Early warning method and system for rheumatic immune nephropathy based on data analysis

Abstract

The present invention discloses an early warning method and system for rheumatic autoimmune nephropathy based on data analysis, comprising: dividing patients with rheumatic autoimmune nephropathy into different types of symptoms, and for any type of patient, obtaining a correlation coefficient between any item of examination data of the patient and the severity of the symptom, and obtaining a final correlation coefficient between any item of examination data of the patient that meets the symptom type, and based on the difference distance of the examination data and the difference distance of the symptom severity between patients of the type, obtaining a weight for subsequent prediction of the risk index of an unknown patient suffering from the type of symptom, and combining the examination data of the patient to be predicted and the relevant data of the patient of the type to obtain an estimated value of the risk index of the unknown patient suffering from the type of symptom, and then combining the estimated value of the single risk index or the time series changes of multiple risk indices to perform more accurate and timely warnings, thereby providing patients with more accurate medical decisions in a timely manner.

Description

Technical Field

[0001] The present invention belongs to the technical field of data processing, and relates to an early warning method and system for rheumatic immune nephropathy based on data analysis. Background Art

[0002] Rheumatic autoimmune nephropathy is a kidney-damaging disease triggered by autoimmune abnormalities. It encompasses subtypes such as systemic lupus erythematosus nephritis, anti-neutrophil cytoplasmic antibody-associated nephritis, and rheumatoid arthritis-related nephropathy. Its early symptoms are subtle and nonspecific, and by the time a diagnosis is made, patients often have already developed irreversible renal impairment or even progressed to end-stage renal disease (ESRD). Traditional diagnostic models rely on clinical symptoms and laboratory parameters (such as proteinuria and elevated serum creatinine). However, these parameters only become significantly abnormal after more than 50% of the nephrons have been damaged, resulting in a delay in intervention. Therefore, achieving early warning for rheumatic autoimmune nephropathy is an urgent issue that needs to be addressed. Summary of the Invention

[0003] The purpose of the present invention is to solve the problem in the prior art that the early symptoms of rheumatic autoimmune nephropathy are not obvious and timely intervention cannot be carried out, and to provide an early warning method and system for rheumatic autoimmune nephropathy based on data analysis.

[0004] In order to achieve the above object, the present invention adopts the following technical solutions:

[0005] The early warning method for rheumatoid arthritis based on data analysis includes:

[0006] Obtain medical examination data of patients with rheumatic immune nephropathy and classify patients with rheumatic immune nephropathy into different types according to the type of immune disease;

[0007] Based on any type of patient, normalize different examination data in the medical examination data of the patient of that type respectively;

[0008] Based on the normalized different examination data, the correlation coefficient between any examination data and the severity of the patient's rheumatism and immune disease is obtained;

[0009] The correlation coefficients of all examination data are aggregated and processed to obtain the final correlation coefficient between any examination data and the disease type;

[0010] Based on the final correlation coefficient obtained, the difference distance between any two patients of the disease type is obtained;

[0011] Based on the severity of the patient's symptoms and the difference distance, patients with this type of symptoms are obtained as reference weights for subsequent risk index estimation of unknown patients with this type of symptoms; the severity of the patient's symptoms is normalized according to the disease severity score value to obtain the patient's disease severity; the disease severity score value is data from medical examination data of patients with rheumatic autoimmune nephropathy;

[0012] Based on the reference weight and the difference distance between any two patients, a risk index of any unknown patient suffering from any rheumatoid immune nephropathy type disease is obtained;

[0013] The relationship between the risk index and the set threshold is determined. If it is greater than, it is determined that the current patient suffers from rheumatic immune nephropathy type disease.

[0014] A further improvement of the present invention is:

[0015] Furthermore, based on the normalized different examination data, the correlation coefficient between any examination data and the severity of the patient's rheumatic autoimmune disease is obtained, specifically:

[0016] ;

[0017] in, Indicates the correlation coefficient between the i-th examination data of the patient currently being analyzed and the severity of the type of disease suffered by the patient; Indicates the absolute value operator symbol; Indicates the corresponding value of the normalized examination data of the i-th item in the currently analyzed patient; Indicates the reference range of the normalized examination data of the i-th item in the currently analyzed patient; Indicates the normalized severity of the type of disease in the patient currently being analyzed; It represents the median value of the normalized reference range of the i-th item of examination data in the currently analyzed patient, that is, the reference value of the examination data; It represents the ratio of the absolute value of the difference between the corresponding value of the i-th examination data of the currently analyzed patient and the reference value of the examination data to the severity of the patient's disease. The larger the value, the higher the correlation between the amplitude change of the data and the type of disease of the patient. Otherwise, the correlation between the examination data and the type of disease of the patient is lower.

[0018] Furthermore, the correlation coefficients of all items of examination data are aggregated and processed to obtain the final correlation coefficient between any item of examination data and the type of disease, specifically: the correlation coefficients of all items of examination data of patients of this type are aggregated as a target set; the correlation coefficients of the correlation coefficient set are arranged in sequence, the median of the sorted set is selected, and the absolute value of the difference between each correlation coefficient in the set and the selected median is calculated, and the difference is used as the distance value between each correlation coefficient and the median data; based on the distance value between each correlation coefficient and the median and the corresponding numerical value of each correlation coefficient, the final correlation coefficient between any item of examination data and the type of disease is obtained.

[0019] Furthermore, based on the distance between each correlation coefficient and the median and the corresponding numerical value of each correlation coefficient, the final correlation coefficient between any examination data and the disease type is obtained, specifically:

[0020] ;

[0021] in, It represents the final correlation coefficient between the i-th examination data and the type of disease in the patients with the disease currently being analyzed; It represents the median of the sorted set of target correlation coefficients corresponding to the i-th examination data among patients with the currently analyzed disease type; Indicates the number of elements in the target correlation coefficient set corresponding to the i-th examination data among patients with the currently analyzed disease type; 、 They respectively represent the sth correlation value of the target correlation coefficient set corresponding to the i-th examination data among patients with the currently analyzed type of disease and the distance value between the correlation coefficient and the median data; c represents a constant term to prevent the denominator from being 0.

[0022] Furthermore, based on the final correlation coefficient obtained, the difference distance between any two patients of the disease type is obtained, specifically:

[0023] ;

[0024] in, Indicates the difference distance between patients of the currently analyzed disease type, patient a and patient b, represents the square root operation; n represents the number of data items examined for these two patients among the patients with the disease type currently being analyzed; Indicates the final correlation coefficient between the i-th examination data and the disease type currently being analyzed; 、 Respectively represent the normalized i-th examination data value corresponding to the patient of the currently analyzed disease type, patient a and patient b; It represents the final correlation coefficient between the i-th examination data and the disease type currently being analyzed, multiplied by the absolute value of the difference between the i-th examination data values ​​of patients A and B of the disease type currently being analyzed. If the final correlation coefficient of the examination data is larger, it means that the examination data contributes more to the diagnosis of the disease type. When the difference between the examination data of the two patients is smaller, the symptoms of the two patients are likely to be more similar, and the difference in symptoms and disease severity of the two patients is likely to be closer. It means that for patients with the type of disease currently being analyzed, the square root of the sum of the squares of the absolute value of the difference between the normalized examination data values ​​of each identical item of patient A and patient B multiplied by the product of their correlation coefficients with the type of disease is the difference distance between the two patients for the type of disease.

[0025] Furthermore, based on the severity of the patient's disease and the difference distance, the patient with this disease type is obtained as a reference weight for the subsequent risk index estimation of unknown patients with this type of disease, specifically:

[0026] ;

[0027] in, Indicates that among patients with known diseases of the type currently being analyzed, the jth patient serves as a reference weight for subsequent risk estimation of unknown patients with the disease type; 、 represents the severity of the disease of the jth and rth patients among the known patients of the disease type currently analyzed; g represents the number of patients with the disease type currently known; It represents the overall difference between the severity value of the disease type of the jth patient and the severity value of the disease type of the rest of the patients in the same type among the patients with known diseases of the type currently analyzed; It represents the sum of the difference distances between the jth patient and all other patients with the same disease type among the known patients with the disease type currently being analyzed. That is, it represents the difference distance between the patient's examination data and the examination data of other patients with the same disease type. Because the theory predicted by the model here is that the greater the difference in examination data between patients, the greater the severity of the patient's disease, so it shows a positive correlation to a certain extent.

[0028] Therefore, when The smaller the value, the The larger the value, the higher the similarity between the severity of the patient's disease and the patient's measured distance. Therefore, when predicting the disease risk index in the future, the greater the reference weight of the prediction, the more accurate the prediction result may be.

[0029] Furthermore, based on the reference weight and the difference distance between any two patients, the risk index of any unknown patient suffering from any type of rheumatic autoimmune nephropathy is obtained, specifically:

[0030] ;

[0031] in, represents the risk index of any unknown patient p suffering from this type of disease; represents the normalization function; p represents the predicted unknown patient; Represents the difference distance between the examination data of the predicted patient P and the jth patient among the current patients with the same type of disease; The risk index of the patient p predicted to have this type of disease is obtained by multiplying the sum of the difference distances between the patient p and the examination data of the patient p predicted to have this type of disease by the sum of the reference weights of the patient p predicted to have this type of disease.

[0032] Furthermore, the relationship between the risk index and the set threshold is determined. If it is greater than, it is determined that the current patient suffers from rheumatic immune nephropathy type disease, specifically:

[0033] If the risk index of the currently predicted patient for any type of classified disease is greater than the threshold T, then it means that the patient may be at risk of suffering from this type of disease, or the currently predicted patient is monitored over time, and when it is found that the disease risk index shows an upward trend over time, that is, its slope is greater than 0, it means that the patient has a trend of risk of this type of immune disease.

[0034] The early warning system for rheumatoid arthritis based on data analysis includes:

[0035] a classification module, wherein the classification module obtains medical examination data of patients with rheumatic immune nephropathy and classifies the patients with rheumatic immune nephropathy into different types according to the type of immune disease;

[0036] A normalization processing module, which performs normalization processing on different items of medical examination data of any type of patient based on the type of patient;

[0037] a first acquisition module, which acquires, based on normalized different examination data, a correlation coefficient between any one of the examination data and the severity of the patient's rheumatic autoimmune disease;

[0038] a second acquisition module, which aggregates and processes the correlation coefficients of all items of examination data to obtain a final correlation coefficient between any item of examination data and the disease type;

[0039] a difference distance acquisition module, wherein the difference distance acquisition module acquires the difference distance between any two patients of the disease type based on the acquired final correlation coefficient;

[0040] A reference weight acquisition module, which acquires, based on the patient's symptom severity and difference distance, a patient of the symptom type as a reference weight for subsequent estimation of the risk index of an unknown patient suffering from the symptom type; the patient's symptom severity is obtained by normalizing the symptom severity score value; the symptom severity score value is data from medical examination data of patients with rheumatic autoimmune nephropathy;

[0041] a risk index acquisition module, which acquires a risk index of any unknown patient suffering from any rheumatic immune nephropathy type disease based on a reference weight and a difference distance between any two patients;

[0042] The judgment module judges the relationship between the risk index and the set threshold value. If the risk index is greater than the set threshold value, it is determined that the current patient suffers from rheumatic immune nephropathy.

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

[0044] The present invention divides patients with rheumatic autoimmune nephropathy into different types of symptoms. For any type of patient, a correlation coefficient between any item of examination data of the patient and the severity of his or her symptoms is obtained, and a final correlation coefficient of any item of examination data of the patient that meets the symptom type is obtained. Based on the difference distance of the examination data and the difference distance of the severity of the symptoms between patients of this type, a weight is obtained for subsequent prediction of the risk index of an unknown patient suffering from this type of disease. The examination data of the patient to be predicted and the relevant data of the patient of this type are combined to obtain an estimated value of the risk index of the unknown patient suffering from this type of disease. Then, a single risk index estimate or a time series change of multiple risk indices is combined to provide a more accurate and timely warning, thereby providing patients with more accurate medical decisions in a timely manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0046] Figure 1 Schematic diagram of the process of the early warning method of rheumatic immune nephropathy based on data analysis of the present invention;

[0047] Figure 2 This is a schematic structural diagram of the early warning system for rheumatic immune nephropathy based on data analysis of the present invention. DETAILED DESCRIPTION

[0048] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations.

[0049] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention as claimed, but rather merely represents selected embodiments of the present invention. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without creative effort shall fall within the scope of protection of the present invention.

[0050] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings.

[0051] In the description of the embodiments of the present invention, it should be noted that if the terms "upper," "lower," "horizontal," "inner," etc. appear, the orientation or positional relationship indicated is based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship in which the inventive product is typically placed when in use. These terms are merely for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or component referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limitations on the present invention. In addition, the terms "first," "second," etc. are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0052] In addition, if the term "horizontal" appears, it does not mean that the component must be absolutely horizontal, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical", and does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0053] In the description of the embodiments of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "disposed," "installed," "connected," and "connected" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections or electrical connections; they can refer to direct connections or indirect connections through an intermediate medium; and they can refer to internal connections between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0054] The present invention is described in further detail below with reference to the accompanying drawings:

[0055] See also Figure 1 The present invention discloses an early warning method for rheumatic immune nephropathy based on data analysis, comprising:

[0056] S101, obtaining medical examination data of patients with rheumatic immune nephropathy, and classifying the patients with rheumatic immune nephropathy into different types according to the type of immune disease;

[0057] S102, based on any type of patient, normalize different items of examination data in the medical examination data of the patient of that type;

[0058] S103, based on the normalized different examination data, obtaining a correlation coefficient between any examination data and the severity of the patient's rheumatic autoimmune disease;

[0059] ;

[0060] in, Indicates the correlation coefficient between the i-th examination data of the patient currently being analyzed and the severity of the type of disease suffered by the patient; Indicates the absolute value operator symbol; Indicates the corresponding value of the normalized examination data of the i-th item in the currently analyzed patient; Indicates the reference range of the normalized examination data of the i-th item in the currently analyzed patient; Indicates the normalized severity of the type of disease in the patient currently being analyzed; It represents the median value of the normalized reference range of the i-th item of examination data in the currently analyzed patient, that is, the reference value of the examination data; It represents the ratio of the absolute value of the difference between the corresponding value of the i-th examination data of the currently analyzed patient and the reference value of the examination data to the severity of the patient's disease. The larger the value, the higher the correlation between the amplitude change of the data and the type of disease of the patient. Otherwise, the correlation between the examination data and the type of disease of the patient is lower.

[0061] S104, collecting and processing the correlation coefficients of all items of examination data to obtain the final correlation coefficient between any item of examination data and the disease type;

[0062] The correlation coefficients of all examination data of patients of this type are collected as a target set; the correlation coefficients of the correlation coefficient set are arranged in sequence, the median of the sorted set is selected, and the absolute value of the difference between each correlation coefficient in the set and the selected median is calculated, and the difference is used as the distance value between each correlation coefficient and the median data; based on the distance value between each correlation coefficient and the median and the corresponding numerical value of each correlation coefficient, the final correlation coefficient between any examination data and the disease type is obtained.

[0063] The final correlation coefficient between any one of the examination data and the disease type is obtained based on the distance between each correlation coefficient and the median and the corresponding numerical value of each correlation coefficient, specifically:

[0064] ;

[0065] in, It represents the final correlation coefficient between the i-th examination data and the type of disease in the patients with the disease currently being analyzed; It represents the median of the sorted set of target correlation coefficients corresponding to the i-th examination data among patients with the currently analyzed disease type; Indicates the number of elements in the target correlation coefficient set corresponding to the i-th examination data among patients with the currently analyzed disease type; 、 They respectively represent the sth correlation value of the target correlation coefficient set corresponding to the i-th examination data among patients with the currently analyzed type of disease and the distance value between the correlation coefficient and the median data; c represents a constant term to prevent the denominator from being 0.

[0066] S105, obtaining a difference distance between any two patients of the disease type based on the obtained final correlation coefficient;

[0067] ;

[0068] in, Indicates the difference distance between patients of the currently analyzed disease type, patient a and patient b, represents the square root operation; n represents the number of data items examined for these two patients among the patients with the disease type currently being analyzed; Indicates the final correlation coefficient between the i-th examination data and the disease type currently being analyzed; 、 Respectively represent the normalized i-th examination data value corresponding to the patient of the currently analyzed disease type, patient a and patient b; It means that in the currently analyzed disease type, the final correlation coefficient between the i-th examination data and the disease type is multiplied by the absolute value of the difference between the i-th examination data values ​​of patients A and B of the currently analyzed disease type. If the final correlation coefficient of the examination data is larger, then the contribution of the examination data to the confirmation and diagnosis of the disease type is higher. Then, when the difference between the examination data of the two patients is smaller, the symptoms of the two patients may be closer, and the difference in the symptoms and severity of the two patients may be closer. It means that for patients with the type of disease currently being analyzed, the square root of the sum of the squares of the absolute value of the difference between the normalized examination data values ​​of each identical item of patient A and patient B multiplied by the product of their correlation coefficients with the type of disease is the difference distance between the two patients for the type of disease.

[0069] S106, based on the patient's symptom severity and the difference distance, obtaining patients of the symptom type as a reference weight for subsequent estimation of the risk index of an unknown patient suffering from the symptom type; the symptom severity of the patient is obtained by normalizing the symptom severity score value; the symptom severity score value is data from medical examination data of patients with rheumatic autoimmune nephropathy;

[0070] ;

[0071] in, Indicates that among patients with known diseases of the type currently being analyzed, the jth patient serves as a reference weight for subsequent risk estimation of unknown patients with the disease type; 、 represents the severity of the disease of the jth and rth patients among the known patients of the disease type currently analyzed; g represents the number of patients with the disease type currently known; It represents the overall difference between the severity value of the disease type of the jth patient and the severity value of the disease type of the rest of the patients in the same type among the patients with known diseases of the type currently analyzed; It represents the sum of the difference distances between the jth patient and all other patients with the same disease type among the known patients with the disease type currently being analyzed. That is, it represents the difference distance between the patient's examination data and the examination data of other patients with the same disease type. Because the theory predicted by the model here is that the greater the difference in examination data between patients, the greater the severity of the patient's disease, so it shows a positive correlation to a certain extent.

[0072] Therefore, when The smaller the value, the The larger the value, the higher the similarity between the severity of the patient's disease and the patient's measured distance. Therefore, when predicting the disease risk index in the future, the greater the reference weight of the prediction, the more accurate the prediction result may be.

[0073] S107, obtaining a risk index for any unknown patient suffering from any rheumatic immune nephropathy type disease based on the reference weight and the difference distance between any two patients;

[0074] ;

[0075] in, represents the risk index of any unknown patient p suffering from this type of disease; represents the normalization function; p represents the predicted unknown patient; Represents the difference distance between the examination data of the predicted patient P and the jth patient among the current patients with the same type of disease; The risk index of the patient p predicted to have this type of disease is obtained by multiplying the sum of the difference distances between the patient p and the examination data of the patient p predicted to have this type of disease by the sum of the reference weights of the patient p predicted to have this type of disease.

[0076] S108, determining the relationship between the risk index and the set threshold value, if it is greater than, determining that the current patient suffers from rheumatic immune nephropathy type disease.

[0077] If the risk index of the currently predicted patient for any type of classified disease is greater than the threshold T, then it means that the patient may be at risk of suffering from this type of disease, or the currently predicted patient is monitored over time, and when it is found that the disease risk index shows an upward trend over time, that is, its slope is greater than 0, it means that the patient has a trend of risk of this type of immune disease.

[0078] See also Figure 2 The present invention discloses an early warning system for rheumatic immune nephropathy based on data analysis, comprising:

[0079] a classification module, wherein the classification module obtains medical examination data of patients with rheumatic immune nephropathy and classifies the patients with rheumatic immune nephropathy into different types according to the type of immune disease;

[0080] A normalization processing module, which performs normalization processing on different items of medical examination data of any type of patient based on the type of patient;

[0081] a first acquisition module, which acquires, based on normalized different examination data, a correlation coefficient between any one of the examination data and the severity of the patient's rheumatic autoimmune disease;

[0082] a second acquisition module, which aggregates and processes the correlation coefficients of all items of examination data to obtain a final correlation coefficient between any item of examination data and the disease type;

[0083] a difference distance acquisition module, wherein the difference distance acquisition module acquires the difference distance between any two patients of the disease type based on the acquired final correlation coefficient;

[0084] A reference weight acquisition module, which acquires, based on the patient's symptom severity and difference distance, a patient of the symptom type as a reference weight for subsequent estimation of the risk index of an unknown patient suffering from the symptom type; the patient's symptom severity is obtained by normalizing the symptom severity score value; the symptom severity score value is data from medical examination data of patients with rheumatic autoimmune nephropathy;

[0085] a risk index acquisition module, which acquires a risk index of any unknown patient suffering from any rheumatic immune nephropathy type disease based on a reference weight and a difference distance between any two patients;

[0086] The judgment module judges the relationship between the risk index and the set threshold value. If the risk index is greater than the set threshold value, it is determined that the current patient suffers from rheumatic immune nephropathy.

[0087] Example:

[0088] The present invention discloses an early warning method for rheumatic immune nephropathy based on data analysis, comprising:

[0089] Step 1: Use medical and other related equipment to collect relevant medical data of patients.

[0090] The patient's relevant data is collected manually and by machine, including the patient's basic information, such as name, gender, etc., and information collected clinically, such as blood routine, urine routine, renal function indicators, etc., the type of disease of patients with rheumatic autoimmune diseases, and the disease severity score.

[0091] Step 2: Divide patients with rheumatic immune nephropathy into different types according to the type of immune disease.

[0092] In order to more accurately predict rheumatic autoimmune nephropathy in patients, we first collected examination data from patients who already had rheumatic autoimmune nephropathy so that it could serve as relevant reference data for subsequent risk index analysis of rheumatic autoimmune nephropathy and disease type in patients with rheumatic autoimmune diseases. Since the data collected from patients, the impact of deviations in different examination data on their rheumatic autoimmune nephropathy and risk index may vary, and therefore, the risk index for predicting rheumatic autoimmune nephropathy for each examination data item is also different.

[0093] Patients who have already suffered from rheumatic autoimmune nephropathy are divided into different types according to the type of their immune disease, such as systemic lupus erythematosus (SLE) nephritis, anti-neutrophil cytoplasmic antibody (ANCA)-associated nephritis, etc. Then, based on any type of patient, different examination data in the medical test data of this type of patient are normalized separately.

[0094] The analysis is performed by taking any of the types as an example, for example, patients with systemic lupus erythematosus (SLE) nephritis type are analyzed at this time.

[0095] Any single data collected from this type of patient, such as white blood cells, red blood cells, etc., is normalized to avoid the differences caused by different data dimensions.

[0096] For this type, the correlation coefficient of any data item of the patient with the severity of the rheumatological autoimmune disease type of the patient can be expressed as:

[0097] ;

[0098] in, Indicates the correlation coefficient between the i-th examination data of the patient currently being analyzed and the severity of the type of disease suffered by the patient; Indicates the absolute value operator symbol; Indicates the corresponding value of the normalized examination data of the i-th item in the currently analyzed patient; Indicates the reference range of the normalized examination data of the i-th item in the currently analyzed patient; Indicates the normalized severity of the type of disease in the patient currently being analyzed; It represents the median value of the normalized reference range of the i-th item of examination data in the currently analyzed patient, that is, the reference value of the examination data; It represents the ratio of the absolute value of the difference between the corresponding value of the i-th examination data of the currently analyzed patient and the reference value of the examination data to the severity of the patient's disease. The larger the value, the higher the correlation between the amplitude change of the data and the type of disease of the patient. Otherwise, the correlation between the examination data and the type of disease of the patient is lower.

[0099] Step 3: Obtain the reference weight for estimating the risk of unknown patients with any type of disease when the patients participate in the disease.

[0100] After obtaining the correlation coefficient between any examination item and the severity of the disease for any type of patient, the correlation coefficient between the corresponding data item and the severity of the disease can be obtained for each patient of the same type. In theory, the correlation coefficients of these patients' data are relatively close. However, in order to avoid the uneven distribution of these correlation coefficients due to differences in individual attributes of a few patients, which may affect the accuracy of the final correlation coefficient determination.

[0101] Step 3.1, the correlation coefficients of all examination data are aggregated and processed to obtain the final correlation coefficient between any examination data and the disease type;

[0102] First, the correlation coefficients of any examination item data of this type of patients are taken as a target set for analysis, which is called the target correlation coefficient set.

[0103] Then the set of correlation coefficients is arranged in order, and the analysis is performed here taking ascending order as an example.

[0104] Then the median of the sorted set is selected, because the median is less susceptible to abnormal or uneven data distribution, and its accuracy as a reference is relatively high. However, in order to consider the overall situation of the examination coefficient of all current patients and combine its overall distribution, the overall correlation coefficient of the current data for this type of disease is obtained.

[0105] Therefore, the absolute value of the difference between the remaining elements in the set and the selected median is further calculated and used as the distance value between the remaining elements and the median data.

[0106] Then, the correlation coefficient between the examination data and the type of disease can be obtained by weighting, thereby reducing the impact of individual attribute peculiarities in the collected data of patients with this type of disease or deviations in the sampling data records, thereby improving the accuracy of subsequent risk index estimation and warning for kidney disease caused by this type of rheumatic autoimmune disease in unknown patients.

[0107] Therefore, for patients with this type of disease, the final correlation coefficient between the corresponding examination data and the disease type is calculated as follows:

[0108] ;

[0109] in, It represents the final correlation coefficient between the i-th examination data and the type of disease in the patients with the disease currently being analyzed; It represents the median of the sorted set of target correlation coefficients corresponding to the i-th examination data among patients with the currently analyzed disease type; Indicates the number of elements in the target correlation coefficient set corresponding to the i-th examination data among patients with the currently analyzed disease type; 、 They respectively represent the sth correlation value of the target correlation coefficient set corresponding to the i-th examination data among patients with the currently analyzed type of disease and the distance value between the correlation coefficient and the median data; c represents a constant term to prevent the denominator from being 0.

[0110] Step 3.2: Based on the final correlation coefficient obtained, the difference distance between any two patients of the disease type is obtained.

[0111] Then for patients with this disease type, the difference distance between any two patients can be expressed as:

[0112] ;

[0113] in, Indicates the difference distance between patients of the currently analyzed disease type, patient a and patient b, represents the square root operation; n represents the number of data items examined for these two patients among the patients with the disease type currently being analyzed; Indicates the final correlation coefficient between the i-th examination data and the disease type currently being analyzed; 、 Respectively represent the normalized i-th examination data value corresponding to the patient of the currently analyzed disease type, patient a and patient b; It means that in the currently analyzed disease type, the final correlation coefficient between the i-th examination data and the disease type is multiplied by the absolute value of the difference between the i-th examination data values ​​of patients A and B of the currently analyzed disease type. If the final correlation coefficient of the examination data is larger, then the contribution of the examination data to the confirmation and diagnosis of the disease type is higher. Then, when the difference between the examination data of the two patients is smaller, the symptoms of the two patients may be closer, and the difference in the symptoms and severity of the two patients may be closer. It means that for patients with the type of disease currently being analyzed, the square root of the sum of the squares of the absolute value of the difference between the normalized examination data values ​​of each identical item of patient A and patient B multiplied by the product of their correlation coefficients with the type of disease is the difference distance between the two patients for the type of disease.

[0114] Through the above, the metric distance between any two patients under any disease type can be obtained.

[0115] Step 3.3: Based on the severity of the patient's disease and the difference distance, obtain the patient with the disease type as a reference weight for subsequent risk index estimation of unknown patients with the disease type.

[0116] To calculate the risk index for an unknown patient with this type of disease, a comprehensive analysis of the distance and severity is required to obtain its weight for subsequent reference.

[0117] Therefore, for patients with this type of disease, the similarity between the severity of the disease and the patient's measured distance is used to obtain the reference weight for subsequent risk index estimation of unknown patients with this type of disease. The calculation formula is as follows:

[0118] ;

[0119] in, Indicates that among patients with known diseases of the type currently being analyzed, the jth patient serves as a reference weight for subsequent risk estimation of unknown patients with the disease type; 、 represents the severity of the disease of the jth and rth patients among the known patients of the disease type currently analyzed; g represents the number of patients with the disease type currently known; It represents the overall difference between the severity value of the disease type of the jth patient and the severity value of the disease type of the rest of the patients in the same type among the patients with known diseases of the type currently analyzed; It represents the sum of the difference distances between the jth patient and all other patients with the same disease type among the known patients with the disease type currently being analyzed. That is, it represents the difference distance between the patient's examination data and the examination data of other patients with the same disease type. Because the theory predicted by the model here is that the greater the difference in examination data between patients, the greater the severity of the patient's disease, so it shows a positive correlation to a certain extent.

[0120] Therefore, when The smaller the value, the The larger the value, the higher the similarity between the severity of the patient's disease and the patient's measured distance. Therefore, when predicting the disease risk index in the future, the greater the reference weight of the prediction, the more accurate the prediction result may be.

[0121] Step 4: Based on the reference weight and the difference distance between any two patients, obtain the risk index of any unknown patient suffering from any rheumatoid arthritis type disease.

[0122] After obtaining the reference weight for predicting that an unknown patient has the disease corresponding to a patient of any disease type, the risk index for the unknown patient with the disease type can be expressed as:

[0123] ;

[0124] in, represents the risk index of any unknown patient p suffering from this type of disease; represents the normalization function; p represents the predicted unknown patient; Represents the difference distance between the examination data of the predicted patient P and the jth patient among the current patients with the same type of disease; The risk index of the patient p predicted to have this type of disease is obtained by multiplying the sum of the difference distances between the patient p and the examination data of the patient p predicted to have this type of disease by the sum of the reference weights of the patient p predicted to have this type of disease.

[0125] Step 5: Issue early warning and reminder to the patient who is predicted to have rheumatoid immune nephropathy.

[0126] After obtaining the risk index of different types of rheumatic autoimmune diseases for different patients, a threshold T is set, which is set to 0.2 here. If the risk index of any type of disease among the classified diseases for the currently predicted patient is greater than the threshold T, then it means that the patient may be at risk of suffering from that type of disease. Alternatively, the currently predicted patient is monitored over time. If the risk index shows an upward trend over time, that is, its slope is greater than 0, then it means that the patient has a trend of risk of suffering from that type of immune disease and needs timely intervention and treatment to avoid delaying the disease and causing greater damage to the kidneys and body.

[0127] An embodiment of the present invention provides a terminal device. The terminal device of this embodiment includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of each of the aforementioned method embodiments are implemented. Alternatively, when the processor executes the computer program, the functions of each module / unit in each of the aforementioned device embodiments are implemented.

[0128] The computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to accomplish the present invention.

[0129] The terminal device may be a computing device such as a desktop computer, a notebook computer, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0130] The processor can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0131] The memory may be used to store the computer programs and / or modules, and the processor implements various functions of the terminal device by running or executing the computer programs and / or modules stored in the memory and calling the data stored in the memory.

[0132] If the module / unit integrated into the terminal device is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention can implement all or part of the process steps in the above-mentioned method embodiments by using a computer program to instruct the relevant hardware. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal, and software distribution medium. It should be noted that the content of the computer-readable medium can be appropriately increased or decreased based on the requirements of legislation and patent practice in a jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media does not include electric carrier signals and telecommunication signals.

[0133] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. An early warning system for rheumatic immune nephropathy based on data analysis, characterized by: include: a classification module, wherein the classification module obtains medical examination data of patients with rheumatic immune nephropathy and classifies the patients with rheumatic immune nephropathy into different types according to the type of immune disease; A normalization processing module, which performs normalization processing on different items of medical examination data of any type of patient based on the type of patient; a first acquisition module, which acquires, based on normalized different examination data, a correlation coefficient between any one of the examination data and the severity of the rheumatic immune nephropathy suffered by the patient; a second acquisition module, which aggregates and processes the correlation coefficients of all items of examination data to obtain a final correlation coefficient between any item of examination data and the type of rheumatic immune nephropathy; a difference distance acquisition module, wherein the difference distance acquisition module acquires the difference distance between any two patients of the rheumatic immune nephropathy type based on the acquired final correlation coefficient; A reference weight acquisition module, which obtains, based on the severity of the patient's rheumatic immune nephropathy and the difference distance, a patient with the rheumatic immune nephropathy type as a reference weight for subsequent risk index estimation of an unknown patient with the rheumatic immune nephropathy type; the severity of the patient's rheumatic immune nephropathy is obtained by normalizing the severity score of the disease; the severity score of the disease is data from medical examination data of the patient with rheumatic immune nephropathy; a risk index acquisition module, which acquires a risk index for any unknown patient suffering from any type of rheumatic immune nephropathy based on a reference weight and a difference distance between any two patients; A judgment module, wherein the judgment module judges the relationship between the risk index and a set threshold value, and if the relationship is greater than the threshold value, determines that the current patient suffers from the type of rheumatic autoimmune nephropathy; The correlation coefficient between any one of the examination data and the severity of the patient's rheumatic immune nephropathy is obtained based on the normalized examination data of different items, specifically: ； in, Indicates the correlation coefficient between the i-th examination data of the patient currently analyzed and the severity of the type of rheumatic immune nephropathy suffered by the patient; Indicates the absolute value operator symbol; Indicates the corresponding value of the normalized examination data of the i-th item in the currently analyzed patient; Indicates the reference range of the normalized examination data of the i-th item in the currently analyzed patient; Indicates the normalized severity of the type of rheumatic immune nephropathy in the patient currently analyzed; It represents the median value of the normalized reference range of the i-th item of examination data in the currently analyzed patient, that is, the reference value of the examination data; It represents the ratio of the absolute value of the difference between the corresponding value of the i-th examination data and the reference value of the examination data in the currently analyzed patient to the severity of the rheumatic immune nephropathy suffered by the patient. The larger the ratio, the higher the correlation between the amplitude change of the examination data and the type of rheumatic immune nephropathy suffered by the patient. Otherwise, the correlation between the examination data and the type of rheumatic immune nephropathy suffered by the patient is lower. The correlation coefficients of all items of examination data are aggregated and processed to obtain the final correlation coefficient between any item of examination data and the type of rheumatic immune nephropathy, specifically: the correlation coefficients of all items of examination data of patients of this type are aggregated as a target set; the correlation coefficients of the correlation coefficient set are arranged in sequence, the median of the sorted set is selected, and the absolute value of the difference between each correlation coefficient in the set and the selected median is calculated, and the difference is used as the distance value between each correlation coefficient and the median data; based on the distance value between each correlation coefficient and the median and the numerical value of each corresponding correlation coefficient, the final correlation coefficient between any item of examination data and the type of rheumatic immune nephropathy is obtained; The final correlation coefficient between any one of the examination data and the type of rheumatic immune nephropathy is obtained based on the distance between each correlation coefficient and the median and the corresponding numerical value of each correlation coefficient, specifically: ； in, It represents the final correlation coefficient between the i-th examination data and the type of rheumatic immune nephropathy in the patients with the currently analyzed type; It represents the median of the sorted set of target correlation coefficients corresponding to the i-th examination data in the patients with rheumatic immune nephropathy currently analyzed; Indicates the number of target correlation coefficient set elements corresponding to the i-th examination data in the patients with rheumatic immune nephropathy currently analyzed; 、 They respectively represent the sth correlation value after sorting the target correlation coefficient set corresponding to the i-th examination data in the patients with rheumatic immune nephropathy of the current type being analyzed, and the distance value between the correlation coefficient and the median data; c represents a constant term to prevent the denominator from being 0.

2. The early warning system for rheumatic immune nephropathy based on data analysis according to claim 1, characterized in that: The method of obtaining the difference distance between any two patients of the rheumatic immune nephropathy type based on the obtained final correlation coefficient is as follows: ； in, Indicates the difference distance between patients a and b in the currently analyzed rheumatic immune nephropathy type. represents the square root operation; n represents the number of data items examined for the two patients in the currently analyzed rheumatic immune nephropathy type patients; Indicates the final correlation coefficient between the i-th examination data and the rheumatic immune nephropathy type currently analyzed; 、 They represent the normalized data values ​​of the i-th examination item corresponding to the patients with rheumatic immune nephropathy currently analyzed, patient a and patient b; It means that for the patients with rheumatic immune nephropathy type currently being analyzed, the absolute value of the difference between the normalized examination data values ​​of each identical item between patients A and B is multiplied by the square root of the sum of the squares of the products of their correlation coefficients with the type of rheumatic immune nephropathy, which represents the difference distance between the two patients for the type of rheumatic immune nephropathy.

3. The early warning system for rheumatic immune nephropathy based on data analysis according to claim 2, characterized in that: The patient's rheumatic immune nephropathy severity and difference distance are used as reference weights for subsequent risk index estimation of unknown patients with the rheumatic immune nephropathy type, specifically: ； in, Indicates that among the patients with known rheumatic immune nephropathy of the currently analyzed type, the jth patient is used as the reference weight for subsequent risk estimation of the rheumatic immune nephropathy of unknown patients with the type; 、 represents the severity of rheumatic immune nephropathy of the jth and rth patients among the known patients of the currently analyzed type of rheumatic immune nephropathy; g represents the number of patients with the currently known type of rheumatic immune nephropathy; Indicates the overall difference between the severity value of the rheumatoid arthritis type of the jth patient among the known patients with the currently analyzed type of rheumatoid arthritis and the severity value of the rheumatoid arthritis type of the rest of the patients of the type; It represents the sum of the difference distances between the jth patient and all other patients with the type of rheumatic immune nephropathy currently being analyzed, that is, the difference distance between the examination data of this patient and the examination data of other patients with the type of rheumatic immune nephropathy.

4. The early warning system for rheumatic immune nephropathy based on data analysis according to claim 3, characterized in that: The risk index of any unknown patient suffering from any type of rheumatic autoimmune nephropathy is obtained based on the reference weight and the difference distance between any two patients, specifically: ； in, It represents the risk index of any unknown patient p suffering from this type of rheumatoid arthritis; represents the normalization function; p represents the predicted unknown patient; Represents the difference distance between the predicted patient P and the jth patient in the current rheumatic immune nephropathy patient; It represents the reference weight of each patient participating in the prediction of this type of rheumatic immune nephropathy multiplied by the sum of the difference distances between the patient and the examination data of the predicted patient p, divided by the sum of the reference weights of each patient participating in the prediction of this type of rheumatic immune nephropathy, thereby obtaining the risk index of the predicted patient p suffering from this type of rheumatic immune nephropathy.

Images