Risk analysis method and system for precocious precocious early patient

Abstract

The invention relates to the technical field of precocious puberty physiological data analysis, in particular to a precocious puberty early-stage patient risk analysis method and system. According to the method, the dimension deviation degree is obtained according to the distribution of the data of the patient in each dimension in all the data, and then similar patient groups are screened out; obtaining an individual difference degree according to the similarity and correlation between the patient and the similar patient group in each dimension; obtaining the precocious puberty tendency degree of each dimension according to the patient proportion of the similar patient group under the data in each dimension; screening out a tiny difference dimension according to the individual difference degree, and further obtaining a model error term; and obtaining a precocious puberty risk analysis regression model by using the individual difference degree, the precocious puberty tendency degree and the model error term, and performing risk analysis on the precocious puberty patient. According to the method, the calculation complexity and noise interference of a precocious puberty risk analysis regression model can be greatly reduced, and then an accurate risk analysis result is obtained.

Description

Technical Field

[0001] The present invention relates to the technical field of precocious puberty physiological data analysis, and in particular to a method and system for risk analysis of early-stage precocious puberty patients. Background Art

[0002] Precocious puberty is a common developmental disorder of puberty, referring to the early onset of sexual development in children before the normal age range, primarily manifested by the premature appearance of secondary sexual characteristics. The pathogenesis of precocious puberty is complex and may be closely linked to multiple factors, including genetics, the environment, and endocrine hormones. This condition not only severely impacts children's growth and development, mental health, and social adaptability, but may also increase the risk of other related diseases, such as decreased bone density and metabolic abnormalities. Therefore, early identification and accurate assessment of the risk of precocious puberty are crucial for disease intervention and treatment.

[0003] Conventional risk analysis methods for precocious puberty primarily rely on indicators such as age, hormone levels, and bone age, analyzing clinical indicators and examination data to assess a child's risk of precocious puberty. However, due to differences in developmental patterns across race, region, and individual, as well as individual variations in genetic background, living environment, and other factors, traditional methods are unable to comprehensively assess the impact of multiple factors and fail to fully account for individual characteristics. Furthermore, physiological indicator data often have multiple dimensions, and the impact of each indicator on the target individual may vary. Failure to perform feature selection and analysis significantly increases computational complexity and noise, compromising the stability and accuracy of risk analysis results. Summary of the Invention

[0004] In order to solve the technical problems that traditional analysis methods cannot comprehensively evaluate the multi-factor impact of patients with precocious puberty, and the multiple dimensions of physiological indicator data will greatly increase the computational complexity and noise interference, thereby affecting the accuracy of risk analysis results, the purpose of the present invention is to provide a risk analysis method and system for patients with early precocious puberty. The technical solutions adopted are as follows: A method for risk analysis of patients with early precocious puberty, comprising: Obtain physiological index data of all dimensions for all patients with precocious puberty; Arbitrarily select a patient with precocious puberty as the target patient; select any dimension for the target patient as the target dimension; obtain the degree of dimensional deviation of the target patient in the target dimension based on the data distribution of the target patient's physiological indicator data in the target dimension among all physiological indicator data; screen all other patients with precocious puberty based on the degree of dimensional deviation to obtain a similar patient group in the target dimension of the target patient; obtain the degree of individual difference of the target patient in the target dimension based on the degree of similarity between the target patient and the similar patient group in the target dimension, and the correlation between the target dimension and other dimensions; obtain the degree of precocious puberty tendency in the target dimension based on the change characteristics of the proportion of patients with precocious puberty under the physiological indicator data of the target dimension in the similar patient group; All minor difference dimensions of the target patients are screened out according to the degree of individual differences; a model error term is obtained according to the degree of individual differences of the target patients in all minor difference dimensions; a precocious puberty risk analysis regression model is obtained according to the degree of individual differences of the target patients in each dimension, the degree of precocious puberty tendency and the model error term; and a risk analysis is performed on the physiological indicator data of the precocious puberty patients according to the precocious puberty risk analysis regression model.

[0005] Furthermore, the method for obtaining the dimensional deviation degree includes: The dimensional deviation degree is obtained according to the dimensional deviation degree calculation formula, and the dimensional deviation degree calculation formula is as follows: ; Where, Indicates the degree of dimensional deviation of the target patient in the target dimension; Indicates the maximum value of the physiological indicator data of the target dimension among all physiological indicator data; Indicates the minimum value of the physiological indicator data of the target dimension among all physiological indicator data; represents the number of patients with precocious puberty; Represents the physiological indicator data of the target patient in the target dimension; Represents the average value of all physiological indicator data of the target dimension; represents the absolute value function; Represents a logarithmic function with a natural constant as its base.

[0006] Furthermore, all other patients with precocious puberty were screened according to the degree of deviation from the dimension to obtain a similar patient group to the target patient, including: Subtract the target patient's physiological index data in the target dimension from the dimension deviation degree to obtain the minimum value of the neighborhood, and add the target patient's physiological index data in the target dimension to the dimension deviation degree to obtain the maximum value of the neighborhood; Obtain the neighborhood range of the target patient's physiological indicator data in the target dimension based on the neighborhood minimum value and the domain maximum value; In the neighborhood range of the physiological indicator data of the target dimension, all other patients with precocious puberty who have the same physiological indicator data are selected, and all physiological indicator data in the neighborhood range are traversed to obtain a similar patient group consisting of all precocious puberty patients constituting the target patient.

[0007] Furthermore, the method for obtaining the degree of individual difference includes: Calculate the Pearson correlation coefficient between the target patient in the target dimension and each other dimension to obtain the correlation between the target patient in the target dimension and each other dimension, and select a preset number of other dimensions with the largest correlation as the related dimensions of the target dimension; The individual difference degree is obtained according to the individual difference degree calculation formula, and the individual difference degree calculation formula is as follows: ; Where, Indicates the degree of individual differences among target patients on the target dimension; Represents the variance of all physiological indicator data of similar patient groups in the target dimension; Indicates the preset number of related dimensions; The target patient The physiological index data of the relevant dimensions in similar patient groups Standard scores between physiological index data of relevant dimensions; The target dimension representing the target patient is The correlation coefficient between the physiological index data of the relevant dimensions; represents the sum of the correlation coefficients between the target dimension of the target patient and the physiological indicator data of each related dimension; represents the normalization function; represents the absolute value function.

[0008] Furthermore, the method for obtaining the degree of precocious puberty tendency includes: The degree of precocious puberty tendency is obtained according to the calculation formula of precocious puberty tendency, which is as follows: ; Where, Indicates the degree of precocious puberty tendency in the target dimension; Indicates the number of other physiological indicator data of the target patient within the neighborhood of the physiological indicator data of the target dimension; Represents the first physiological indicator data neighborhood within the target dimension in a similar patient group. The proportion of patients with precocious puberty under other physiological indicators; Represents the first The proportion of patients with precocious puberty under other physiological indicators; represents the absolute value function.

[0009] Furthermore, the method for obtaining the model error term includes: The dimension in which the individual difference degree of the target patients is less than the preset first threshold is regarded as a minor difference dimension; The individual differences in each small difference dimension of the target patients are amplified and averaged to obtain the model error term.

[0010] Furthermore, the method for obtaining the precocious puberty risk analysis regression model includes: The precocious puberty risk analysis regression model is obtained according to the precocious puberty risk analysis regression model formula, and the precocious puberty risk analysis regression model formula is as follows: ; Where, represents the probability of the target patient experiencing precocious puberty; Indicates the target patient's degree of precocious puberty tendency in the first dimension; Indicates the degree of individual differences among target patients in the first dimension; Indicates the target patient's tendency towards precocious puberty in the second dimension; Indicates the degree of individual differences among target patients in the second dimension; Indicates that the target patient is The degree of tendency of precocious puberty in each dimension; Indicates that the target patient is The degree of individual differences in each dimension; represents the model error term; Represents an exponential function with a natural constant as its base.

[0011] A system for analyzing the risk of patients with early precocious puberty, the system comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the above-mentioned method for analyzing the risk of patients with early precocious puberty are implemented.

[0012] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned method for risk analysis of patients with early precocious puberty.

[0013] The present invention has the following beneficial effects: The present invention obtains physiological index data of all dimensions of all patients with precocious puberty because the height, bone age, hormone level and sexual organ function of patients with precocious puberty will affect whether the patients suffer from precocious puberty; because the more special the physiological index data of the patient in each dimension is relative to all physiological index data in the dimension, the greater the difference of the physiological index data of the dimension is. In order to describe the difference, the degree of dimension deviation is first analyzed. By comparing the difference between the physiological index data of the target dimension of the target patient and precocious puberty patients with similar dimensions in other dimensions, the individual difference of the target patient in the target dimension can be described. Secondly, the similar patient group in the target dimension is obtained by analysis. Secondly, according to the similarity degree of the target patient and the similar patient group in the target dimension, and the correlation between the target dimension and other dimensions, the target patient is analyzed. The relationship is used to obtain the degree of individual differences in the target patient in the target dimension; since the magnitude of the individual difference cannot directly represent the magnitude of the risk of sexual precocious puberty, it is necessary to accurately assess the impact of the physiological indicator data of each dimension on the results of the precocious puberty risk analysis, so the degree of precocious puberty tendency in the target dimension is analyzed; the degree of precocious puberty tendency in each dimension is used as a variable to construct a regression model for precocious puberty risk analysis. Since dimensions with smaller individual differences are less important in the risk analysis, they are first removed. However, completely ignoring dimensions with smaller individual differences may cause errors in the model, so the error terms of the model are constructed using dimensions with smaller individual differences, and then a regression model for precocious puberty risk analysis is constructed; the model is used to perform risk analysis on the physiological indicator data of precocious puberty patients. The present invention can greatly reduce the computational complexity and noise interference of the regression model for precocious puberty risk analysis, thereby obtaining accurate risk analysis results. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0015] Figure 1 This is a flow chart of a method for risk analysis of patients with early precocious puberty provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0016] To further illustrate the technical means and effectiveness of the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effectiveness of a method and system for analyzing the risk of early-stage precocious puberty patients proposed by the present invention. In the following description, references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0017] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0018] The specific scheme of the risk analysis method for patients with early precocious puberty provided by the present invention is described in detail below with reference to the accompanying drawings.

[0019] See also Figure 1 , which shows a method for analyzing the risk of patients with early precocious puberty provided by one embodiment of the present invention, the method comprising: Step S1: Obtain physiological indicator data of all dimensions for all patients with precocious puberty.

[0020] This embodiment of the present invention is primarily used to analyze physiological indicator data for patients with precocious puberty, accurately assessing their physical condition. Because a patient's height, bone age, hormone levels, and sexual organ function all influence whether they experience precocious puberty, this embodiment of the present invention first obtains physiological indicator data for all precocious puberty patients in all dimensions.

[0021] In one embodiment of the present invention, the dimensions of the physiological indicator data are set to height, bone age, hormone levels and sexual organ function. It should be noted that the data dimensions can be set voluntarily and are not limited here. Since there are significant differences in the performance of precocious puberty between men and women, the subsequent operation steps divide all physiological indicator data into two categories according to males and females, and process and analyze them separately, standardize and normalize all physiological indicator data, and upload them to the data analysis system for subsequent processing. Among them, standardization and normalization operations are technical means well known to those skilled in the art and will not be described in detail here.

[0022] Step S2: Select any one patient with precocious puberty as the target patient; select any dimension for the target patient as the target dimension; obtain the dimensional deviation degree of the target patient in the target dimension based on the data distribution of the target patient's physiological indicator data in the target dimension among all physiological indicator data; screen all other patients with precocious puberty based on the dimensional deviation degree to obtain a similar patient group in the target dimension of the target patient; obtain the individual difference degree of the target patient in the target dimension based on the data similarity between the target patient and the similar patient group in the target dimension, and the correlation between the target dimension and other dimensions; obtain the degree of precocious puberty tendency in the target dimension based on the change characteristics of the proportion of patients with precocious puberty under the physiological indicator data of the target dimension in the similar patient group.

[0023] Due to differences between individual patients with precocious puberty, physiological indicator data in different dimensions may have different importance in the risk analysis of precocious puberty for different patients. For example, although bone age is an important physiological indicator for assessing the risk of precocious puberty, the bone age of some patients with precocious puberty may deviate due to genetics, nutritional conditions, or other environmental factors. Therefore, in actual situations, the physiological indicator data of the target patient in the target dimension will have a certain deviation. Since the more special the physiological indicator data of the target patient in the target dimension is relative to all physiological indicator data in the target dimension, the greater the difference in the physiological indicator data of the target patient in the target dimension. In order to describe the difference, the degree of dimensional deviation of the target patient in the target dimension is first obtained. In this embodiment of the present invention, the degree of dimensional deviation of the target patient in the target dimension is obtained based on the data distribution of the physiological indicator data of the target patient in the target dimension among all physiological indicator data.

[0024] Preferably, in one embodiment of the present invention, the method for obtaining the degree of dimensional deviation includes: The dimension deviation degree is obtained according to the dimension deviation degree calculation formula. The dimension deviation degree calculation formula is as follows: ; Where, Indicates the degree of dimensional deviation of the target patient in the target dimension; Indicates the maximum value of the physiological indicator data of the target dimension among all physiological indicator data; Indicates the minimum value of the physiological indicator data of the target dimension among all physiological indicator data; represents the number of patients with precocious puberty; Represents the physiological indicator data of the target patient in the target dimension; Represents the average value of all physiological indicator data of the target dimension; represents the absolute value function; Represents a logarithmic function with a natural constant as its base.

[0025] It should be noted that, in one embodiment of the present invention, the number of patients with precocious puberty is set to 5, and the number of patients with precocious puberty can be set arbitrarily and is not limited here.

[0026] In the dimension deviation calculation formula, the physiological index data of the target dimension is extremely poor. The larger the value, the greater the data difference between different patients with precocious puberty in the target dimension. At this time, the deviation range of the physiological indicator data of the target dimension is larger. If the target patient has similar physiological indicator data in the target dimension, the selected range is larger when screening other precocious puberty patients. The difference between the target patient's physiological indicator data in the target dimension and the average value of all physiological indicator data in the target dimension The larger the value is, the more special the physiological index data of the target patient in the target dimension is. At this time, the deviation range of the target patient in the target dimension is larger, that is, the greater the dimensional deviation degree of the target patient in the target dimension is. to zoom in or out.

[0027] By comparing the differences in physiological indicator data of the target patient with those of precocious puberty patients with similar dimensions in the target dimension, the individual differences of the target patient in the target dimension can be analyzed. Therefore, in the embodiment of the present invention, a similar patient group in the target dimension is first screened.

[0028] Preferably, in one embodiment of the present invention, all other patients with precocious puberty are screened according to the degree of dimensional deviation to obtain a patient group similar to the target patient, including: The minimum value of the neighborhood is obtained by subtracting the physiological index data of the target patient in the target dimension from the dimension deviation degree, and the maximum value of the neighborhood is obtained by adding the physiological index data of the target patient in the target dimension to the dimension deviation degree.

[0029] The neighborhood range of the target patient’s physiological index data in the target dimension is obtained according to the neighborhood minimum value and the domain maximum value, that is, the neighborhood range is .

[0030] In the neighborhood range of the physiological indicator data of the target dimension, all other patients with precocious puberty who have the same physiological indicator data are selected, and all physiological indicator data in the neighborhood range are traversed to obtain a similar patient group consisting of all precocious puberty patients constituting the target patient.

[0031] Secondly, based on the degree of data similarity between the target patient and similar patient groups in the target dimension, as well as the correlation between the target dimension and other dimensions, the degree of individual difference of the target patient in the target dimension is obtained.

[0032] Preferably, in one embodiment of the present invention, the method for obtaining the degree of individual difference includes: Calculate the Pearson correlation coefficient between the target patient in the target dimension and each other dimension to obtain the correlation between the target patient in the target dimension and each other dimension, and select a preset number of other dimensions with the largest correlation as the related dimensions of the target dimension.

[0033] The degree of individual difference is obtained according to the individual difference degree calculation formula, which is as follows: ; Where, Indicates the degree of individual differences among target patients on the target dimension; Represents the variance of all physiological indicator data of similar patient groups in the target dimension; Indicates the preset number of related dimensions; The target patient The physiological index data of the relevant dimensions in similar patient groups Standard scores between physiological index data of relevant dimensions; The target dimension representing the target patient is The correlation coefficient between the physiological index data of the relevant dimensions; represents the sum of the correlation coefficients between the target dimension of the target patient and the physiological indicator data of each related dimension; represents the normalization function; represents the absolute value function.

[0034] In the individual difference degree calculation formula, in one embodiment of the present invention, the preset number is set to 3, wherein the preset number can be set by oneself and is not limited here, and the standard score is a technical means well known to those skilled in the art and will not be described in detail here. The larger the target patient is, the more likely they are to be in the same group as similar patients. The greater the difference in physiological indicator data of the first related dimension, the greater the difference in physiological indicator data of the first related dimension; The proportion of the correlation coefficients between the physiological indicator data of the relevant dimensions to the total correlation coefficients The larger the The correlation and closeness between the first related dimension and the target dimension, at this time relevant dimensions The larger the weight, the greater the variance of all physiological indicator data of similar patient groups in the target dimension. The smaller it is, the higher the stability of the target dimension in similar patient groups. At this time, the individual differences in the target dimension of the target patients are easier to reflect, so the degree of individual differences in the target dimension of the target patients is greater.

[0035] In reality, the degree of individual differences cannot directly represent the magnitude of the risk of sexual precocious puberty. For example, high hormone levels in a similar group does not necessarily indicate a higher risk of sexual precocious puberty. It may also be a temporary phenomenon caused by environmental, dietary, and other factors. Therefore, in order to accurately assess the impact of physiological indicator data in each dimension on the results of precocious puberty risk analysis, in this embodiment of the present invention, the degree of precocious puberty tendency in the target dimension is obtained based on the changing characteristics of the proportion of patients with precocious puberty in the physiological indicator data of the target dimension in a similar patient group.

[0036] Preferably, in one embodiment of the present invention, the method for obtaining the degree of precocious puberty tendency includes: The physiological indicator data of patients with precocious puberty can be obtained directly and will not be elaborated here.

[0037] The degree of precocious puberty tendency is obtained according to the calculation formula of precocious puberty tendency. The calculation formula of precocious puberty tendency is as follows: ; Where, Indicates the degree of precocious puberty tendency in the target dimension; Indicates the number of other physiological indicator data of the target patient within the neighborhood of the physiological indicator data of the target dimension; Represents the first physiological indicator data neighborhood within the target dimension in a similar patient group. The proportion of patients with precocious puberty under other physiological indicators; Represents the first The proportion of patients with precocious puberty under other physiological indicators; represents the absolute value function.

[0038] In the calculation formula for the degree of precocious puberty tendency, the proportion of patients with precocious puberty in the neighborhood of the physiological indicator data of the target dimension in the similar patient group The larger the value is, the greater the impact of the target dimension on the onset of symptoms in patients with precocious puberty. At this time, the greater the degree of precocious puberty tendency of the target dimension is; the change rate of the proportion of patients with precocious puberty in similar patient groups within the neighborhood of the physiological indicator data of the target dimension is. The larger the value is, the higher the sensitivity of similar patient groups to changes in the physiological indicator data of the target dimension, indicating that similar patient groups are not stable under the target dimension. In this case, the degree of precocious puberty tendency in the target dimension is relatively high.

[0039] Step S3: Screen out all minor difference dimensions of the target patients based on the degree of individual differences; obtain the model error term based on the degree of individual differences of the target patients in all minor difference dimensions; obtain a precocious puberty risk analysis regression model based on the degree of individual differences of the target patients in each dimension, the degree of precocious puberty tendency and the model error term; perform risk analysis on the physiological indicator data of precocious puberty patients based on the precocious puberty risk analysis regression model.

[0040] The degree of precocious puberty tendency in each dimension is used as a variable to construct a regression model for precocious puberty risk analysis. Since dimensions with smaller individual differences are less important in risk analysis, they are first removed. However, completely ignoring dimensions with smaller individual differences may cause errors in the model. Therefore, the error terms of the model are constructed using dimensions with smaller individual differences, and then a regression model for precocious puberty risk analysis is constructed.

[0041] Preferably, in one embodiment of the present invention, the method for obtaining the model error term includes: The dimension in which the individual difference degree of the target patients is less than the preset first threshold is regarded as a slight difference dimension. In one embodiment of the present invention, the preset first threshold is set to 0.5. It should be noted that in other embodiments of the present invention, the preset first threshold can be set by itself and is not limited here.

[0042] The individual differences in each minor difference dimension of the target patient are amplified and averaged to obtain the model error term. In one embodiment of the present invention, the model error term calculation formula is as follows: ; Where, represents the model error term; Indicates the number of dimensions with minor differences in target patients; Indicates that the target patient is The degree of individual differences in a small difference dimension.

[0043] In the model error term calculation formula, the individual difference degree of each small difference dimension of the target patient is amplified by an exponential function and averaged.

[0044] Preferably, in one embodiment of the present invention, the method for obtaining a regression model for risk analysis of precocious puberty includes: The regression model for risk analysis of precocious puberty is obtained according to the regression model formula for risk analysis of precocious puberty. The regression model formula for risk analysis of precocious puberty is as follows: ; Where, represents the probability of the target patient experiencing precocious puberty; Indicates the target patient's degree of precocious puberty tendency in the first dimension; Indicates the degree of individual differences among target patients in the first dimension; Indicates the target patient's degree of precocious puberty tendency in the second dimension; Indicates the degree of individual differences among target patients in the second dimension; Indicates that the target patient is The degree of tendency of precocious puberty in each dimension; Indicates that the target patient is The degree of individual differences in each dimension; represents the model error term; Represents an exponential function with a natural constant as its base.

[0045] In one embodiment of the present invention, the precocious puberty risk analysis regression model obtained through the above-described operations is used to calculate the probability of future precocious puberty in patients with precocious puberty, and different clinical management measures are implemented based on the magnitude of the probability. If the probability value is less than 0.3, the patient is considered to be in the low-risk range and can be observed for further development. If the probability value is between 0.3 and 0.6, the patient is considered to be in the medium-risk range and further examination (such as imaging) is considered to be necessary to better assess the risk. If the probability value is greater than 0.6, the patient is considered to be in the high-risk range and timely intervention or treatment is required to prevent any subsequent adverse physical or psychological effects on the child.

[0046] At this point, the risk analysis of the physiological indicator data of patients with precocious puberty is completed.

[0047] In summary, the physiological indicator data of all dimensions of all patients with precocious puberty are obtained; any one patient with precocious puberty is selected as the target patient; any dimension of the target patient is selected as the target dimension; the dimensional deviation degree of the target patient in the target dimension is obtained according to the data distribution of the physiological indicator data of the target patient in the target dimension among all physiological indicator data; all other patients with precocious puberty are screened according to the dimensional deviation degree to obtain a similar patient group in the target dimension of the target patient; the individual difference degree of the target patient in the target dimension is obtained according to the data similarity degree between the target patient and the similar patient group in the target dimension, as well as the correlation between the target dimension and other dimensions; the degree of precocious puberty tendency in the target dimension is obtained according to the change characteristics of the proportion of patients with precocious puberty in the physiological indicator data of the target dimension in the similar patient group; all minor difference dimensions of the target patient are screened according to the individual difference degree; the model error term is obtained according to the individual difference degree of the target patient in all minor difference dimensions; the precocious puberty risk analysis regression model is obtained according to the individual difference degree of the target patient in each dimension, the precocious puberty tendency degree and the model error term; and the physiological indicator data of precocious puberty patients are subjected to risk analysis according to the precocious puberty risk analysis regression model.

[0048] One embodiment of the present invention provides a risk analysis system for patients with early precocious puberty, which includes a memory, a processor and a computer program, wherein the memory is used to store the corresponding computer program, and the processor is used to run the corresponding computer program. When the computer program runs in the processor, it can implement the method described in steps S1-S3.

[0049] The third object of an embodiment of the present invention is to provide a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the method described in steps S1-S3 is implemented when the processor executes the computer program.

[0050] A fourth object of an embodiment of the present invention is to provide a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described in steps S1-S3 is implemented.

[0051] It should be noted that the order in which the embodiments of the present invention are described above is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0052] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

Claims

1. A method for risk analysis of patients with early precocious puberty, characterized in that: The method includes: obtaining physiological indicator data of all dimensions for all patients with precocious puberty; selecting any one patient with precocious puberty as a target patient; selecting any one dimension for the target patient as a target dimension; obtaining the dimensional deviation degree of the target patient in the target dimension based on the data distribution of the physiological indicator data of the target patient in the target dimension among all physiological indicator data; screening all other patients with precocious puberty based on the dimensional deviation degree to obtain a similar patient group in the target dimension of the target patient; obtaining the individual difference degree of the target patient in the target dimension based on the data similarity degree between the target patient and the similar patient group in the target dimension, and the correlation between the target dimension and other dimensions; obtaining the precocious puberty tendency degree in the target dimension based on the change characteristics of the proportion of patients with precocious puberty in the similar patient group under the physiological indicator data of the target dimension; screening all minor difference dimensions of the target patient based on the individual difference degree; obtaining a model error term based on the individual difference degree of the target patient in all minor difference dimensions; obtaining a precocious puberty risk analysis regression model based on the individual difference degree of the target patient in each dimension, the precocious puberty tendency degree, and the model error term; and performing risk analysis on the physiological indicator data of the precocious puberty patients based on the precocious puberty risk analysis regression model.

2. The method for risk analysis of patients with early precocious puberty according to claim 1, characterized in that: The method for obtaining the dimensional deviation degree includes: obtaining the dimensional deviation degree according to a dimensional deviation degree calculation formula, and the dimensional deviation degree calculation formula is as follows: ； Where, Indicates the degree of dimensional deviation of the target patient in the target dimension; represents the number of patients with precocious puberty; Indicates the maximum value of the physiological indicator data of the target dimension among all physiological indicator data; Indicates the minimum value of the physiological indicator data of the target dimension among all physiological indicator data; Represents the physiological indicator data of the target patient in the target dimension; Represents the average value of all physiological indicator data of the target dimension; represents the absolute value function; Represents a logarithmic function with a natural constant as its base.

3. The method for risk analysis of patients with early precocious puberty according to claim 1, characterized in that: All other patients with precocious puberty are screened according to the degree of dimensional deviation to obtain a similar patient group to the target patient, including: subtracting the target patient's physiological indicator data in the target dimension from the degree of dimensional deviation to obtain a neighborhood minimum value, and adding the target patient's physiological indicator data in the target dimension to the degree of dimensional deviation to obtain a neighborhood maximum value; obtaining a neighborhood range of the target patient's physiological indicator data in the target dimension according to the neighborhood minimum value and the domain maximum value; within the neighborhood range of the physiological indicator data of the target dimension, all other patients with precocious puberty who have the same physiological indicator data as each physiological indicator data are selected, and all physiological indicator data in the neighborhood range are traversed to obtain a similar patient group to the target patient composed of all patients with precocious puberty.

4. The method for risk analysis of patients with early precocious puberty according to claim 1, characterized in that: The method for obtaining the degree of individual difference includes: calculating the Pearson correlation coefficient between the target patient in the target dimension and each other dimension, obtaining the correlation between the target patient in the target dimension and each other dimension, selecting a preset number of other dimensions with the largest correlation as the related dimensions of the target dimension; obtaining the degree of individual difference according to the individual difference degree calculation formula, the individual difference degree calculation formula is as follows: ； Where, Indicates the degree of individual differences among target patients on the target dimension; Represents the variance of all physiological indicator data of similar patient groups in the target dimension; Indicates the preset number of related dimensions; The target patient The physiological index data of the relevant dimensions in similar patient groups Standard scores between physiological index data of relevant dimensions; The target dimension representing the target patient is The correlation coefficient between the physiological index data of the relevant dimensions; represents the sum of the correlation coefficients between the target dimension of the target patient and the physiological indicator data of each related dimension; represents the normalization function; represents the absolute value function.

5. The method for risk analysis of patients with early precocious puberty according to claim 3, characterized in that: The method for obtaining the degree of precocious puberty tendency includes: obtaining the degree of precocious puberty tendency according to a calculation formula for the degree of precocious puberty tendency, wherein the calculation formula for the degree of precocious puberty tendency is as follows: ； Where, Indicates the degree of precocious puberty tendency in the target dimension; Indicates the number of other physiological indicator data of the target patient within the neighborhood of the physiological indicator data of the target dimension; Represents the first physiological indicator data neighborhood within the target dimension in a similar patient group. The proportion of patients with precocious puberty under other physiological indicators; Represents the first The proportion of patients with precocious puberty under other physiological indicators; represents the absolute value function.

6. The method for risk analysis of patients with early precocious puberty according to claim 1, characterized in that: The method for obtaining the model error term includes: taking the dimension in which the individual difference degree of the target patient is less than a preset first threshold as a small difference dimension; amplifying the individual difference degree of each small difference dimension of the target patient, and averaging to obtain the model error term.

7. The method for risk analysis of patients with early precocious puberty according to claim 1, characterized in that: The method for obtaining the precocious puberty risk analysis regression model includes: obtaining the precocious puberty risk analysis regression model according to the precocious puberty risk analysis regression model formula, and the precocious puberty risk analysis regression model formula is as follows: ； Where, represents the probability of the target patient experiencing precocious puberty; Indicates the target patient's degree of precocious puberty tendency in the first dimension; Indicates the degree of individual differences among target patients in the first dimension; Indicates the target patient's degree of precocious puberty tendency in the second dimension; Indicates the degree of individual differences among target patients in the second dimension; Indicates that the target patient is The degree of tendency of precocious puberty in each dimension; Indicates that the target patient is The degree of individual differences in each dimension; represents the model error term; Represents an exponential function with a natural constant as its base.

8. A system for analyzing the risk of patients with early precocious puberty, the system comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method for risk analysis of patients with early precocious puberty as described in any one of claims 1 to 7 are implemented.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method for risk analysis of patients with early precocious puberty as claimed in any one of claims 1 to 7 are implemented.

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