A data processing system and method for population bone density t-score evaluation analysis

Abstract

The present application relates to the technical field of medical data processing, in particular to a data processing system and method for crowd bone density T value evaluation analysis, data acquisition module, data storage module, preset value module, calculation module, combined with the calculation preset parameter, statistical module. The present application only needs two parameters of age and alkaline phosphatase to calculate the bone density T value evaluation index, the data acquisition is simple, the calculation process is efficient, and it is especially suitable for large-scale crowd screening.

Description

Technical Field

[0001] This invention relates to the field of medical data processing technology, specifically to a data processing system and method for assessing and analyzing bone mineral density T-scores in a population. Background Technology

[0002] Bone mineral density (BMD) is an important indicator for assessing bone health. The T-score, calculated by comparing an individual's BMD to the average BMD of a healthy young adult population, is the gold standard for clinical diagnosis of osteoporosis. With the accelerating aging of the population, osteoporosis has become a global public health issue, making large-scale screening and assessment of population bone mineral density of significant public health importance.

[0003] Currently, bone mineral density (BMD) measurement mainly relies on specialized equipment such as dual-energy X-ray absorptiometry (DXA). These devices are expensive and complex to operate, making them difficult to widely apply in primary healthcare institutions and large-scale epidemiological surveys. Therefore, exploring methods to assess BMD T-scores using routine biochemical indicators and basic population information has significant practical value.

[0004] While some existing technologies use biochemical indicators to predict bone mineral density, most suffer from the following problems: complex prediction models, too many computational parameters, and difficulty in standardization; lack of statistical analysis functions for different regional populations, failing to provide data support for the formulation of regional osteoporosis prevention and control strategies; and low system integration, with data collection, storage, computation, and statistical analysis being independent of each other, making it difficult to achieve automated batch processing.

[0005] In view of this, we propose a data processing method for the assessment and analysis of bone mineral density T-scores in the population. Summary of the Invention

[0006] The technical problem to be solved by this invention is to provide a data processing system and method for assessing and analyzing bone mineral density T-scores in a population. This system can quickly calculate bone mineral density T-score assessment indicators based on basic population information and routine biochemical test data, and realize statistical analysis of regional populations, providing technical support for large-scale screening of osteoporosis and the formulation of regional prevention and control strategies.

[0007] To address the aforementioned technical problems, this invention provides a data processing system for assessing and analyzing bone mineral density T-scores in a population, comprising:

[0008] The data acquisition module is used to acquire basic information about the population and biochemical test data;

[0009] The data storage module is used to store the data acquired by the data acquisition module in the form of variables into a variable library;

[0010] The preset value module is used to store the preset parameters for calculating the bone mineral density T-score assessment index;

[0011] The calculation module is used to calculate the bone mineral density T-value assessment index based on the basic information of the population and biochemical test data, combined with the preset calculation parameters.

[0012] The statistics module is used to perform statistical analysis on the T-score assessment index of bone mineral density in the population.

[0013] Furthermore, the data acquisition module includes a data acquisition interface integration module, which is an interface module reserved for the population basic information acquisition device and the biochemical test data acquisition device.

[0014] Furthermore, the basic information of the population includes subject ID, region ID, gender, and age; the biochemical test data includes alkaline phosphatase levels.

[0015] Furthermore, the preset parameters stored in the preset value module include: the power value of age x, the power value of alkaline phosphatase y, and the optimization coefficient h; the preset value module also stores the threshold value of the bone mineral density T-value assessment index.

[0016] A data processing method for assessing bone mineral density T-scores in a population includes the following steps:

[0017] S1. Acquire basic population information and biochemical test data through the data acquisition module. The basic population information includes at least age and region number, and the biochemical test data includes at least alkaline phosphatase value.

[0018] S2. Perform power calculation on the age data of the population to obtain the x-th power value of the age;

[0019] S3. Perform power calculation on the alkaline phosphatase values ​​of the population to obtain the y-th power value of alkaline phosphatase.

[0020] S4. Calculate the bone mineral density T-score assessment index according to the following formula:

[0021] Bone mineral density T-score assessment index = h × age^x + (1-h) × alkaline phosphatase^y

[0022] Where x is the power value of age, y is the power value of alkaline phosphatase, and h is the optimization coefficient, where h is a real number;

[0023] S5. For populations with the same region number, calculate the bone mineral density T-value assessment index value of each subject using the calculation formula in step S4, and form a set of index values ​​for the same region.

[0024] S6. Perform statistical analysis on the set of regional index values ​​obtained in step S5, calculate the regional mean, regional median, regional mode, regional variance and regional standard deviation, and obtain the set of statistical indexes for regional bone density T-value assessment.

[0025] S7. Repeat steps S5 and S6 for all regions to obtain a set of statistical indicators for bone mineral density T-value assessment of each region.

[0026] S8. Classify and summarize the statistical indicator sets of each region to form the regional mean set, regional median set, regional mode set, regional variance set, and regional standard deviation set.

[0027] Furthermore, in step S1, each subject corresponds to a data vector (p(1,r1),p(2,r2),p(3,r3),p(4,r4),p(5,r5)), where p(1,r1) is the subject number, p(2,r2) is the region number, p(3,r3) is the gender, p(4,r4) is the age, and p(5,r5) is the alkaline phosphatase value; where the region number r2 is a positive integer with a value range of 1≤r2≤N, and N is the total number of regions.

[0028] Furthermore, in step S4, the optimization coefficient h = 0.0228; the calculated bone mineral density T-value evaluation index is represented as p(6,r6), where r6 is the index number.

[0029] Furthermore, in step S5, the elements of the set of index values ​​in the same region are binary pairs, in the form of (p(1,r1),p(6,r6)), which represent the subject number and the corresponding bone mineral density T-value assessment index value, respectively.

[0030] Furthermore, in step S6, for any region p(2,i), let the number of subjects in this region be n(i), where i is the region number and n(i)≥1, then:

[0031] Regional mean (i) = p(6,j) represents the bone mineral density T-value of subject p(1,j) in region p(2,i);

[0032] The regional median (i) is the median of the bone mineral density T-value assessment index for all subjects within region p(2,i);

[0033] The regional mode (i) is the bone mineral density T-value that appears most frequently in region p(2,i).

[0034] Regional variance (i) = p(6,j) represents the bone mineral density T-value of subject p(1,j) in region p(2,i);

[0035] Regional standard deviation (i) = .

[0036] Furthermore, in step S8:

[0037] The set of regional means = {regional mean(i)|1≤i≤N};

[0038] Regional median set={regional median(i)|1≤i≤N};

[0039] The set of regional modes = {regional mode(i) | 1 ≤ i ≤ N};

[0040] The set of regional variances = {regional variance(i)|1≤i≤N};

[0041] The set of regional standard deviations = {regional standard deviation(i)|1≤i≤N};

[0042] Where N is the total number of regions.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] This invention requires only two parameters, age and alkaline phosphatase, to calculate the bone mineral density T-score assessment index. The data acquisition is simple, the calculation process is efficient, and it is particularly suitable for large-scale population screening.

[0045] This invention can automatically perform statistical analysis of a regional population, including various statistical indicators such as mean, median, mode, variance, and standard deviation, providing a scientific basis for the formulation of regional osteoporosis prevention and control strategies.

[0046] This invention integrates data acquisition, storage, calculation, and statistical analysis functions into a single system, and reserves standardized interfaces to facilitate interfacing with existing medical information systems, enabling automated data collection and processing.

[0047] This invention organizes data using data vectors and binary pairs, which facilitates data management, statistical analysis, and individual traceability, and has good scalability. Attached Figure Description

[0048] Figure 1 This is a structural block diagram of the data processing system of the present invention;

[0049] Figure 2 This is a flowchart of the data processing method of the present invention. Detailed Implementation

[0050] The present application will be further described in detail below with reference to the accompanying drawings.

[0051] Example 1; as Figure 1As shown, this application discloses a data processing system for assessing and analyzing bone mineral density T-scores in a population, comprising:

[0052] The data acquisition module is used to acquire basic information about the population and biochemical test data;

[0053] The data storage module is used to store the data acquired by the data acquisition module in the form of variables into a variable library;

[0054] The preset value module is used to store the preset parameters for calculating the bone mineral density T-score assessment index;

[0055] The calculation module is used to calculate the bone mineral density T-value assessment index based on the basic information of the population and biochemical test data, combined with the preset calculation parameters.

[0056] The statistics module is used to perform statistical analysis on the T-score assessment index of bone mineral density in the population.

[0057] As an embodiment of this application, the data acquisition module includes a data acquisition interface integration module, which is an interface module reserved for the population basic information acquisition device and the biochemical test data acquisition device.

[0058] As an embodiment of this application, the basic information of the population includes subject number, region number, gender and age; the biochemical test data includes alkaline phosphatase value.

[0059] As an embodiment of this application, the preset parameters stored in the preset value module include: the power value of age x, the power value of alkaline phosphatase y, and the optimization coefficient h; the preset value module also stores the threshold value of the bone mineral density T-value assessment index.

[0060] Example 2; as Figure 2 As shown, a data processing method for assessing and analyzing bone mineral density T-scores in a population includes the following steps:

[0061] S1. Acquire basic population information and biochemical test data through the data acquisition module. The basic population information includes at least age and region number, and the biochemical test data includes at least alkaline phosphatase value.

[0062] S2. Perform power calculation on the age data of the population to obtain the x-th power value of the age;

[0063] S3. Perform power calculation on the alkaline phosphatase values ​​of the population to obtain the y-th power value of alkaline phosphatase.

[0064] S4. Calculate the bone mineral density T-score assessment index according to the following formula:

[0065] Bone mineral density T-score assessment index = h × age^x + (1-h) × alkaline phosphatase^y

[0066] Where x is the power value of age, y is the power value of alkaline phosphatase, and h is the optimization coefficient, where h is a real number;

[0067] S5. For populations with the same region number, calculate the bone mineral density T-value assessment index value of each subject using the calculation formula in step S4, and form a set of index values ​​for the same region.

[0068] S6. Perform statistical analysis on the set of regional index values ​​obtained in step S5, calculate the regional mean, regional median, regional mode, regional variance and regional standard deviation, and obtain the set of statistical indexes for regional bone density T-value assessment.

[0069] S7. Repeat steps S5 and S6 for all regions to obtain a set of statistical indicators for bone mineral density T-value assessment of each region.

[0070] S8. Classify and summarize the statistical indicator sets of each region to form the regional mean set, regional median set, regional mode set, regional variance set, and regional standard deviation set.

[0071] As an embodiment of this application, in step S1, each subject corresponds to a data vector (p(1,r1),p(2,r2),p(3,r3),p(4,r4),p(5,r5)), where p(1,r1) is the subject number, p(2,r2) is the region number, p(3,r3) is the gender, p(4,r4) is the age, and p(5,r5) is the alkaline phosphatase value; where the region number r2 is a positive integer, and the value range is 1≤r2≤N, and N is the total number of regions.

[0072] As an embodiment of this application, in step S4, the optimization coefficient h = 0.0228; the calculated bone mineral density T-value evaluation index value is represented as p(6,r6), where r6 is the index number.

[0073] As an embodiment of this application, in step S5, the elements of the set of index values ​​in the same region are binary pairs, in the form of (p(1,r1),p(6,r6)), which represent the subject number and the corresponding bone mineral density T-value assessment index value, respectively.

[0074] As an embodiment of this application, in step S6, for any region p(2,i), let the number of subjects in this region be n(i), where i is the region number and n(i)≥1, then:

[0075] Regional mean (i) = p(6,j) represents the bone mineral density T-value of subject p(1,j) in region p(2,i);

[0076] The regional median (i) is the median of the bone mineral density T-value assessment index for all subjects within region p(2,i);

[0077] The regional mode (i) is the bone mineral density T-value that appears most frequently in region p(2,i).

[0078] Regional variance (i) = p(6,j) represents the bone mineral density T-value of subject p(1,j) in region p(2,i);

[0079] Regional standard deviation (i) = .

[0080] As an embodiment of this application, in step S8:

[0081] The set of regional means = {regional mean(i)|1≤i≤N};

[0082] Regional median set={regional median(i)|1≤i≤N};

[0083] The set of regional modes = {regional mode(i) | 1 ≤ i ≤ N};

[0084] The set of regional variances = {regional variance(i)|1≤i≤N};

[0085] The set of regional standard deviations = {regional standard deviation(i)|1≤i≤N};

[0086] Where N is the total number of regions.

[0087] Example 3; This example provides a data processing system for assessing and analyzing bone mineral density T-scores in a population, including a data acquisition module, a data storage module, a preset value module, a calculation module, and a statistics module.

[0088] The data acquisition module includes a data acquisition device interface integration module, which provides standardized interfaces for the population basic information acquisition device and the biochemical test data acquisition device. In practical applications, this interface module can be used to connect with external data sources such as hospital information systems, laboratory information systems, and physical examination management systems to automatically acquire the basic information and biochemical test data of the subjects.

[0089] The data storage module uses a relational database or distributed storage system to store the acquired data as variables in a variable library. Each subject's data is stored as a single record, containing fields such as subject ID, region ID, gender, age, and alkaline phosphatase level.

[0090] The preset value module stores preset parameters for calculating the bone mineral density T-score assessment index, including: age power value x=3.4, alkaline phosphatase power value y=2, optimization coefficient h=0.0228, and bone mineral density T-score assessment index threshold; the calculation module calculates the bone mineral density T-score assessment index according to the following formula:

[0091] Bone mineral density T-score assessment index = 0.0228 × age^3.4 + 0.9772 × alkaline phosphatase^2

[0092] The statistics module performs statistical analysis on the T-score assessment index of bone mineral density in the population, and calculates the mean, median, mode, variance and standard deviation for each region.

[0093] Example 4, as Figure 2 As shown, this embodiment provides a data processing method for assessing and analyzing bone mineral density T-scores in a population. Using the data processing system described in Embodiment 1, and taking 1000 subjects from 5 regions in a certain area as an example, the specific implementation steps are as follows:

[0094] Step S1: Obtain basic information and biochemical test data of 1000 subjects from the physical examination management system through the data acquisition module. The data for each subject is stored in vector form.

[0095] For example, the data vector of subject 001 is (001,1,male,55,78), which means: subject number is 001, region number is 1, gender is male, age is 55 years old, and alkaline phosphatase value is 78U / L.

[0096] Step S2: Calculate the age of each subject by power to obtain the 3.4th power value of the age.

[0097] Taking subject 001 as an example: age^3.4 = 55^3.4 ≈ 871,638.5

[0098] Step S3: Calculate the power of the alkaline phosphatase value for each subject to obtain the square value of alkaline phosphatase.

[0099] Taking subject 001 as an example: alkaline phosphatase^2=78^2=6,084;

[0100] Step S4: Calculate the bone mineral density T-value assessment index according to the formula.

[0101] Taking subject 001 as an example:

[0102] Bone mineral density T-score assessment index = 0.0228×871,638.5+0.9772×6,084=19,873.36+5,945.30=25,818.66; the calculation result is stored as p(6,001)=25,818.66.

[0103] Step S5: Group the subjects according to the region number and construct a set of indicator values ​​for the same region.

[0104] If Region 1 has 200 subjects, then the set of indicator values ​​for Region 1 is as follows:

[0105] {(001,25818.66),(002,23456.78),...,(200,28765.43)}

[0106] Step S6: Perform statistical analysis on the set of indicator values ​​for region 1.

[0107] The calculation result is:

[0108] The regional mean (1) = 26,500.50;

[0109] Regional median(1)=26,100.25;

[0110] The regional mode (1) = 25,800.00;

[0111] Regional variance (1) = 8,250,000.00;

[0112] Regional standard deviation (1) = 2,872.28;

[0113] Step S7: Repeat steps S5 and S6 for the remaining 4 regions to obtain the statistical indicators for each region.

[0114] Step S8: Classify and summarize the statistical indicators of the 5 regions to obtain:

[0115] The set of regional mean values ​​is {26500.50, 24800.30, 27200.45, 25600.80, 26100.60}.

[0116] Regional median set={26100.25,24500.50,27000.30,25400.75,25900.40};

[0117] The regional mode set = {25800.00, 24200.00, 26800.00, 25200.00, 25700.00};

[0118] Regional variance set = {8250000.00, 7800000.00, 8600000.00, 8100000.00, 8350000.00};

[0119] The set of regional standard deviations is {2872.28, 2792.85, 2932.58, 2846.05, 2889.64}.

[0120] The above statistical results allow for a direct comparison of the distribution characteristics of bone mineral density T-scores in different regions, providing data support for the formulation of regional osteoporosis prevention and control strategies. For example, the mean and median values ​​in Region 2 are relatively low, suggesting a potential need to strengthen osteoporosis screening and health education efforts in this region.

[0121] Example 5; This example illustrates the method for determining preset parameters in a data processing system.

[0122] The process for determining the power value of age x, the power value of alkaline phosphatase y, and the optimization coefficient h is as follows:

[0123] Data collection: Collect bone mineral density data diagnosed by DXA, along with the corresponding subjects' age and alkaline phosphatase levels, to build a training dataset.

[0124] Model Construction: Establishing a mathematical model for assessing bone mineral density (BMD) T-score in relation to age and alkaline phosphatase:

[0125] Bone mineral density T-score assessment index = h × age^x + (1-h) × alkaline phosphatase^y;

[0126] Parameter optimization: Using the least squares method or gradient descent method, the parameters x, y, and h are optimized with the goal of maximizing the correlation coefficient between the evaluation index and the actual bone mineral density T value.

[0127] Validation testing: Use independent test datasets to validate the model's accuracy and generalization ability.

[0128] After training and validation with a large amount of clinical data, the optimal parameters were obtained as follows: x=3.4, y=2, h=0.0228.

[0129] This parameter combination makes the bone mineral density T-score assessment index highly correlated with the bone mineral density T-score measured by DXA, and can be used as an effective tool for initial bone mineral density screening.

[0130] This invention provides a method that does not rely on significant radiation or high-cost testing equipment, enabling bone mineral density (BMD) analysis and assessment of a population using only basic subject information and biochemical test data. This method constructs a BMD T-index assessment formula with the basic form: "BMD T-value assessment index = h * age to the power of x + (1-h) * alkaline phosphatase to the power of y, where h is a real number." It also provides a set of optimal values ​​for x, y, and h: x=3.4, y=2, and h=0.0228, making the correlation between the assessment index and the actual BMD T-value higher than that between age or alkaline phosphatase and BMD T-value. This formula improves the ability to assess population BMD T-values ​​using basic subject data and biochemical test data. Furthermore, the fact that the optimal values ​​for x and y are not equal to 1 reveals the multiple cumulative effects of age and alkaline phosphatase on BMD T-values, especially the age factor. Age is essentially time; it is not an indicator of specific physiological substances. Bone mineral density, on the other hand, is an indicator of real physiological substances. Therefore, age itself cannot affect bone mineral density arbitrarily; it must influence bone mineral density through one or more specific material factors. The results of this study indicate that aging leads to the accumulation of one or more specific material factors, and this accumulation is still affected by aging. This multiple accumulation ultimately affects the process of bone mineral density changes. This discovery inspires a deeper understanding of the mechanisms of bone mineral density changes.

[0131] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A data processing system for assessing and analyzing bone mineral density T-scores in a population, characterized in that, include: The data acquisition module is used to acquire basic information about the population and biochemical test data; The data storage module is used to store the data acquired by the data acquisition module in the form of variables into a variable library; The preset value module is used to store the preset parameters for calculating the bone mineral density T-score assessment index; The calculation module is used to calculate the bone mineral density T-value assessment index based on the basic information of the population and biochemical test data, combined with the preset calculation parameters. The statistics module is used to perform statistical analysis on the T-score assessment index of bone mineral density in the population.

2. The data processing system according to claim 1, characterized in that, The data acquisition module includes a data acquisition interface integration module, which is an interface module reserved for the population basic information acquisition device and the biochemical test data acquisition device.

3. The data processing system according to claim 1 or 2, characterized in that, The basic information of the population includes subject ID, region ID, gender, and age; the biochemical test data includes alkaline phosphatase levels.

4. The data processing system according to claim 1, characterized in that, The preset value module stores the following calculation preset parameters: age power value x, alkaline phosphatase power value y, and optimization coefficient h; the preset value module also stores the threshold value of bone mineral density T-value assessment index.

5. A data processing method for assessing and analyzing bone mineral density T-scores in a population, characterized in that, The data processing system according to any one of claims 1-4 includes the following steps: S1. Acquire basic population information and biochemical test data through the data acquisition module. The basic population information includes at least age and region number, and the biochemical test data includes at least alkaline phosphatase value. S2. Perform power calculation on the age data of the population to obtain the x-th power value of the age; S3. Perform power calculation on the alkaline phosphatase values ​​of the population to obtain the y-th power value of alkaline phosphatase. S4. Calculate the bone mineral density T-score assessment index according to the following formula: Bone mineral density T-score assessment index = h × age^x + (1-h) × alkaline phosphatase^y Where x is the power value of age, y is the power value of alkaline phosphatase, and h is the optimization coefficient, where h is a real number; S5. For populations with the same region number, calculate the bone mineral density T-value assessment index value of each subject using the calculation formula in step S4, and form a set of index values ​​for the same region. S6. Perform statistical analysis on the set of regional index values ​​obtained in step S5, calculate the regional mean, regional median, regional mode, regional variance and regional standard deviation, and obtain the set of statistical indexes for regional bone density T-value assessment. S7. Repeat steps S5 and S6 for all regions to obtain a set of statistical indicators for bone mineral density T-value assessment of each region. S8. Classify and summarize the statistical indicator sets of each region to form the regional mean set, regional median set, regional mode set, regional variance set, and regional standard deviation set.

6. The data processing method according to claim 5, characterized in that, In step S1, each subject corresponds to a data vector (p(1,r1),p(2,r2),p(3,r3),p(4,r4),p(5,r5)), where p(1,r1) is the subject number, p(2,r2) is the region number, p(3,r3) is the gender, p(4,r4) is the age, and p(5,r5) is the alkaline phosphatase value; the region number r2 is a positive integer with a value range of 1≤r2≤N, and N is the total number of regions.

7. The data processing method according to claim 5, characterized in that, In step S4, the optimization coefficient h = 0.0228; the calculated bone mineral density T-value evaluation index is represented as p(6,r6), where r6 is the index number.

8. The data processing method according to claim 6, characterized in that, In step S5, the elements of the set of index values ​​in the same region are binary pairs, in the form of (p(1,r1),p(6,r6)), which represent the subject number and the corresponding bone mineral density T-value assessment index value, respectively.

9. The data processing method according to claim 6, characterized in that, In step S6, for any region p(2,i), let the number of subjects in this region be n(i), where i is the region number and n(i)≥1, then: Regional mean (i) = p(6,j) represents the bone mineral density T-value of subject p(1,j) in region p(2,i); The regional median (i) is the median of the bone mineral density T-value assessment index for all subjects within region p(2,i); The regional mode (i) is the bone mineral density T-value that appears most frequently in region p(2,i). Regional variance (i) = p(6,j) represents the bone mineral density T-value of subject p(1,j) in region p(2,i); Regional standard deviation (i) = .

10. The data processing method according to claim 9, characterized in that, In step S8: The set of regional means = {regional mean(i)|1≤i≤N}; Regional median set={regional median(i)|1≤i≤N}; The set of regional modes = {regional mode(i) | 1 ≤ i ≤ N}; The set of regional variances = {regional variance(i)|1≤i≤N}; The set of regional standard deviations = {regional standard deviation(i)|1≤i≤N}; Where N is the total number of regions.

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