A human body aging evaluation method and system based on multiple-system aging-related indexes

Abstract

The present application relates to the field of human aging assessment, and particularly relates to a human aging assessment method and system based on multi-system aging related indexes. The method first collects multi-source heterogeneous physical examination data and performs basic scoring, divides the aging detection indexes into a core cluster and an edge cluster with primary and secondary dependency relationship, and respectively calculates the original total scores of the two clusters; compares the core original total score with a set core mutation threshold value, if the core original total score is greater than the mutation threshold value, a decompensation punishment mechanism is triggered; according to the edge original total score, a punishment amplification coefficient is calculated and weighted to the core original total score, and then combined with the edge original total score, a comprehensive aging score is obtained; finally, a human aging assessment score is calculated and grading assessment is performed accordingly. The present application breaks the defect of easy score neutralization in traditional linear assessment, the dynamic punishment mechanism truly reflects the chain reaction of accelerated aging of body function, and realizes sensitive and accurate assessment of aging risk of high-risk groups.

Description

Technical Field

[0001] This invention relates to the field of human aging assessment, specifically to a method and system for human aging assessment based on multi-system aging-related indicators. Background Technology

[0002] With the accelerating aging of the population, objective, systematic, and quantitative assessment of human aging has become a crucial need in the fields of geriatrics and health management. Human aging is not merely about increasing age; it is a comprehensive manifestation of the gradual structural and functional decline of multiple bodily systems.

[0003] Currently, the main methods for evaluating aging status fall into the following categories: first, detecting a single biomarker (such as a specific hormone or metabolic indicator); second, evaluating a single organ system (such as checking lung function or osteoporosis separately); third, subjective scoring through functional scales or questionnaires (such as sleep or cognitive scores); and fourth, conducting molecular-level detection (such as telomere length).

[0004] However, when existing assessment systems attempt to integrate these multiple dimensions for comprehensive evaluation, they generally suffer from overly simplistic and crude underlying data processing rules. Most existing multidimensional assessment systems employ parallel scoring, fixed weights, and direct addition. Under this traditional additive approach, the system treats physiologically interconnected systems (such as the cardiovascular, nervous, and skin systems) as completely independent components for isolated calculation, ignoring the chain reactions of mutual influence between different systems in the human body.

[0005] More importantly, this simple linear addition operation leads to a severe score neutralization phenomenon in the system, thus masking the true high-risk aging condition. In actual pathological patterns, when the indicators of core organs such as the heart, brain, and lungs have deteriorated to a critical point, accompanied by a decline in external manifestations (such as skin and motor function), the patient is often already in a highly risky multi-organ decompensation stage, and their physical condition will deteriorate rapidly. However, because the calculation weights of the existing system are fixed, the ordinary scores of external manifestation indicators dilute the risk scores of core organs, resulting in a falsely stable total score. In other words, the existing algorithm cannot dynamically adjust the overall deduction intensity according to the degree of deterioration of core organs, and lacks a severe penalty mechanism for malignant chain reactions. This directly leads to a slow early warning of high-risk groups in the assessment system, failing to meet the real clinical prevention needs. Summary of the Invention

[0006] To address the technical problem that existing algorithms cannot dynamically adjust the overall deduction intensity based on the deterioration of core organs, lack a severe penalty mechanism for malignant chain reactions, and thus cause the assessment system to be slow to issue early warnings for high-risk groups, the present invention aims to provide a method and system for assessing human aging based on multi-system aging-related indicators. The specific technical solution adopted is as follows:

[0007] This invention proposes a method for assessing human aging based on multi-system aging-related indicators, the method comprising:

[0008] Obtain multi-source heterogeneous physical examination data of the examinee, and analyze and map the various aging detection indicators in the multi-source heterogeneous physical examination data to obtain the basic scores of each aging detection indicator.

[0009] Each of the aforementioned aging detection indicators is divided into a core cluster and a peripheral cluster with a primary-secondary dependency relationship. The basic scores of the aging detection indicators in the core cluster and the peripheral cluster are aggregated and calculated to obtain the core raw total score and the peripheral raw total score.

[0010] Determine whether the core raw total score is greater than the set core mutation threshold: if it is greater than the core mutation threshold, calculate the penalty amplification coefficient based on the edge raw total score; if it is less than or equal to the core mutation threshold, set the penalty amplification coefficient to a value of 1; weight the penalty amplification coefficient to the core raw total score, and then combine it with the edge raw total score to calculate the comprehensive aging score;

[0011] The human aging assessment score is calculated based on the comprehensive aging score, and the aging classification assessment result is output based on the human aging assessment score.

[0012] Furthermore, the basic scores for obtaining each aging detection indicator include:

[0013] Acquire the multi-source heterogeneous physical examination data that covers multiple physiological dimensions and includes different data types and dimensions. The data types of the multi-source heterogeneous physical examination data include at least continuous numerical variables, discrete ordinal variables, and semi-structured or unstructured text data.

[0014] For each of the aforementioned aging detection indicators, specific parsing and mapping rules are invoked to clean and transform the various aging detection indicators with inconsistent dimensions, and uniformly map them to the dimensionless basic scores.

[0015] Furthermore, the specific parsing mapping rules invoked for the data types corresponding to each of the aforementioned aging detection indicators include:

[0016] For aging detection indicators that belong to the continuous numerical variables, a preset threshold comparator is called to compare them with the set pathological thresholds in intervals, and a corresponding discretized basic score is assigned according to the interval in which the comparison result falls.

[0017] For aging detection indicators that belong to structured medical image text, regular expressions are used to extract grading constants from the text, and corresponding base scores are assigned based on the extracted grading variable constants.

[0018] For subjective scales or electronic medical records containing multiple concurrent local signs, each minor sign is converted into multiple Boolean variables, the Boolean variables are summed, and a corresponding base score is assigned based on the calculated sum.

[0019] Furthermore, the step of dividing each of the aging detection indicators into core clusters and peripheral clusters with a primary-secondary dependency relationship includes:

[0020] Aging detection indicators belonging to the dimensions of the circulatory system, respiratory system, nervous system, and digestive system are extracted and classified into the core cluster;

[0021] Aging detection indicators of other physiological dimensions besides those included in the core cluster are extracted and classified into the marginal cluster.

[0022] Furthermore, obtaining the core raw total score and the marginal raw total score includes:

[0023] The base scores of each aging detection indicator in the core cluster are summed to obtain the core raw total score; and the sum of the maximum values ​​of the base scores of all aging detection indicators in the core cluster is used as the core full score threshold.

[0024] The baseline scores of each aging detection indicator in the edge cluster are summed to obtain the original total score of the edge cluster; and the sum of the maximum values ​​of the baseline scores of all aging detection indicators in the edge cluster is used as the edge full score threshold.

[0025] Furthermore, before determining whether the core original total score is greater than the set core mutation threshold, the method further includes:

[0026] Calculate the product of the core full score threshold and the preset hyperparameter, and use the product as the core mutation threshold, wherein the value range of the preset hyperparameter is set to 0.2 to 0.5.

[0027] Furthermore, the calculation of the penalty amplification factor based on the original total edge score includes:

[0028] Calculate the ratio of the original total score of the edge to the edge full score threshold, and calculate the square of the ratio. Add the squared result to the value 1 to obtain the penalty amplification coefficient.

[0029] Furthermore, the step of weighting the penalty amplification coefficient onto the core raw total score and then combining it with the marginal raw total score to calculate the comprehensive aging score includes:

[0030] The core raw total score is multiplied by the penalty amplification factor to obtain the penalty adjustment term. The penalty adjustment term is then added to the marginal raw total score to perform a summation operation. The summation result is used as the subject's comprehensive aging score.

[0031] Furthermore, the step of calculating a human aging assessment score based on the comprehensive aging score and outputting an aging grading assessment result based on the human aging assessment score includes:

[0032] The comprehensive aging score is compared with the standard full score, and the smaller value between the two is obtained by taking the minimum value function, which is used as the human aging assessment score.

[0033] The system compares the human aging assessment score with a preset aging threshold to divide the data into intervals. When the human aging assessment score is less than the preset first aging threshold, it outputs an assessment result of mild aging. When the human aging assessment score is greater than or equal to the preset first aging threshold and less than the preset second aging threshold, it outputs an assessment result of moderate aging. When the human aging assessment score is greater than or equal to the preset second aging threshold, it outputs an assessment result of severe aging.

[0034] This invention also proposes a human aging assessment system based on multi-system aging-related indicators. 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, it implements any one of the steps of a human aging assessment method based on multi-system aging-related indicators.

[0035] The present invention has the following beneficial effects:

[0036] 1. Most existing multidimensional assessment systems employ parallel scoring and linear calculation methods involving direct addition. This approach isolates and calculates mutually influencing systems, causing scores for peripheral, common aging characteristics (such as hair loss and age spots) to dilute the critical scores for core organs (such as the heart, brain, and lungs), resulting in severe score neutralization and a falsely stable final score. This invention introduces a primary-secondary dependent graph network structure, dividing aging detection indicators into key core clusters (circulatory, respiratory, nervous, and digestive systems) and peripheral buffer clusters (skin, motor, and sensory systems). This non-linear structure breaks away from the traditional one-dimensional linear array structure, establishing aging detection boundaries for different physiological systems and preventing the aging characteristics of core organs from being masked by ordinary deductions from peripheral signs.

[0037] 2. Existing systems, with their fixed weights, cannot dynamically adjust the penalty intensity based on the deterioration of core organs, lacking a mechanism to aggravate the punishment for malignant chain reactions, resulting in delayed early warnings for high-risk groups. This invention combines the principles of compensation and decompensation in clinical medicine. By setting a core mutation threshold, when the subject's core physiological system exceeds this critical point (i.e., enters the decompensation phase), the system automatically triggers a punishment mechanism, calculating a punishment amplification coefficient greater than 1. At this point, the more severe the aging and degeneration of the limbic system, the stronger the reverse impact and punishment on the core physiological system. This allows the assessment results to truly reflect the chain reaction of accelerated collapse of bodily functions, thereby accurately meeting real clinical prevention needs. Attached Figure Description

[0038] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0039] Figure 1 A flowchart of a human aging assessment method based on multi-system aging-related indicators provided in one embodiment of the present invention;

[0040] Figure 2 This is a framework diagram of a human aging assessment system based on multiple system aging-related indicators, provided as an embodiment of the present invention. Detailed Implementation

[0041] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a human aging assessment method and system based on multi-system aging-related indicators proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0042] Unless otherwise defined, 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 pertains.

[0043] The following description, in conjunction with the accompanying drawings, details the specific scheme of the human aging assessment method and system based on multi-system aging-related indicators provided by this invention.

[0044] Please see Figure 1 The diagram illustrates a flowchart of a human aging assessment method based on multi-system aging-related indicators, according to an embodiment of the present invention. The method includes:

[0045] Step S1: Collect multi-source heterogeneous physical examination data and convert and uniformly structure and map them into dimensionless basic scores.

[0046] In real-world medical examination environments, the test data generated by different physiological systems in the human body exhibit significant heterogeneity in terms of data type and dimension. For example, there are continuous numerical variables (such as hormone concentrations on lab reports), discrete ordinal variables (such as bone mineral density T-score grading), and unstructured or semi-structured text data (such as ultrasound report descriptions and questionnaires). Computers cannot directly process this heterogeneous data with inconsistent dimensions and incompatible formats.

[0047] This invention constructs an assessment table covering 10 physiological dimensions and including multiple aging detection indicators. The physiological dimensions include the skin system, digestive system, nervous system, sensory system, respiratory system, reproductive system, circulatory system, musculoskeletal system, height, and sleep. Specific aging detection indicators include, for example, chewing function related to the digestive system dimension; the distribution of skin wrinkles and age spots related to the skin system dimension; and the degree of demyelination of brain white matter related to the nervous system dimension. Furthermore, the system can dynamically call the corresponding reproductive system sub-item based on the examinee's gender. The system sets a specific parsing mapping method for each data type, mapping each aging detection indicator to a corresponding base score. The higher the base score of a certain aging detection indicator, the more severe the degree of human aging reflected by that indicator. Due to space limitations, not all aging detection indicators are listed here; only three of the most representative heterogeneous data processing procedures are illustrated.

[0048] Specifically, this includes: 1. Discretization mapping of continuous numerical variables. Continuous physiological values ​​often have normal ranges and pathological thresholds. Linear processing cannot reflect the risk jump when crossing the pathological threshold. Taking bone mineral density as an example, the data acquisition module captures the bone mineral density T value (floating-point type) generated by the dual-energy X-ray absorptiometry (DXA) instrument through a standard medical interface (such as the DICOM protocol). The central processing unit calls a preset threshold comparator. If the bone mineral density is less than or equal to the set first bone mineral density threshold (for example, the first bone mineral density threshold is preferably -2.5), it meets the pathological diagnostic threshold for osteoporosis, and the system assigns it a score of 6 points; if the bone mineral density is greater than the first bone mineral density threshold and less than the second bone mineral density threshold (for example, the first bone mineral density threshold is preferably -1.0), it is determined to be in the bone loss stage and assigned 3 points; if the bone mineral density is greater than or equal to the second bone mineral density threshold, it is determined to be normal and assigned 0 points.

[0049] 2. Feature extraction and mapping of structured text in medical images: Imaging diagnoses are mostly semi-structured hierarchical descriptions by radiologists, lacking directly calculable numerical features. Taking white matter demyelination as an example, the system receives head MRI imaging report text from the PACS system. Text parsing uses regular expressions to extract the Fazekas grading constant in the text. If the extracted grade variable is level 3, representing a severe lesion, the system assigns 7 points; level 2 assigns 5 points; level 1 assigns 3 points; and level 0 assigns 0 points.

[0050] 3. Boolean logic mapping of subjective scales and combined signs: Some local aging is not caused by a single lesion, but by the concurrent occurrence of multiple minor signs. Taking the manifestation of periorbital degeneration as an example, the system retrieves the selected records of the three physiological signs "periocular skin laxity," "corneal arcus senilis," and "conjunctival laxity" from the electronic medical records and converts them into three Boolean variables (present = 1, absent = 0). The system performs a summation operation. If the sum of the three variables is 2 or 3, it means that two or three of the three physiological signs are present, indicating extensive degeneration of the external ocular structure support, and assigns 2 points; if the sum is 1, it means that only one of the three physiological signs is present, indicating that the degeneration of the external ocular structure support is relatively mild, and assigns 1 point; if the sum is 0, it means that none of the three physiological signs are present, indicating that the degeneration of the external ocular structure support is not obvious, and assigns 0 points.

[0051] The table below lists several aging detection indicators and their scoring rules:

[0052]

[0053] Through this preprocessing step, the system cleans, transforms, and uniformly maps the underlying data from various medical devices into dimensionless basic scores, generating a one-dimensional array containing the basic scores of various aging detection indicators. This not only eliminates the dimensional differences between different types of data, but also provides physiological feature inputs with standardized format, clean data, and unified measurement for subsequent graph network architecture and nonlinear aggregation calculations.

[0054] Step S2: Divide the various aging detection indicators into core clusters and peripheral clusters with primary and secondary dependencies, and calculate the original total score and full score threshold for each.

[0055] Traditional physical examination scoring systems typically employ a one-dimensional linear array structure, treating all indicator scores equally and performing a simple linear summation. However, in real pathology, the aging of core organs such as the heart and brain has a global and life-threatening impact on the human body, while peripheral signs of aging such as hair loss, age spots, and decreased height are mostly localized functional declines. If both are summed with equal weight in the same dimension, the ordinary deductions for peripheral signs will severely dilute and mask the aging characteristics of core organs, leading to the averageization of high-risk aging. Therefore, it is necessary to break this linear relationship at the data processing level to improve the accuracy of data analysis.

[0056] Since the circulatory, nervous, respiratory, and digestive systems are the most fundamental support for maintaining the stability of the human internal environment, these four systems dominate blood pumping, oxygen exchange, command transmission, and nutrient absorption. They are the topological central nodes that determine whether widespread organ failure will occur in the human body. This invention first extracts aging detection indicators belonging to the four life dimensions of the circulatory, respiratory, nervous, and digestive systems, and classifies them into independent core clusters. Then, the basic scores of the aging detection indicators in the core clusters are summed to obtain the core original total score of the core cluster. :

[0057]

[0058] in, This represents the core original total score of the core cluster; Represents the first in the core cluster The basic score of each aging detection indicator; This indicates the number of aging detection indicators included in the core cluster.

[0059] Since each aging detection indicator has a corresponding maximum base score, such as the maximum base score of white matter demyelination in the table above being 7 points, this embodiment of the invention uses the sum of the maximum base scores of all aging detection indicators in the core cluster as the core full score threshold of the core cluster. The core full score threshold can then be used to determine whether the subject's core organs or physiological system have reached the aging threshold.

[0060] Meanwhile, the decline of other systems besides the circulatory, nervous, respiratory, and digestive systems (such as the skin system and musculoskeletal system) is usually not directly fatal. When the body's core organs are strong, they can withstand a high degree of damage without causing systemic collapse, acting as peripheral buffer nodes in the body's physiological network. In this embodiment of the invention, aging detection indicators belonging to the skin, motor, sensory, height, sleep, and gender-specific reproductive systems are classified into marginal clusters, and the basic scores of the aging detection indicators in these marginal clusters are summed to obtain the marginal raw total score of the marginal cluster. :

[0061]

[0062] in, Represents the original total score of the edge cluster; Represents the first in the edge cluster The basic score of each aging detection indicator; This indicates the number of aging detection indicators contained in the edge cluster.

[0063] Similarly, in this embodiment of the invention, the sum of the maximum values ​​of the base scores of all aging detection indicators in the edge cluster is used as the edge full score threshold of the edge cluster. Subsequently, the correlation coefficient that triggers the decompensation penalty mechanism can be calculated and analyzed by combining the edge original total score and the edge full score threshold of the edge cluster.

[0064] Thus, by dividing aging detection indicators into a graph network structure of core clusters and edge clusters with primary and secondary dependencies, this embodiment of the invention establishes the aging detection boundaries of different physiological systems, providing a computational basis for subsequently capturing the decompensation chain reaction in the human body.

[0065] Step S3: Compare the core raw total score with the set core mutation threshold to determine whether to trigger the decompensation penalty mechanism, then calculate the penalty amplification coefficient and weight it to the core raw total score, and finally combine it with the marginal raw total score to obtain the subject's comprehensive aging score.

[0066] Because of the compensatory and decompensatory laws in clinical medicine: when the core organs (such as the heart, brain, nerves, etc.) are still functioning well, the decline of the limbic system (such as severe hair loss, bone loss) has a limited impact on the overall life function (compensatory period); however, if the core organs suffer substantial damage and break through the critical point, the comprehensive aging of the limbic system will lead to the body entering the decompensatory period of accelerated deterioration of the whole body function. At this time, every decline of the peripheral limbic system will lead to a further acceleration of the aging rate and a further aggravation of the aging degree. The existing aging assessment methods based on fixed weights cannot handle this condition-triggered chain reaction, causing the aging assessment results to deviate from the actual situation of the examinee.

[0067] First, a clear aging threshold needs to be set to distinguish whether the human body is in the stage of normal physiological aging or has turned into pathological decline. In this embodiment of the invention, the product of the core cluster full score threshold and the preset hyperparameter is set as the core mutation threshold. The preset hyperparameter is set to a value range of 0.2 to 0.5. In one embodiment of the invention, the preset hyperparameter can be set to 0.3.

[0068] Then, the original core score of the core cluster calculated in step S2 is compared with the core mutation threshold. When the original core score of the core cluster is less than or equal to the core mutation threshold, it indicates that the subject's core organs or core physiological system are in a stable compensatory period and the core physiological system is functioning well. In this case, the penalty mechanism is not triggered, and the system sets the penalty amplification coefficient to a value of 1. This indicates that the deterioration of the peripheral physiological system at this time will not increase the burden on the core heart organ.

[0069] When the core raw total score of the core cluster exceeds the core mutation threshold, it indicates that the aging and degeneration of the core organ or core physiological system is relatively severe, and it is in an unstable decompensated state. The aging of the peripheral physiological system will further increase the burden on the core organ, and the aging of the human body will be further accelerated. At this time, the penalty mechanism is triggered, and the penalty amplification coefficient is calculated using the magnitude of the peripheral raw total score of the peripheral cluster relative to the peripheral full score threshold. This indicates that in the decompensated state, the more severe the aging and degeneration of the peripheral physiological system, the stronger the impact on the aging of the core physiological system and the overall aging of the subject. The calculation method of the penalty amplification coefficient is as follows:

[0070]

[0071] in, Indicates the penalty amplification factor; Represents the original total score of the edge cluster; This represents the edge full score threshold of the edge cluster.

[0072] in, The larger the value, the closer the original total score of the marginal cluster is to the marginal full score threshold, indicating that the aging and degeneration of the subject's marginal physiological system is more severe. This suggests that the aging of the marginal physiological system will have a reverse effect on the core physiological system. At the same time, the penalty amplification coefficient is calculated by squaring, so that the growth curve of this value is steeper when the aging of the marginal organs is more severe. This indicates that each time the aging of the marginal physiological system worsens, it will further accelerate the aging and degeneration of the core physiological system.

[0073] After obtaining the penalty amplification factor through the above analysis, the penalty amplification factor can be applied to the core raw total score of the core cluster in a weighted manner, and combined with the edge raw total score of the edge cluster, the comprehensive aging score of the examinee can be calculated:

[0074]

[0075] in, This indicates the subject's overall aging score; This represents the core original total score of the core cluster; Indicates the penalty amplification factor; This represents the original total score of the edge cluster.

[0076] This formula appropriately amplifies and adjusts the core original total score of the core cluster by using a penalty amplification factor. This is to adapt to the impact of the aging of the peripheral physiological system on the aging of the core physiological system when the core physiological system is in a decompensated state of pathological decline. This makes the subsequent assessment of human aging of the examinee more accurate, thereby meeting the real clinical prevention needs.

[0077] Step S4: Convert the comprehensive aging score into a standardized human aging assessment score, and conduct a final graded assessment of the subject's aging level based on the preset score range.

[0078] Clinical examination report systems typically use a standardized percentage system (0-100) for easy reading and comparison by doctors. However, the comprehensive aging score calculated through the above steps may exceed 100. When the comprehensive aging score exceeds 100, it indicates that the individual is in an extremely dangerous end-stage state of multiple organ failure. The final assessment score can be set directly to the highest score of 100, indicating that the examinee's degree of aging has been maximized. Therefore, the following formula can be used to calculate the examinee's aging assessment score:

[0079]

[0080] in, This indicates the subject's human aging assessment score; This indicates the subject's overall aging score; This represents the standard full score. ; This represents the function that takes the minimum value.

[0081] After obtaining the human aging assessment score, the aging status of the examinee's body can be graded by dividing the score into intervals. In the embodiments of the present invention, when the human aging assessment score is less than a preset first aging threshold (for example, the preset first aging threshold is set to 30), the system sets the examinee's aging level as mild aging, indicating that individual indicators have slightly declined and are highly reversible, which can be improved by adjusting lifestyle habits. When the human aging assessment score is greater than or equal to the preset first aging threshold and less than a preset second aging threshold (for example, the preset second aging threshold is set to 80), the system sets the examinee's aging level as moderate aging, indicating that the examinee's physiological system is aging relatively severely and requires timely intervention. When the human aging assessment score is greater than or equal to the preset second aging threshold, the system sets the examinee's aging level as severe aging, indicating that the examinee's multiple physiological systems have severely degenerated and the functions of multiple systems are severely imbalanced.

[0082] In other embodiments of the present invention, more thresholds can be set and more intervals can be divided to conduct a more detailed graded assessment of the aging status of the subject's body, which is not limited here.

[0083] This invention also provides a human aging assessment system based on multi-system aging-related indicators. Please refer to [link to relevant documentation]. Figure 2The diagram illustrates a framework of a human aging assessment system based on multi-system aging-related indicators provided in an embodiment of the present invention. The system may include: a memory 201, a processor 202, and a computer program stored in the memory 201 and executable on the processor 202. When the processor 202 executes the program, it implements the human aging assessment method based on multi-system aging-related indicators provided in the above embodiment.

[0084] Furthermore, the system also includes a communication interface 203 for communication between the memory 201 and the processor 202.

[0085] The memory 201 may include high-speed RAM memory, and may also include nonvolatile memory, such as at least one disk storage.

[0086] If the memory 201, processor 202, and communication interface 203 are implemented independently, then the communication interface 203, memory 201, and processor 202 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 2 A bus is represented by a single line, but this does not mean that there is only one bus or one type of bus.

[0087] Optionally, in a specific implementation, if the memory 201, processor 202, and communication interface 203 are integrated on a single chip, then the memory 201, processor 202, and communication interface 203 can communicate with each other through an internal interface.

[0088] The processor 202 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention.

[0089] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0090] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for assessing human aging based on multi-system aging-related indicators, characterized in that, The method includes: Obtain multi-source heterogeneous physical examination data of the examinee, and analyze and map the various aging detection indicators in the multi-source heterogeneous physical examination data to obtain the basic scores of each aging detection indicator. Each of the aforementioned aging detection indicators is divided into a core cluster and a peripheral cluster with a primary-secondary dependency relationship. The basic scores of the aging detection indicators in the core cluster and the peripheral cluster are aggregated and calculated to obtain the core raw total score and the peripheral raw total score. Determine whether the core raw total score is greater than the set core mutation threshold: if it is greater than the core mutation threshold, calculate the penalty amplification coefficient based on the edge raw total score; if it is less than or equal to the core mutation threshold, set the penalty amplification coefficient to a value of 1; weight the penalty amplification coefficient to the core raw total score, and then combine it with the edge raw total score to calculate the comprehensive aging score; The human aging assessment score is calculated based on the comprehensive aging score, and the aging classification assessment result is output based on the human aging assessment score.

2. The method for assessing human aging based on multi-system aging-related indicators according to claim 1, characterized in that, The basic scores obtained for each aging detection indicator include: Acquire the multi-source heterogeneous physical examination data that covers multiple physiological dimensions and includes different data types and dimensions. The data types of the multi-source heterogeneous physical examination data include at least continuous numerical variables, discrete ordinal variables, and semi-structured or unstructured text data. For each of the aforementioned aging detection indicators, specific parsing and mapping rules are invoked to clean and transform the various aging detection indicators with inconsistent dimensions, and uniformly map them to the dimensionless basic scores.

3. The method for assessing human aging based on multi-system aging-related indicators according to claim 2, characterized in that, The specific parsing mapping rules invoked for the data types corresponding to each of the aforementioned aging detection indicators include: For aging detection indicators that belong to the continuous numerical variables, a preset threshold comparator is called to compare them with the set pathological thresholds in intervals, and a corresponding discretized basic score is assigned according to the interval in which the comparison result falls. For aging detection indicators that belong to structured medical image text, regular expressions are used to extract grading constants from the text, and corresponding base scores are assigned based on the extracted grading variable constants. For subjective scales or electronic medical records containing multiple concurrent local signs, each minor sign is converted into multiple Boolean variables, the Boolean variables are summed, and a corresponding base score is assigned based on the calculated sum.

4. The method for assessing human aging based on multi-system aging-related indicators according to claim 1, characterized in that, The step of dividing the various aging detection indicators into core clusters and peripheral clusters with primary and secondary dependencies includes: Aging detection indicators belonging to the dimensions of the circulatory system, respiratory system, nervous system, and digestive system are extracted and classified into the core cluster; Aging detection indicators of other physiological dimensions besides those included in the core cluster are extracted and classified into the marginal cluster.

5. The method for assessing human aging based on multi-system aging-related indicators according to claim 1, characterized in that, The obtained core raw total score and marginal raw total score include: The base scores of each aging detection indicator in the core cluster are summed to obtain the core raw total score; and the sum of the maximum values ​​of the base scores of all aging detection indicators in the core cluster is used as the core full score threshold. The baseline scores of each aging detection indicator in the edge cluster are summed to obtain the original total score of the edge cluster; and the sum of the maximum values ​​of the baseline scores of all aging detection indicators in the edge cluster is used as the edge full score threshold.

6. The method for assessing human aging based on multi-system aging-related indicators according to claim 5, characterized in that, Before determining whether the core original total score is greater than the set core mutation threshold, the method further includes: Calculate the product of the core full score threshold and the preset hyperparameter, and use the product as the core mutation threshold, wherein the value range of the preset hyperparameter is set to 0.2 to 0.

5.

7. The method for assessing human aging based on multi-system aging-related indicators according to claim 5, characterized in that, The calculation of the penalty amplification factor based on the original edge score includes: Calculate the ratio of the original total score of the edge to the edge full score threshold, and calculate the square of the ratio. Add the squared result to the value 1 to obtain the penalty amplification coefficient.

8. The method for assessing human aging based on multi-system aging-related indicators according to claim 1, characterized in that, The step of weighting the penalty amplification coefficient onto the core raw total score and then combining it with the marginal raw total score to calculate the comprehensive aging score includes: The core raw total score is multiplied by the penalty amplification factor to obtain the penalty adjustment term. The penalty adjustment term is then added to the marginal raw total score to perform a summation operation. The summation result is used as the subject's comprehensive aging score.

9. The method for assessing human aging based on multi-system aging-related indicators according to claim 1, characterized in that, The process of calculating a human aging assessment score based on the comprehensive aging score and outputting an aging grading assessment result based on the human aging assessment score includes: The comprehensive aging score is compared with the standard full score, and the smaller value between the two is obtained by taking the minimum value function, which is used as the human aging assessment score. The system compares the human aging assessment score with a preset aging threshold to divide the data into intervals. When the human aging assessment score is less than the preset first aging threshold, it outputs an assessment result of mild aging. When the human aging assessment score is greater than or equal to the preset first aging threshold and less than the preset second aging threshold, it outputs an assessment result of moderate aging. When the human aging assessment score is greater than or equal to the preset second aging threshold, it outputs an assessment result of severe aging.

10. A human aging assessment system based on multi-system aging-related indicators, 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, it implements the steps of the method as described in any one of claims 1 to 9.

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