Elderly endocrine disease risk assessment method based on big data

Abstract

The invention relates to the technical field of elderly endocrine medical information management, in particular to an elderly endocrine disease risk assessment method based on big data. According to the method, all the highest similarity vectors of the target medical vectors are screened out; obtaining a basic risk factor according to the detection moment distribution and the similarity between the two; obtaining self risk factors in combination with detection moments of all medical vectors; obtaining a data change risk factor according to the number of the similar sick patients and the data difference and the detection moment difference between the medical vector and the first medical vector; and obtaining endocrine system stable factors by using the similarity between the medical vectors, combining the endocrine system stable factors to obtain dynamic risk factors, and performing senile endocrine disease risk assessment on the to-be-assessed person. According to the method, the timeliness of physiological changes and risk factors of the old people is considered, so that the risk assessment of the endocrine diseases of the old people is more accurate.

Description

Technical Field

[0001] The present invention relates to the field of endocrine medical information management for the elderly, and in particular to a risk assessment method for endocrine diseases in the elderly based on big data. Background Art

[0002] Elderly endocrine diseases refer to endocrine system diseases that develop with age, such as diabetes, hypothyroidism or hyperthyroidism, osteoporosis, etc. These diseases pose a great threat to the health of the elderly. Through disease screening and risk assessment in hospitals, potential endocrine diseases can be detected and prevented early, preventing the disease from getting worse and reducing the impact of the disease on daily life.

[0003] In the current medical field, big data technology is a commonly used and accurate disease risk assessment technology that integrates and analyzes massive amounts of medical data and extracts disease-related features and risk factors from them. However, most of the existing big data-based risk assessment methods for elderly endocrine diseases rely on static data and empirical models, and infer the patient's disease risk by matching similarities with historical patient data to find similar feature manifestations. However, this method fails to make full use of dynamic data, does not consider the timeliness of physiological changes and risk factors in the elderly population, and often ignores the correlation between data changes between patients. Therefore, the risk assessment of endocrine diseases is often not accurate enough, and the best prevention opportunity is missed. Summary of the invention

[0004] In order to solve the technical problem that conventional big data models cannot take into account the timeliness of physiological changes and risk factors of the elderly population, ignore the correlation between data changes between patients, and lead to inaccurate risk assessment of elderly endocrine diseases, the purpose of the present invention is to provide a method for risk assessment of elderly endocrine diseases based on big data, and the technical scheme adopted is as follows: a method for risk assessment of elderly endocrine diseases based on big data, the method comprising: obtaining the medical vectors of the person to be evaluated at different detection times and the first medical vector of each treated patient at different detection times; taking the medical vector of the person to be evaluated at the first detection time as the target medical vector; screening each treated patient according to the cosine similarity between the target medical vector and all the first medical vectors of each treated patient, and obtaining similar sick patients of the person to be evaluated and the highest similarity vector among all the first medical vectors of each similar sick patient ... the target medical vector and the target medical vector. The basic risk factor of the person to be evaluated is obtained based on the number of patients, the degree of similarity between the target medical vector and the highest similarity data of each similar patient, and the distribution of the detection time of the highest similarity data; the own risk factor of the person to be evaluated is obtained based on the basic risk factor and the detection time distribution of all medical vectors of the person to be evaluated; the data change risk factor of the person to be evaluated is obtained based on the number of similar patients, the degree of data difference and the degree of detection time difference between all medical vectors of the person to be evaluated and all the first medical vectors of each similar patient; the endocrine system stability factor of the person to be evaluated is obtained based on the degree of similarity between the medical vectors of each two adjacent detection moments of the person to be evaluated; the dynamic risk factor of the person to be evaluated is obtained based on the endocrine system stability factor, the own risk factor and the data change risk factor of the person to be evaluated; the risk of endocrine diseases in the elderly is evaluated for the person to be evaluated based on the dynamic risk factor.

[0005] Furthermore, the method for obtaining the highest similarity vector among all first medical vectors of similar patients with diseases of the person to be evaluated and each similar patient with diseases includes: calculating the cosine similarity between the target medical vector and all first medical vectors of each treated patient, and screening out the first medical vector with the highest cosine similarity as the highest similarity vector; when the cosine similarity between the target medical vector and the highest similarity vector is not less than a preset first threshold, taking the treated patient corresponding to the highest similarity vector as a similar patient; and taking similar patients with a history of endocrine diseases as similar patients with diseases.

[0006] Furthermore, the method for obtaining the basic risk factor includes: obtaining the basic risk factor according to the basic risk factor calculation formula, and the basic risk factor calculation formula is as follows: In the formula, Indicates the basic risk factor of the person to be assessed; represents the number of patients with similar diseases; Indicates The interval between the detection time and the illness time corresponding to the highest similarity vector of similar patients, among which The onset time of similar patients can be directly obtained; represents the target medical vector of the person to be evaluated; Indicates The highest similarity vector of similar patients; Represents the target medical vector and the The cosine similarity between the highest similarity vectors of similarly ill patients; Represents the number of similar patients.

[0007] Furthermore, the method for obtaining the self-risk factor includes: obtaining the self-risk factor according to the self-risk factor calculation formula, and the self-risk factor calculation formula is as follows: In the formula, Indicates the risk factor of the person to be assessed; represents the number of medical vectors of the person to be evaluated; Indicates The underlying risk factors of the medical vector; Indicates the last detection time of the person to be evaluated; Indicates The detection moment corresponding to the medical vector.

[0008] Furthermore, the method for obtaining the data change risk factor includes: selecting any similar patient as the target patient; in the first medical vector of the target patient, selecting the corresponding highest similarity vector of the first medical vector of the person to be evaluated as the first similarity vector; after the corresponding detection moment of the first similarity vector, selecting the corresponding highest similarity vector of the second medical vector of the person to be evaluated as the second similarity vector, until the corresponding highest similarity vector of each medical vector of the person to be evaluated is obtained; forming a first matrix with each medical vector of the person to be evaluated, and forming a second matrix with the corresponding highest similarity vector of each medical vector of the person to be evaluated in the first medical vector of each similar patient; obtaining the data change risk factor according to the data change risk factor calculation formula, the data change risk factor calculation formula is as follows: In the formula, Indicates the data change risk factor of the person to be evaluated; represents the number of patients with similar diseases; Indicates The interval between the last detection time and the time of illness of similar patients; represents each medical vector of the person to be evaluated in the first matrix; Indicates each highest similarity vector of similar diseased patients in the second matrix; represents the number of detection moments; represents the difference in detection time between every two adjacent medical vectors of the person to be evaluated; Indicates The difference in detection time between each two adjacent highest similarity vectors of similar patients; Represents the number of similar patients.

[0009] Furthermore, the method for obtaining the endocrine system stability factor includes: obtaining the endocrine system stability factor according to the endocrine system stability factor calculation formula, and the endocrine system stability factor calculation formula is as follows: In the formula, It represents the endocrine system stability factor of the person to be evaluated; represents the number of detection moments; Indicates the person to be evaluated Medical vectors; Indicates the first Medical vectors; Indicates the person to be evaluated Medical vectors and The cosine similarity between medical vectors; Represents the normalization function.

[0010] Furthermore, the method for obtaining the dynamic risk factor includes: obtaining the dynamic risk factor according to a dynamic risk factor calculation formula, and the dynamic risk factor calculation formula is as follows: In the formula, Represents the dynamic risk factor of the person to be assessed; It represents the endocrine system stability factor of the person to be evaluated; Indicates the risk factor of the person to be assessed; Indicates the data change risk factor of the person to be evaluated; Represents the normalization function.

[0011] A system for assessing the risk of endocrine diseases in the elderly based on big data. The system includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned method for assessing the risk of endocrine diseases in the elderly based on big data 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 assessing the risk of endocrine diseases in the elderly based on big data.

[0013] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned method for assessing the risk of endocrine diseases in the elderly based on big data are implemented.

[0014] The present invention has the following beneficial effects: the present invention obtains the medical vector of the person to be evaluated at different detection moments and the first medical vector of each treated patient at different detection moments; due to the different physiological functions, lifestyles, environmental conditions and other factors of different patients, which are easy to affect the pathogenesis of endocrine diseases, the first medical vector of the person to be evaluated and the treated patient is compared and analyzed to obtain the basic risk factor of the person to be evaluated; due to the significant physiological changes in the physical functions of the elderly population over time, it is necessary to combine the change characteristics of the medical data of the person to be evaluated and the data change characteristics between the person to be evaluated and other similar patients, so the self-risk factor of the person to be evaluated and the data change risk factor are analyzed; due to the instability of the changes in the endocrine system function of the elderly, which may lead to the irregular changes in their medical data, the endocrine system stability factor of the person to be evaluated is analyzed; the self-risk factor of the person to be evaluated and the data change risk factor are combined, and the dynamic risk factor of the person to be evaluated is analyzed; the risk of elderly endocrine diseases is evaluated for the person to be evaluated according to the dynamic risk factor. The present invention takes into account the timeliness of the physiological changes and risk factors of the elderly population, so that the risk assessment of elderly endocrine diseases is more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. 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 creative work.

[0016] Figure 1 A flow chart of a method for assessing the risk of endocrine diseases in the elderly based on big data provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the specific implementation method, structure, characteristics and effects of a method for risk assessment of endocrine diseases in the elderly based on big data proposed by the present invention in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0018] 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.

[0019] The following is a detailed description of a method for risk assessment of endocrine diseases in the elderly based on big data provided by the present invention in conjunction with the accompanying drawings.

[0020] See also Figure 1 , which shows a method for risk assessment of endocrine diseases in the elderly based on big data provided by an embodiment of the present invention, the method comprising: Step S1: obtaining the medical vector of the person to be assessed at different detection times and the first medical vector of each treated patient at different detection times.

[0021] The embodiment of the present invention is mainly applied to the prediction and evaluation scenario of endocrine diseases in the elderly. In order to predict whether the elderly will suffer from endocrine diseases, it is first necessary to conduct medical tests on the elderly in different dimensions, such as blood sugar, blood lipids, renal function, urine protein, thyroid, sex hormones and other dimensions, integrate the medical data of different dimensions into a feature vector as a medical vector, and take the elderly who need to be tested as the person to be evaluated. Since the physical condition of the elderly may change greatly in different periods, the medical vectors of the person to be evaluated at different testing times are obtained. Since the embodiment of the present invention is based on a big data model to analyze whether the person to be evaluated suffers from an endocrine disease, it is necessary to obtain endocrine-related medical big data of the elderly group through channels such as medical institutions, research institutions or public databases, and form a first medical vector composed of medical data of the same testing dimension of the patient who has been treated, that is, obtain the first medical vector of each treated patient at different testing times.

[0022] In one embodiment of the present invention, the detection time is set to be every week. It should be noted that in other embodiments of the present invention, the detection time can be set by itself, which is not limited here.

[0023] Step S2: taking the medical vector of the first detection moment of the person to be evaluated as the target medical vector; screening each treated patient according to the cosine similarity between the target medical vector and all the first medical vectors of each treated patient, obtaining the highest similarity vector among similar patients with the disease and all the first medical vectors of each similar patient with the disease; obtaining the basic risk factor of the person to be evaluated according to the number of similar patients with the disease, the degree of similarity between the target medical vector and the highest similarity data of each similar patient with the disease, and the distribution of the detection moments at which the highest similarity data is located; obtaining the own risk factor of the person to be evaluated according to the basic risk factor and the detection moment distribution of all the medical vectors of the person to be evaluated; obtaining the data change risk factor of the person to be evaluated according to the number of similar patients with the disease, the degree of data difference between all the medical vectors of the person to be evaluated and all the first medical vectors of each similar patient with the disease, and the degree of difference at the detection moment.

[0024] In reality, there are many factors that cause endocrine diseases in the elderly, and there are large individual differences among elderly patients. Different patients have different physiological functions, lifestyles, environmental conditions and other factors, which can easily affect the pathogenesis of endocrine diseases. Therefore, the first medical vectors of the person to be evaluated and the treated patients are compared and analyzed, that is, the highest similarity vector among all the first medical vectors of similar patients with the person to be evaluated and each similar patient with the disease is obtained, and the basic risk factor of the person to be evaluated is obtained through the difference between the medical vector of the person to be evaluated and the highest similarity vector.

[0025] Preferably, in one embodiment of the present invention, the method for obtaining the highest similarity vector among all first medical vectors of similar patients with the person to be evaluated and each similar patient with the disease includes: calculating the cosine similarity between the target medical vector and all first medical vectors of each treated patient, and screening out the first medical vector with the highest cosine similarity as the highest similarity vector.

[0026] When the cosine similarity between the target medical vector and the highest similarity vector is not less than a preset first threshold, the treated patient corresponding to the highest similarity vector is taken as a similar patient. In one embodiment of the present invention, the preset first threshold is set to 0.6. 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.

[0027] Since different disease types may exist among the treated patients, in order to analyze whether the person to be evaluated suffers from endocrine diseases, similar patients with a history of endocrine diseases are regarded as similar diseased patients, and the first medical vectors of the similar diseased patients are analyzed.

[0028] Preferably, in one embodiment of the present invention, the method for obtaining the basic risk factor includes: obtaining the basic risk factor according to a basic risk factor calculation formula, and the basic risk factor calculation formula is as follows: In the formula, Indicates the basic risk factor of the person to be assessed; represents the number of patients with similar diseases; Indicates The interval between the detection time and the illness time corresponding to the highest similarity vector of similar patients, among which The onset time of similar patients can be directly obtained; represents the target medical vector of the person to be evaluated; Indicates The highest similarity vector of similar patients; Represents the target medical vector and the The cosine similarity between the highest similarity vectors of similarly ill patients; Represents the number of similar patients.

[0029] In the basic risk factor calculation formula, The time interval between the detection time and the onset time corresponding to the highest similarity vector of similar patients The smaller the value, the more the target medical vector of the person to be evaluated is consistent with the The more similar the conditions of the first and second similar patients are, the greater the risk of the person to be assessed, and the target medical vector is The greater the cosine similarity between the highest similarity vectors of similar patients, the closer the target medical vector is to the first The first medical vector of similar patients at the time of illness is the greater the risk of the person to be evaluated; each treated patient is compared with the person to be evaluated to obtain the comprehensive risk index of the person to be evaluated; among similar patients, if the number of patients with the same endocrine disease is more, that is The larger it is, the greater the risk of the person to be assessed, that is, the greater the basic risk factor of the person to be assessed.

[0030] In reality, the body functions of the elderly population will undergo significant physiological changes over time, such as a decline in liver and kidney function and changes in drug metabolism, which can lead to changes in the risk of endocrine diseases. In addition, the elderly have poor resistance, and changes in risk factors such as lifestyle and environment can easily affect the function of their endocrine system, which can also lead to changes in the risk of endocrine diseases. Therefore, it is difficult to ensure the accuracy of the results of risk assessment of elderly endocrine diseases through static data. It is necessary to combine the characteristics of changes in the medical data of the person to be assessed and the characteristics of changes in the data between the person to be assessed and other patients with similar diseases.

[0031] Firstly, the dynamic changes of the medical vectors of the persons to be evaluated at different detection moments are analyzed to obtain the risk factors of the persons to be evaluated.

[0032] Preferably, in one embodiment of the present invention, the method for obtaining the self-risk factor includes: obtaining the self-risk factor according to the self-risk factor calculation formula, and the self-risk factor calculation formula is as follows: In the formula, Indicates the risk factor of the person to be assessed; represents the number of medical vectors of the person to be evaluated; Indicates The underlying risk factors of the medical vector; Indicates the last detection time of the person to be evaluated; Indicates The detection moment corresponding to the medical vector.

[0033] In the calculation formula of the own risk factor, The smaller the time interval between the first medical vector and the last medical vector detected, the The greater the time difference between the first medical vector and the first detection moment, the greater the dynamic change that occurs. The underlying risk factors of the medical vectors have a greater weight, that is, The larger the value, the more risk factors are determined for each medical vector.

[0034] Secondly, based on the changes in medical data between patients with similar diseases and the persons to be evaluated, the data change risk factors of the persons to be evaluated are analyzed.

[0035] Preferably, in one embodiment of the present invention, the method for obtaining data change risk factors includes: randomly selecting a similar patient as the target patient; in the first medical vector of the target patient, selecting the corresponding highest similarity vector of the first medical vector of the person to be evaluated as the first similarity vector; after the corresponding detection moment of the first similarity vector, selecting the corresponding highest similarity vector of the second medical vector of the person to be evaluated as the second similarity vector, until the corresponding highest similarity vector of each medical vector of the person to be evaluated is obtained; each medical vector of the person to be evaluated is formed into a first matrix, and the corresponding highest similarity vector of each medical vector of the person to be evaluated in the first medical vector of each similar patient is formed into a second matrix. The embodiment of the present invention provides a method for obtaining a first matrix and all second matrices, which specifically includes: 1. Recording the elderly individual to be evaluated currently collected The medical vector is The medical vectors collected are ; 2. For any first medical vector of a similar patient, record The first medical vector with the highest similarity is ; 3. From Among all the first medical vectors after the detection time, calculate The feature vector with the highest similarity is recorded as ; 4. Repeat step 3 until all first medical vectors are traversed, , , …, Combine them in order and record them as the first matrix ,Will , , …, Combine them in order and record them as the second matrix .

[0036] Repeat steps 2 to 4, starting from The second matrix is ​​constructed from the first medical vectors of similar patients. .

[0037] The data change risk factor is obtained according to the data change risk factor calculation formula. The data change risk factor calculation formula is as follows: In the formula, Indicates the data change risk factor of the person to be evaluated; represents the number of patients with similar diseases; Indicates The interval between the last detection time and the time of illness of similar patients; represents each medical vector of the person to be evaluated in the first matrix; Indicates each highest similarity vector of similar diseased patients in the second matrix; represents the number of detection moments; represents the difference in detection time between every two adjacent medical vectors of the person to be evaluated; Indicates The difference in detection time between each two adjacent highest similarity vectors of similar patients; Represents the number of similar patients.

[0038] In the data change risk factor calculation formula, The smaller the interval between the last detection moment and the moment of illness of similar patients, that is, The larger the The more likely a similar patient was ill at the last moment of testing, the greater the risk of illness for the person to be assessed. Represents the first matrix With the second matrix The Frobenius norm of the residual between the two matrices. The smaller the value, the smaller the difference between the two matrices. The more similar the medical data of the patient is to the person to be evaluated, the The greater the weight of the first medical vector of similar patients for the person to be evaluated; the matrix , The difference in detection time The smaller it is, the smaller the difference in detection time between each two adjacent vectors in the two matrices is. The closer the illness of the patient to the person to be evaluated is, the The greater the weight of the first medical vector of similar patients for the person to be evaluated; if the number of patients with the same endocrine disease is large, that is, The larger it is, the greater the risk of the person to be evaluated. At this time, the greater the data change risk factor of the person to be evaluated.

[0039] Step S3: According to the similarity between the medical vectors of each two adjacent detection moments of the person to be evaluated, the endocrine system stability factor of the person to be evaluated is obtained; according to the endocrine system stability factor, the own risk factor and the data change risk factor of the person to be evaluated, the dynamic risk factor of the person to be evaluated is obtained.

[0040] Since the changes in the endocrine system functions of the elderly are unstable, which may lead to irregular changes in their medical data, in an embodiment of the present invention, the endocrine system stability factor of the person to be evaluated is obtained based on the similarity between the medical vectors of each two adjacent detection moments of the person to be evaluated.

[0041] Preferably, in one embodiment of the present invention, the method for obtaining the endocrine system stability factor includes: obtaining the endocrine system stability factor according to the endocrine system stability factor calculation formula, and the endocrine system stability factor calculation formula is as follows: In the formula, It represents the endocrine system stability factor of the person to be evaluated; represents the number of detection moments; Indicates the person to be evaluated Medical vectors; Indicates the first Medical vectors; Indicates the person to be evaluated Medical vectors and The cosine similarity between medical vectors; Represents the normalization function.

[0042] In the calculation formula of the endocrine system stability factor, the greater the cosine similarity between each two adjacent medical vectors of the person to be evaluated, the smaller the change in the endocrine system of the person to be evaluated is. The larger it is, the greater the endocrine system stability factor of the person being evaluated.

[0043] In the above steps, the risk factors of the person to be evaluated and the data change risk factors can be obtained. Combined with the endocrine system stability factors of the person to be evaluated, they are combined and analyzed to obtain the dynamic risk factors of the person to be evaluated.

[0044] Preferably, in one embodiment of the present invention, the method for obtaining the dynamic risk factor includes: obtaining the dynamic risk factor according to a dynamic risk factor calculation formula, and the dynamic risk factor calculation formula is as follows: In the formula, Represents the dynamic risk factor of the person to be assessed; It represents the endocrine system stability factor of the person to be evaluated; Indicates the risk factor of the person to be assessed; Indicates the data change risk factor of the person to be evaluated; Represents the normalization function.

[0045] In the dynamic risk factor calculation formula, the larger the endocrine system stability factor of the person to be evaluated, that is, the stronger the stability of the endocrine system, the higher the credibility of the data change risk factor of the person to be evaluated, and the higher the weight of the data change risk factor; if the endocrine system stability factor of the person to be evaluated is smaller, that is, the weaker the stability of the endocrine system, the higher the credibility of the person to be evaluated's own risk factor, and the higher the weight of the own risk factor.

[0046] At this point, the dynamic risk factor of the person to be evaluated is obtained.

[0047] Step S4: Conduct risk assessment of geriatric endocrine diseases on the person to be assessed based on dynamic risk factors.

[0048] In one embodiment of the present invention, the preset second threshold is set to 0.5, and the preset third threshold is set to 0.7. Based on the dynamic risk factor, the endocrine disease risk of the person to be assessed is assessed. The embodiment of the present invention provides a risk assessment method, which specifically includes: , it is considered that the person to be evaluated has a certain risk of endocrine disease and needs to receive guidance from a doctor; if , it is considered that the person to be evaluated has a greater risk of endocrine disease, and it is necessary to promptly warn the patient and his or her family and have them receive appropriate treatment.

[0049] It should be noted that the preset second threshold and the preset third threshold can be set arbitrarily and are not limited here.

[0050] At this point, the risk assessment of endocrine diseases in the elderly is completed.

[0051] In summary, the medical vectors of the person to be evaluated at different detection moments and the first medical vectors of each treated patient at different detection moments are obtained; the medical vector of the first detection moment of the person to be evaluated is used as the target medical vector; according to the cosine similarity between the target medical vector and all the first medical vectors of each treated patient, each treated patient is screened to obtain similar patients with diseases of the person to be evaluated and the highest similarity vector among all the first medical vectors of each similar patient with diseases; according to the number of similar patients with diseases, the degree of similarity between the target medical vector and the highest similarity data of each similar patient with diseases, and the distribution of the detection moments of the highest similarity data, the basic risk factor of the person to be evaluated is obtained; according to the cosine similarity between the target medical vector and the highest similarity data of each similar patient with diseases, the basic risk factor of the person to be evaluated is obtained; The basic risk factors of all medical vectors of the person to be evaluated and the distribution of detection time are used to obtain the own risk factor of the person to be evaluated; according to the number of similar patients, the degree of data difference and the degree of difference at detection time between all medical vectors of the person to be evaluated and all the first medical vectors of each similar patient, the data change risk factor of the person to be evaluated is obtained; according to the similarity between the medical vectors of each two adjacent detection moments of the person to be evaluated, the endocrine system stability factor of the person to be evaluated is obtained; according to the endocrine system stability factor, the own risk factor and the data change risk factor of the person to be evaluated, the dynamic risk factor of the person to be evaluated is obtained; the risk of endocrine diseases in the elderly is evaluated for the person to be evaluated based on the dynamic risk factor.

[0052] An embodiment of the present invention provides a system for assessing the risk of endocrine diseases in the elderly based on big data, the system comprising a memory, a processor and a computer program, wherein the memory is used to store the corresponding computer program, the processor is used to run the corresponding computer program, and when the computer program runs in the processor, the method described in steps S1-S4 can be implemented.

[0053] The third objective 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 when the processor executes the computer program, the steps of the method described in the above steps S1-S4 are implemented.

[0054] A fourth objective 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 steps of the method described in the above steps S1-S4 are implemented.

[0055] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0056] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A method for assessing the risk of endocrine diseases in the elderly based on big data, characterized in that: The method comprises: obtaining the medical vectors of the person to be evaluated at different detection moments and the first medical vectors of each treated patient at different detection moments; taking the medical vector of the person to be evaluated at the first detection moment as the target medical vector; screening each treated patient according to the cosine similarity between the target medical vector and all the first medical vectors of each treated patient, obtaining the highest similarity vector among all the first medical vectors of similar patients with diseases of the person to be evaluated and each similar patient with diseases; obtaining the basic risk factor of the person to be evaluated according to the number of similar patients with diseases, the degree of similarity between the target medical vector and the highest similarity data of each similar patient with diseases, and the distribution of the detection moments at which the highest similarity data is located; and determining the basic risk factor of the person to be evaluated according to the cosine similarity between the target medical vector and all the first medical vectors of each treated patient. The basic risk factors of all medical vectors of the assessor and the distribution at the time of detection are used to obtain the risk factor of the person to be assessed; the data change risk factor of the person to be assessed is obtained according to the number of similar patients, the degree of data difference between all medical vectors of the person to be assessed and all first medical vectors of each similar patient, and the degree of difference at the time of detection; the endocrine system stability factor of the person to be assessed is obtained according to the similarity between the medical vectors of each two adjacent detection moments of the person to be assessed; the dynamic risk factor of the person to be assessed is obtained according to the endocrine system stability factor, the self-risk factor and the data change risk factor of the person to be assessed; and the risk of endocrine diseases in the elderly is assessed for the person to be assessed according to the dynamic risk factor.

2. According to the big data-based risk assessment method for endocrine diseases in the elderly according to claim 1, it is characterized in that: The method for obtaining the highest similarity vector among all first medical vectors of similar patients to be evaluated and each similar patient includes: calculating the cosine similarity between the target medical vector and all first medical vectors of each treated patient, and screening out the first medical vector with the highest cosine similarity as the highest similarity vector; when the cosine similarity between the target medical vector and the highest similarity vector is not less than a preset first threshold, taking the treated patient corresponding to the highest similarity vector as a similar patient; and taking similar patients with a history of endocrine diseases as similar patients.

3. The method for risk assessment of endocrine diseases in the elderly based on big data according to claim 2, characterized in that: The method for obtaining the basic risk factor includes: obtaining the basic risk factor according to a basic risk factor calculation formula, and the basic risk factor calculation formula is as follows: In the formula, Indicates the basic risk factor of the person to be assessed; represents the number of patients with similar diseases; Indicates The interval between the detection time and the illness time corresponding to the highest similarity vector of similar patients, where the The onset time of similar patients can be directly obtained; represents the target medical vector of the person to be evaluated; Indicates The highest similarity vector of similar patients; Represents the target medical vector and the The cosine similarity between the highest similarity vectors of similarly ill patients; Represents the number of similar patients.

4. The method for risk assessment of endocrine diseases in the elderly based on big data according to claim 1, characterized in that: The method for obtaining the self-risk factor includes: obtaining the self-risk factor according to a self-risk factor calculation formula, and the self-risk factor calculation formula is as follows: In the formula, Indicates the risk factor of the person to be assessed; represents the number of medical vectors of the person to be evaluated; Indicates The underlying risk factors of the medical vector; Indicates the last detection time of the person to be evaluated; Indicates The detection moment corresponding to the medical vector.

5. The method for risk assessment of endocrine diseases in the elderly based on big data according to claim 1, characterized in that: The method for obtaining the data change risk factor includes: selecting a similar patient as a target patient; selecting the corresponding highest similarity vector of the first medical vector of the person to be evaluated as the first similarity vector in the first medical vector of the target patient; selecting the corresponding highest similarity vector of the second medical vector of the person to be evaluated as the second similarity vector after the corresponding detection moment of the first similarity vector, until the corresponding highest similarity vector of each medical vector of the person to be evaluated is obtained; forming a first matrix with each medical vector of the person to be evaluated, and forming a second matrix with the corresponding highest similarity vector of each medical vector of the person to be evaluated in the first medical vector of each similar patient; obtaining the data change risk factor according to the data change risk factor calculation formula, and the data change risk factor calculation formula is as follows: In the formula, Indicates the data change risk factor of the person to be evaluated; represents the number of patients with similar diseases; Indicates The interval between the last detection time and the time of illness of similar patients; represents each medical vector of the person to be evaluated in the first matrix; Indicates each highest similarity vector of similar diseased patients in the second matrix; represents the number of detection moments; represents the difference in detection time between every two adjacent medical vectors of the person to be evaluated; Indicates The difference in detection time between each two adjacent highest similarity vectors of similar patients; Represents the number of similar patients.

6. The method for risk assessment of endocrine diseases in the elderly based on big data according to claim 1, characterized in that: The method for obtaining the endocrine system stability factor includes: obtaining the endocrine system stability factor according to an endocrine system stability factor calculation formula, and the endocrine system stability factor calculation formula is as follows: In the formula, It represents the endocrine system stability factor of the person to be evaluated; represents the number of detection moments; Indicates the person to be evaluated Medical vectors; Indicates the first Medical vectors; Indicates the person to be evaluated Medical vectors and The cosine similarity between medical vectors; Represents the normalization function.

7. The method for risk assessment of endocrine diseases in the elderly based on big data according to claim 1, characterized in that: The method for obtaining the dynamic risk factor includes: The dynamic risk factor is obtained according to the dynamic risk factor calculation formula, and the dynamic risk factor calculation formula is as follows: In the formula, Represents the dynamic risk factor of the person to be assessed; It represents the endocrine system stability factor of the person to be evaluated; Indicates the risk factor of the person to be assessed; Indicates the data change risk factor of the person to be evaluated; Represents the normalization function.

8. A big data-based risk assessment system for endocrine diseases in the elderly, 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 assessing the risk of endocrine diseases in the elderly based on big data are implemented as described in any one of claims 1 to 7.

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 a method for assessing the risk of endocrine diseases in the elderly based on big data as described in any one of claims 1 to 7 are implemented.

10. A computer device 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 assessing the risk of endocrine diseases in the elderly based on big data are implemented as described in any one of claims 1 to 7.

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