dimer assignment information X4 is set to 1;

[0059] When the deep vein catheterization situation does not exist, the deep vein catheterization assignment information X5 is set to 0; when the deep vein catheterization situation exists, the deep vein catheterization assignment information X5 is set to 1;

[0060] When the DVT medical history does not exist, the DVT medical history assignment information X6 is set to 0; when the DVT medical history does exist, the DVT medical history assignment information X6 is set to 1.

[0061] It is understandable that the electronic case management system fully records the case information of stroke individuals. For example, the registration information in the electronic case management system shows that patient A, 70 years old, female, was diagnosed with hemorrhagic stroke. Laboratory test results showed that the D-dimer was 0.42 mg / L, with deep vein catheterization and no history of DVT. Then the values ​​of age X1, gender X2, stroke subtype X3, D-dimer X4, deep vein catheterization X5, and DVT history X6 obtained by the above variable assignment rules are: >63 years old, female, hemorrhagic stroke, D-dimer >0.32 mg / L, present, and absent. According to the above-obtained age X1, gender X2, stroke subtype X3, D-dimer X4, deep vein catheterization X5, and DVT history X6, the values ​​are assigned as follows: 1, 1, 1, 1, 1, 0.

[0062] For example, the registration information in the electronic case management system shows that patient B, 59 years old, male, was diagnosed with hemorrhagic stroke, and the laboratory test results showed that the D-dimer was 0.29 mg / L, with deep vein catheterization, and DVT occurred one year ago. The values ​​of the age assignment information X1, gender assignment information X2, stroke subtype assignment information X3, D-dimer assignment information X4, deep vein catheterization assignment information X5, and DVT history assignment information X6 obtained through the above variable assignment rules are: ≤63 years old, male, hemorrhagic stroke, D-dimer ≤0.32 mg / L, present, present. According to the above-obtained age assignment information X1, gender assignment information X2, stroke subtype assignment information X3, D-dimer assignment information X4, deep vein catheterization assignment information X5, and DVT history assignment information X6, the values ​​are assigned as follows: 0, 0, 1, 0, 1, 1.

[0063] For another example, the registration information in the electronic case management system shows that patient C, 69 years old, female, was diagnosed with ischemic stroke, and the laboratory test results showed that the D-dimer was 0.18 mg / L, with deep vein catheterization, and no history of DVT. The values ​​of the age assignment information X1, gender assignment information X2, stroke subtype assignment information X3, D-dimer assignment information X4, deep vein catheterization assignment information X5, and DVT history assignment information X6 obtained through the above variable assignment rules are: >63 years old, female, ischemic stroke, D-dimer ≤0.32 mg / L, present, absent. According to the above-obtained age assignment information X1, gender assignment information X2, stroke subtype assignment information X3, D-dimer assignment information X4, deep vein catheterization assignment information X5, and DVT history assignment information X6, the values ​​are assigned as follows: 1, 1, 0, 0, 1, 0.

[0064] Step S20: Calculate an intermediate value based on the assignment information, and calculate probability information based on the intermediate value.

[0065] Specifically, based on the first preset formula, the intermediate value z is calculated according to the age assignment information X1, the gender assignment information X2, the stroke subtype assignment information X3, the D-dimer assignment information X4, the deep vein catheter assignment information X5 and the DVT history assignment information X6, and the coefficients corresponding to each assignment information. The first preset formula is:

[0066] z=(-6.321+β1×X1+β2×X2+β3×X3+β4×X4+β5×X5+β6×X6);

[0067] Among them, β1 represents the coefficient corresponding to the age assignment information X1, β2 represents the coefficient corresponding to the gender assignment information X2, β3 represents the coefficient corresponding to the stroke subtype assignment information X3, β4 represents the coefficient corresponding to the D-dimer assignment information X4, β5 represents the coefficient corresponding to the deep vein catheterization assignment information X5, and β6 represents the coefficient corresponding to the DVT medical history assignment information X6.

[0068] It can be understood that, in this embodiment, the value of the coefficient β corresponding to each assignment information X is obtained after data analysis of the previous study, as shown in the following Table 1:

[0069] Table 1: β value correspondence table

[0070]

[0071]

[0072] Furthermore, the intermediate value is substituted into a second preset formula for calculation to obtain probability information P, where the second preset formula is:

[0073] Wherein, e represents a natural constant. It can be understood that the probability information P is the probability of obtaining DVT in a stroke patient.

[0074] Step S30: performing a level determination on the probability information according to a preset level classification standard to obtain the level of the variable data.

[0075] Specifically, the preset level classification standard is: if the probability information is less than a first preset threshold, the level of the variable data is a low level; if the probability information is greater than or equal to the first preset threshold, and less than or equal to the second preset threshold, the level of the variable data is a medium level; if the probability information is greater than the second preset threshold, the level of the variable data is a high level.

[0076] Based on the above-mentioned classification standard, the probability information is graded and the grade of the variable data is obtained for the doctor's reference. As an example, the probability information can be graded according to the following classification standards: P < 30%, low grade; 30% ≤ P ≤ 70%, medium grade; P > 70%, high grade.

[0077] Taking patient A as an example, the overall process of the data processing method based on stroke individuals is given below:

[0078] First, the registration information in the electronic case management system showed that the patient was a 70-year-old female diagnosed with hemorrhagic stroke. Laboratory test results showed that the D-dimer was 0.42 mg / L, with deep venous catheterization and no history of DVT.

[0079] Next, the values ​​of the age assignment information X1, gender assignment information X2, stroke subtype assignment information X3, D-dimer assignment information X4, deep vein catheter assignment information X5, and DVT history assignment information X6 are respectively: >63 years old, female, hemorrhagic stroke, D-dimer >0.32 mg / L, present, and not present.

[0080] Then, according to the age assignment information X1, gender assignment information X2, stroke subtype assignment information X3, D-dimer assignment information X4, deep vein catheterization assignment information X5, and DVT history assignment information X6 obtained above, the values ​​are assigned as follows: 1, 1, 1, 1, 1, 0, respectively.

[0081] Finally, the probability information P of DVT in stroke individuals is calculated:

[0082] Z=-6.321+1.234×1+1.456×1+1.679×1+1.892×1+2.135×

[0083] 1+2.454×0=2.075;

[0084]

[0085] Therefore, P=88.9%, and the probability information is graded according to the preset grade classification standard (P=88.9%>70%), and the grade of the variable data is obtained to be high.

[0086] Furthermore, in this embodiment, after obtaining the level of the variable data, corresponding preventive measures are provided according to the level, for example:

[0087] P < 30%, low grade; indicates that the patient is at low risk of DVT, and corresponding preventive measures can be taken, such as: medical staff can provide health education to patients by distributing DVT prevention manuals, playing anti-thrombotic knowledge and demonstrations through electronic videos, etc. 30% ≤ P ≤ 70%, medium grade; indicates that the patient is at medium risk of DVT, and corresponding intervention measures can be taken, such as: low-fat diet, drinking water with a scaled cup, and meeting the daily water intake standard in the absence of contraindications; formulate a targeted early bed activity plan to assist patients in exercising both lower limbs. P > 70%, high grade; indicates that the patient is at high risk of DVT, and medical staff should immediately take targeted intervention measures, such as: closely monitoring the patient's vital signs and observing whether there are abnormal symptoms such as swelling, pain, numbness or increased skin temperature in both lower limbs. At the same time, listen to the patient's complaints. For patients with abnormal symptoms such as swelling and pain in both lower limbs, the leg circumference should be measured with a soft ruler in time, and ultrasound examination of both lower limb vessels should be performed if necessary to check for deep vein thrombosis (DVT). In addition, avoid intravenous puncture in the lower limbs. If necessary, intervention can also be combined with air wave pressure therapy.

[0088] It can be seen that the present invention automatically captures the medical information of stroke individuals from the hospital electronic case management system, assigns values ​​to the obtained variables, and then calculates the probability of DVT in stroke individuals through a formula, so that medical staff can quickly make corresponding treatment decisions based on the risk level. It realizes the informatization of DVT risk decision-making for stroke patients, provides a certain guidance for clinical medical staff, replaces cumbersome evaluation methods, and improves work efficiency.

[0089] Furthermore, if Figure 3 As shown, based on the above-mentioned data processing method based on stroke individuals, the present invention also provides a data processing system based on stroke individuals, wherein the data processing system based on stroke individuals includes:

[0090] The data collection and assignment module 51 is used to collect variable data of the target stroke individual, and assign values ​​to the variable data according to the variable assignment rule to obtain assignment information;

[0091] A probability information calculation module 52, configured to calculate an intermediate value according to the assignment information, and calculate probability information according to the intermediate value;

[0092] The level classification module 53 is used to perform level determination on the probability information according to a preset level classification standard to obtain the level of the variable data.

[0093] Furthermore, if Figure 4 As shown, based on the above-mentioned method and system for processing data based on stroke individuals, the present invention also provides a terminal accordingly, and the terminal includes a processor 10, a memory 20 and a display 30. Figure 4 Only some components of the terminal are shown, but it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0094] The memory 20 may be an internal storage unit of the terminal in some embodiments, such as a hard disk or memory of the terminal. The memory 20 may also be an external storage device of the terminal in other embodiments, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the terminal. Further, the memory 20 may also include both an internal storage unit of the terminal and an external storage device. The memory 20 is used to store application software and various types of data installed in the terminal, such as the program code of the installation terminal, etc. The memory 20 may also be used to temporarily store data that has been output or is to be output. In one embodiment, a data processing program 40 based on a stroke individual is stored on the memory 20, and the data processing program 40 based on a stroke individual can be executed by the processor 10, thereby realizing the data processing method based on a stroke individual in the present application.

[0095] In some embodiments, the processor 10 may be a central processing unit (CPU), a microprocessor or other data processing chip, used to run the program code or process data stored in the memory 20, such as executing the data processing method based on stroke individuals.

[0096] In some embodiments, the display 30 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, etc. The display 30 is used to display information on the terminal and to display a visual user interface. The components of the terminal communicate with each other via a system bus.

[0097] In one embodiment, when the processor 10 executes the stroke individual-based data processing program 40 in the memory 20, the following steps are implemented:

[0098] Collecting variable data of a target stroke individual, and assigning values ​​to the variable data according to a variable assignment rule to obtain assignment information;

[0099] Calculating according to the assignment information to obtain an intermediate value, and calculating according to the intermediate value to obtain probability information;

[0100] The probability information is graded according to a preset grade classification standard to obtain the grade of the variable data.

[0101] The variable data of the target stroke individual collected specifically includes:

[0102] Initial variable data of the target stroke individuals to be treated were collected from the hospital's electronic case management system;

[0103] The initial variable data is cleaned, processed for missing data and standardized to obtain variable data.

[0104] The variable data include: age, gender, stroke subtype, D-dimer concentration, deep vein catheterization and DVT history.

[0105] The assignment information includes: age assignment information X1, gender assignment information X2, stroke subtype assignment information X3, D-dimer assignment information X4, deep vein catheterization assignment information X5 and DVT medical history assignment information X6.

[0106] The step of assigning values ​​to the variable data according to the variable assignment rule to obtain the assignment information specifically includes:

[0107] When the age is less than or equal to the age threshold, the age assignment information X1 is set to 0; when the age is greater than the age threshold, the age assignment information X1 is set to 1;

[0108] When the gender is male, the gender assignment information X2 is set to 0, and when the gender is female, the gender assignment information X2 is set to 1;

[0109] When the stroke subtype is ischemic stroke, the stroke subtype assignment information X3 is set to 0; when the stroke subtype is hemorrhagic stroke, the stroke subtype assignment information X3 is set to 1;

[0110] When the D-dimer concentration is less than or equal to the concentration threshold, the D-dimer assignment information X4 is set to 0; when the D-dimer concentration is greater than the concentration threshold, the D-dimer assignment information X4 is set to 1;

[0111] When the deep vein catheterization situation does not exist, the deep vein catheterization assignment information X5 is set to 0; when the deep vein catheterization situation exists, the deep vein catheterization assignment information X5 is set to 1;

[0112] When the DVT medical history does not exist, the DVT medical history assignment information X6 is set to 0; when the DVT medical history does exist, the DVT medical history assignment information X6 is set to 1.

[0113] The step of calculating the intermediate value according to the assignment information and calculating the probability information according to the intermediate value specifically includes:

[0114] Based on the first preset formula, the intermediate value z is calculated according to the age assignment information X1, the gender assignment information X2, the stroke subtype assignment information X3, the D-dimer assignment information X4, the deep vein catheter assignment information X5 and the DVT history assignment information X6, and the coefficients corresponding to each assignment information. The first preset formula is:

[0115] z=(-6.321+β1×X1+β2×X2+β3×X3+β4×X4+β5×X5+β6×X6);

[0116] Among them, β1 represents the coefficient corresponding to the age assignment information X1, β2 represents the coefficient corresponding to the gender assignment information X2, β3 represents the coefficient corresponding to the stroke subtype assignment information X3, β4 represents the coefficient corresponding to the D-dimer assignment information X4, β5 represents the coefficient corresponding to the deep vein catheterization assignment information X5, and β6 represents the coefficient corresponding to the DVT history assignment information X6;

[0117] Substitute the intermediate value into the second preset formula for calculation to obtain probability information P, where the second preset formula is:

[0118]

[0119] Here, e represents a natural constant.

[0120] The preset classification standard is:

[0121] If the probability information is less than a first preset threshold, the level of the variable data is a low level;

[0122] If the probability information is greater than or equal to the first preset threshold and less than or equal to the second preset threshold, the level of the variable data is medium level;

[0123] If the probability information is greater than the second preset threshold, the level of the variable data is a high level.

[0124] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a data processing program based on stroke individuals, and when the data processing program based on stroke individuals is executed by a processor, the steps of the data processing method based on stroke individuals as described above are implemented.

[0125] In summary, the present invention proposes a data processing method, system and terminal based on stroke individuals, the method comprising: collecting variable data of target stroke individuals, assigning values ​​to the variable data according to variable assignment rules, and obtaining assignment information; calculating an intermediate value according to the assignment information, and obtaining probability information according to the intermediate value; and determining the level of the probability information according to a preset level classification standard to obtain the level of the variable data. The present invention automatically captures specific medical information of stroke individuals from the hospital electronic case management system, and assigns values ​​to the obtained variables, and then calculates the probability of DVT in stroke individuals through a formula, and medical personnel can quickly make corresponding processing decisions according to the risk level. According to the level of variable data, it can help medical workers provide references for formulating personalized prevention and intervention plans for stroke individuals, provide a certain guiding role for clinical medical personnel, replace cumbersome evaluation methods, and improve work efficiency.

[0126] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or terminal including the element.

[0127] Of course, those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0128] It should be understood that the application of the present invention is not limited to the above examples. For ordinary technicians in this field, improvements or changes can be made based on the above description. All these improvements and changes should fall within the scope of protection of the claims attached to the present invention.

Claims

1. A data processing method based on stroke individuals, characterized in that: The data processing method based on stroke individuals includes: Collecting variable data of a target stroke individual, and assigning values ​​to the variable data according to a variable assignment rule to obtain assignment information; Calculating according to the assignment information to obtain an intermediate value, and calculating according to the intermediate value to obtain probability information; The probability information is graded according to a preset grade classification standard to obtain the grade of the variable data.

2. The method for processing data based on stroke individuals according to claim 1, characterized in that: The variable data of the target stroke individual is collected, specifically including: Initial variable data of the target stroke individuals to be treated were collected from the hospital's electronic case management system; The initial variable data is cleaned, processed for missing data and standardized to obtain variable data.

3. The method for processing data based on stroke individuals according to claim 1, characterized in that: The variables included age, sex, stroke subtype, D-dimer concentration, deep vein catheterization, and history of DVT.

4. The method for processing data based on stroke individuals according to claim 3, characterized in that: The assignment information includes: age assignment information X1, gender assignment information X2, stroke subtype assignment information X3, D-dimer assignment information X4, deep vein catheterization assignment information X5 and DVT medical history assignment information X6.

5. The method for processing data based on stroke individuals according to claim 4, characterized in that: The step of assigning a value to the variable data according to the variable assignment rule to obtain the assignment information specifically includes: When the age is less than or equal to the age threshold, the age assignment information X1 is set to 0; when the age is greater than the age threshold, the age assignment information X1 is set to 1; When the gender is male, the gender assignment information X2 is set to 0, and when the gender is female, the gender assignment information X2 is set to 1; When the stroke subtype is ischemic stroke, the stroke subtype assignment information X3 is set to 0; when the stroke subtype is hemorrhagic stroke, the stroke subtype assignment information X3 is set to 1; When the D-dimer concentration is less than or equal to the concentration threshold, the D-dimer assignment information X4 is set to 0; when the D-dimer concentration is greater than the concentration threshold, the D-dimer assignment information X4 is set to 1; When the deep vein catheterization situation does not exist, the deep vein catheterization assignment information X5 is set to 0; when the deep vein catheterization situation exists, the deep vein catheterization assignment information X5 is set to 1; When the DVT medical history does not exist, the DVT medical history assignment information X6 is set to 0; when the DVT medical history does exist, the DVT medical history assignment information X6 is set to 1.

6. The method for processing data based on stroke individuals according to claim 4, characterized in that: The calculating according to the assignment information to obtain the intermediate value, and the calculating according to the intermediate value to obtain the probability information specifically includes: Based on the first preset formula, the intermediate value z is calculated according to the age assignment information X1, the gender assignment information X2, the stroke subtype assignment information X3, the D-dimer assignment information X4, the deep vein catheter assignment information X5 and the DVT history assignment information X6, and the coefficients corresponding to each assignment information. The first preset formula is: z=(-6.321+β1×X1+β2×X2+β3×X3+β4×X4+β5×X5+β6×X6); Among them, β1 represents the coefficient corresponding to the age assignment information X1, β2 represents the coefficient corresponding to the gender assignment information X2, β3 represents the coefficient corresponding to the stroke subtype assignment information X3, β4 represents the coefficient corresponding to the D-dimer assignment information X4, β5 represents the coefficient corresponding to the deep vein catheterization assignment information X5, and β6 represents the coefficient corresponding to the DVT history assignment information X6; Substitute the intermediate value z into the second preset formula for calculation to obtain probability information P, where the second preset formula is: Here, e represents a natural constant.

7. The method for processing data based on stroke individuals according to claim 1, characterized in that: The preset grading standards are: If the probability information is less than a first preset threshold, the level of the variable data is a low level; If the probability information is greater than or equal to the first preset threshold and less than or equal to the second preset threshold, the level of the variable data is medium level; If the probability information is greater than the second preset threshold, the level of the variable data is a high level.

8. A data processing system based on stroke individuals, characterized in that: The stroke individual-based data processing system comprises: A data collection and assignment module is used to collect variable data of a target stroke individual, and assign values ​​to the variable data according to a variable assignment rule to obtain assignment information; A probability information calculation module, used to calculate an intermediate value according to the assignment information, and to calculate probability information according to the intermediate value; The level classification module is used to perform level determination on the probability information according to a preset level classification standard to obtain the level of the variable data.

9. A terminal, characterized in that: The terminal includes: a memory, a processor, and a data processing program based on stroke individuals stored in the memory and executable on the processor. When the data processing program based on stroke individuals is executed by the processor, the steps of the data processing method based on stroke individuals as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a data processing program based on stroke individuals, and when the data processing program based on stroke individuals is executed by a processor, the steps of the data processing method based on stroke individuals as described in any one of claims 1-7 are implemented.

Images