Child health risk quantitative evaluation method and system based on multi-modal data fusion

Abstract

This invention discloses a method and system for quantitative assessment of children's health risks based on multimodal data fusion, relating to the fields of children's growth and development and clinical health monitoring. The method includes the following steps: acquiring a health dataset of the target child, wherein the health dataset includes morphological feature vectors covering a target time window, biochemical feature sequences, and corresponding natural time parameters; performing multidimensional feature reduction processing on the biochemical feature sequences to extract maturity characterization factors reflecting tissue growth rate. This invention calculates a sensitivity correction coefficient using a logistic growth function, which can realistically simulate the nonlinear feedback mechanism of human development to metabolic deviations, achieving a more sensitive risk-weighted mapping near the developmental critical threshold, making the corrected residual quantification value more reflective of health risks under real pathological conditions.

Description

Technical Field

[0001] This invention relates to the field of children's growth and development and clinical health monitoring, specifically to a method and system for quantitative assessment of children's health risks based on multimodal data fusion. Background Technology

[0002] Child growth and development assessment is the foundation of pediatric clinical intervention and public health surveillance. Its core lies in accurately quantifying an individual's health status to identify potential developmental abnormalities. In this assessment system, alignment over time is a prerequisite for cross-sectional comparisons.

[0003] Current mainstream developmental assessment systems generally use chronological age as the sole time coordinate, directly comparing a child's single morphological indicator (such as height and weight) with a standard curve for their age. This assessment logic, based on physical time alignment, assumes that all individuals of the same age should be at the same physiological developmental stage, thus ignoring the microscopic metabolic differences in the human development process.

[0004] Although chronological age is readily available and highly standardized, in clinical practice, a child's unique physiological maturation rhythm is determined by a complex interplay of factors, including individual genetic characteristics, endocrine levels, and environmental exposures. When this intrinsic maturation rhythm deviates from external physical timelines—such as the clinically common phenomenon of "early growth" or "late growth"—the rigid timeline based on chronological age fails to accurately reflect the individual's physiological stage. Insisting on using such misaligned comparisons as the basis for risk assessment inevitably leads to misinterpreting atypical growth fluctuations as pathological deviations or masking existing metabolic abnormalities, resulting in high rates of misdiagnosis and missed diagnosis in clinical evaluation. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method and system for quantitative assessment of children's health risks based on multimodal data fusion.

[0006] To achieve the above objectives, the technical solution of the present invention is as follows:

[0007] A method for quantitative assessment of children's health risks based on multimodal data fusion includes the following steps:

[0008] Obtain the health dataset of the target child, which includes morphological feature vectors, biochemical feature sequences, and corresponding natural time parameters covering the target time window;

[0009] Multidimensional feature reduction was performed on the biochemical feature sequences to extract maturity characterization factors that reflect tissue growth rate;

[0010] The natural time parameters are weighted and compensated based on the maturity characterization factors to generate the physiological offset step size. The natural time parameters are then superimposed and corrected based on the physiological offset step size to obtain the corrected time parameters.

[0011] Based on the corrected time parameter, the corresponding baseline morphological reference vector is retrieved from the preset standard developmental benchmark database, and the spatial distance between the morphological feature vector and the baseline morphological reference vector is calculated to obtain the initial residual value.

[0012] The sensitivity correction coefficient is calculated using the maturity characterization factor, and the sensitivity correction coefficient is weighted and mapped with the initial residual value to obtain the corrected residual quantification value. The sensitivity correction coefficient is a weighted value used to measure the strength of the system's response to individual biochemical metabolic abnormalities.

[0013] Based on the corrected residual quantization value, match the corresponding health risk level and output it.

[0014] A quantitative assessment system for children's health risks based on multimodal data fusion, comprising:

[0015] The data acquisition module is used to acquire the health dataset of the target child. The health dataset includes morphological feature vectors, biochemical feature sequences, and corresponding natural time parameters covering the target time window.

[0016] The feature extraction module is used to perform multidimensional feature reduction on biochemical feature sequences and extract maturity characterization factors that reflect tissue growth rate.

[0017] The time parameter correction module is used to calculate the weight compensation of the natural time parameter based on the maturity characterization factor, generate the physiological offset step size, and perform superposition correction on the natural time parameter based on the physiological offset step size to obtain the corrected time parameter.

[0018] The initial residual calculation module is used to retrieve the corresponding baseline morphological reference vector from the preset standard developmental benchmark database based on the correction time parameter, and calculate the spatial distance between the morphological feature vector and the baseline morphological reference vector to obtain the initial residual value.

[0019] The residual correction quantization module is used to calculate the sensitivity correction coefficient using the maturity characterization factor, and to perform a weighted mapping between the sensitivity correction coefficient and the initial residual value to obtain the corrected residual quantization value. The sensitivity correction coefficient is a weighted value used to measure the strength of the system's response to individual biochemical metabolic abnormalities.

[0020] The risk assessment and output module is used to match the corresponding health risk level and output it based on the corrected residual quantification value.

[0021] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0022] This invention calculates the sensitivity correction coefficient using a logical growth function, which can realistically simulate the nonlinear feedback mechanism of human development to metabolic deviations. It achieves a more sensitive risk-weighted mapping near the critical developmental threshold, making the corrected residual quantification value more reflective of health risks under real pathological conditions. This effectively avoids the existing technology from using misaligned comparison results as the basis for risk judgment, which could lead to health fluctuations in atypical growth cycles being mistaken for pathological deviations or masking real metabolic abnormalities. This effectively reduces the misdiagnosis and missed diagnosis rates in clinical assessment. Attached Figure Description

[0023] The disclosure of this invention is illustrated with reference to the accompanying drawings. It should be understood that the drawings are for illustrative purposes only and are not intended to limit the scope of protection of this invention. In the drawings, the same reference numerals are used to refer to the same parts. Wherein:

[0024] Figure 1 This is a diagram illustrating the method steps of the present invention;

[0025] Figure 2 This is a flowchart of the present invention. Detailed Implementation

[0026] It is readily understood that, based on the technical solution of this invention, those skilled in the art can propose various interchangeable structural methods and implementations without altering the essential spirit of the invention. Therefore, the following detailed embodiments and accompanying drawings are merely illustrative examples of the technical solution of this invention and should not be considered as the entirety of the invention or as limitations or restrictions on the technical solution of this invention.

[0027] Application Overview:

[0028] In the field of children's growth and development and clinical health monitoring, especially in the assessment of morphology and physical condition across age groups, the dynamic evolution trajectory of various physiological indicators over time is regarded as a key benchmark for measuring an individual's health status. This high-precision quantification of health risk is essentially a process of spatiotemporal alignment of multimodal vital signs at the biomedical level. That is, by measuring the activity of microscopic biochemical indicators such as endocrine and metabolism, combined with the cumulative amount of macroscopic morphology such as bones and muscles, the time span of natural development is mapped without loss to the individual's actual physiological maturation progress, thereby establishing a benchmark coordinate with specific developmental rhythm in the assessment system.

[0029] However, existing technologies lack a dynamic correction mechanism for the consistency of temporal mapping between multimodal biochemical evolution trends and macroscopic morphological development. This leads to the inability to accurately identify "false positive" misjudgments and "false negative" missed diagnoses in children in atypical growth cycles when aligned with a single natural age. False positive misjudgments occur when the system accurately collects morphological indicators but is limited by rigid physical time coordinates, mechanically classifying normal physiological developmental delays (i.e., "late growth" in healthy individuals) as pathological developmental retardation. False negative missed diagnoses occur when the system relies solely on apparent height and weight attainment, failing to detect compensatory precocious puberty caused by abnormal endocrine hyperactivity (i.e., pathological "early growth"), thus masking microscopic metabolic risks with macroscopic appearances. Consequently, a strict nonlinear physical correspondence cannot be established between biochemical developmental dynamics and natural physical time, resulting in misjudgments or ambiguous feedback from the system regarding individual developmental residuals, thereby affecting the accurate quantification of health risk levels and the targeted nature of clinical interventions.

[0030] For example, in routine screening scenarios at medical institutions or school physical examinations, when children are assessed using multidimensional indicators, traditional evaluation systems can only compare the absolute distribution of an individual's morphological indicators among their peers using standard growth curves, but cannot distinguish whether short-term morphological lag is accompanied by biochemical energy reserves. Furthermore, when some children experience endocrine hyperactivity due to overnutrition or environmental exposure, the system only records the superficial appearance of their morphological signs being in the dominant range, failing to detect that the maturity factors characterizing an individual's real-time growth momentum have seriously deviated from the mean and that future growth potential has been overdrawn. Specifically, the system misjudges normal physiological late growth as pathological short stature, triggering a high-risk warning, or incorrectly classifies hidden metabolic abnormalities and precocious puberty as excellent development. This leads to parents and doctors perpetuating erroneous intervention patterns, failing to form a scientific monitoring mechanism that conforms to the individual's biological developmental rhythm.

[0031] If the above problems are not addressed, the health monitoring system will continue to lose its ability to objectively determine the true physiological maturity of children. Specifically, the failure to identify false positives will cause the assessment system to issue frequent false alarms, leading parents to blindly seek unnecessary hormone or drug interventions, thereby disrupting the child's normal endocrine homeostasis. Simultaneously, the failure to correct false negatives will cause hidden metabolic abnormalities to continue to worsen under the illusion of "good development," leading to premature closure of the epiphyseal plates or a surge in the risk of long-term metabolic diseases, ultimately causing children to lose their expected final height and health. Therefore, inaccurate assessment feedback will systematically hinder the early and accurate identification of deep-seated risks to growth and development in clinical practice, seriously affecting the achievement of children's life-cycle health management goals.

[0032] Therefore, as attached Figure 1 and attached Figure 2 As shown, a method for quantitative assessment of children's health risks based on multimodal data fusion is proposed, including the following steps:

[0033] Step 1: Obtain the target children's health dataset. This dataset includes morphological feature vectors covering the target time window, biochemical feature sequences, corresponding natural time parameters, and the target children's age and gender attributes. The system first obtains a multidimensional health dataset of the target children under the same timestamp or a very short effective time window. This dataset breaks through the limitations of single indicators, integrating morphological feature vectors representing macroscopic physical characteristics, such as height, weight, and body mass index; biochemical feature sequences representing the evolution of microscopic endocrine and metabolic states; and natural time parameters recording the physical age at the time of measurement. This provides complete underlying data support for subsequent cross-validation in both time and space dimensions.

[0034] Step Two: Perform multidimensional feature reduction processing on the biochemical feature sequence to extract a maturity characterization factor reflecting tissue growth rate. Due to the high dimensionality of the original biochemical feature sequence and the complex coupling relationships between indicators, the system does not directly use the messy biochemical concentration values. Instead, it performs multidimensional feature reduction processing on the sequence. By stripping away redundant information, the system accurately extracts a core indicator that comprehensively reflects the current tissue growth momentum of the target child from the dynamic biochemical change trend, namely the "maturity characterization factor." This factor is a quantitative anchor point for understanding the child's developmental rate from a biochemical perspective.

[0035] Step 3: The system performs weighted compensation calculations on the natural time parameters based on maturity characterization factors to generate a physiological offset step size. This physiological offset step size is then used to superimpose and correct the natural time parameters, resulting in corrected time parameters. The system uses the extracted maturity characterization factors to intervene in the natural time parameters. Internally, through weighted compensation calculations, the system assesses the deviation of this maturity factor from the age-standard level and converts this biochemical deviation into a physiological offset step size measured in time. Subsequently, the system directly superimposes this physiological offset step size onto the target child's original natural time parameters. This step essentially adjusts the child's calendar age to a physiological age that aligns with their actual developmental progress (i.e., corrected time parameters), thereby completely filtering out temporal noise caused by individual "early" or "late" development.

[0036] Step 4: Based on the corrected time parameter, the system retrieves the corresponding baseline morphological reference vector from the preset standard developmental benchmark database and calculates the spatial distance between the morphological feature vector and the baseline morphological reference vector to obtain the initial residual value. After reconstructing the time coordinates, the system uses the corrected time parameter as the new retrieval key to delve into the preset standard developmental benchmark database and accurately retrieves the baseline morphological reference vector that is strictly aligned with the physiological age. At this point, the system places the target child's actual morphological feature vector and the baseline morphological reference vector in the same multidimensional space. By measuring the multidimensional spatial distance between the two vectors, it obtains the initial residual value that objectively represents the absolute difference in physical development between the two. This spatial ranging method provides a more global perspective than the traditional single-indicator subtraction.

[0037] Step 5: Calculate the sensitivity correction coefficient using the maturity characterization factor, and then perform a weighted mapping between the sensitivity correction coefficient and the initial residual value to obtain the corrected residual quantification value. The sensitivity correction coefficient is a weighted value used to measure the strength of the system's response to individual biochemical metabolic abnormalities. Considering that the same degree of morphological developmental delay can have drastically different potential pathological risks for children in different metabolically active phases, the system again uses the maturity characterization factor to calculate a non-linear sensitivity correction coefficient. The system then performs a weighted mapping between this correction coefficient and the previously obtained initial residual value. By utilizing the degree of intrinsic metabolic abnormality to amplify or reduce the extrinsic physical residual risk, a corrected residual quantification value that considers both appearance and intrinsic factors is obtained.

[0038] Step Six: Match and output the corresponding health risk level based on the corrected residual quantification value. The system substitutes the corrected residual quantification value into the preset threshold evaluation system, automatically matches the health risk level corresponding to the value, such as: low risk of physiological fluctuations, high risk of metabolic developmental delay, etc., and outputs the level and evaluation report, providing objective evidence for early clinical precision intervention.

[0039] The core innovation of the above technology lies in using a logistic growth function to calculate the sensitivity correction coefficient, which can realistically simulate the nonlinear feedback mechanism of human development to metabolic deviations. It achieves a more sensitive risk-weighted mapping near the critical developmental threshold, making the corrected residual quantification value more reflective of health risks under real pathological conditions. This effectively avoids the existing technology from using misaligned comparison results as the basis for risk judgment, which could lead to health fluctuations in atypical growth cycles being mistaken for pathological deviations or masking real metabolic abnormalities. This effectively reduces the misdiagnosis and missed diagnosis rates in clinical assessment.

[0040] Traditional child development assessments often rely solely on morphological indicators (such as height) at a single time point, neglecting the predictive role of microscopic biochemical indicators in growth dynamics. This fails to reflect the dynamic evolutionary trends during development and does not account for the mismatch between chronological and anatomical ages caused by varying rates of individual physiological development. Therefore, this paper proposes obtaining a health dataset for the target children. This dataset includes morphological feature vectors, biochemical feature sequences, and corresponding chronological time parameters covering the target time window, as detailed below:

[0041] Target time window: refers to the continuous physical time period used to collect and observe the dynamic evolution of various physiological and biochemical indicators of the target child.

[0042] Morphological feature vector: refers to a structured data set that reflects the macroscopic physical development scale of a target child, measured at a specific time point.

[0043] Biochemical characteristic sequence: refers to the set of characteristic data reflecting the microscopic endocrine and metabolic state of the target child, collected multiple times in chronological order within the target time window.

[0044] Natural time parameter: refers to the absolute objective physical time experienced by the target child from birth to the time of data collection, usually expressed as calendar age.

[0045] Step 1: Determine the target time window and natural time parameters:

[0046] The system first sets the time boundaries for data acquisition and calculates the corresponding natural time parameters:

[0047] ; ;

[0048] in, Indicates the duration of the target time window; Indicates the end time stamp of data collection; Indicates the start timestamp of data collection; This represents the natural time parameter of the target child at the deadline timestamp; This represents the birth timestamp of the target child. Using timestamps for time difference calculations unifies the time scale of all heterogeneous data, defines a strict observation period for morphological and biochemical indicators, and ensures that subsequent extraction of dynamic evolutionary features has a unified time span benchmark.

[0049] Step 2: Obtain and construct morphological feature vectors:

[0050] The system extracts the physical measurement indicators of the target children near the deadline time stamp and generates morphological feature vectors. Specifically, the system calculates the body mass index based on baseline physical measurements.

[0051] ; ;

[0052] in, Indicates body mass index; This indicates the target child's weight measurement. This indicates the height measurement of the target child; This represents the completed morphological feature vector.

[0053] Step 3: Obtain and construct biochemical feature sequences:

[0054] The system extracts all biochemical test results within the target time window from the medical information system and arranges them in chronological order to generate a sequence:

[0055] The corresponding sequence construction form is: ;

[0056] No. The subset of biochemical characteristics from the subtest is represented as follows: ;

[0057] in, Represents biochemical characteristic sequences; This represents the total number of biochemical samples taken within the target time window; Indicates the first A subset of biochemical features from the subsamples; This indicates the total number of specific biochemical tests performed on the sample. Indicates the first The first sampling The concentration values ​​of several biochemical indicators are used. A nested vector sequence structure is employed to transform unstructured lab reports into a computer-readable matrix format, preserving the historical trajectory of microscopic metabolic indicators over time and providing time-series data support for subsequent calculations of growth and development rates.

[0058] Step 4: Perform data normalization processing:

[0059] Because the dimensions of various indicators in the morphological feature vector and the biochemical feature sequence are different (such as kilogram, meter, microgram / liter, etc.), the system uses the maximum-minimum linear scaling formula to normalize all acquired values:

[0060] ;

[0061] in, This represents the normalized feature value, whose range is strictly constrained to [0,1]. This indicates the original measurement value obtained; This indicates the upper limit of the statistical extreme values ​​of this type of indicator in the preset reference database; This indicates the lower limit of the statistical extreme values ​​of this type of indicator in the preset reference database. and Historical percentile extreme values ​​(99th and 1st percentile values) were obtained by calling the National Pediatric Endocrinology Clinical Reference Database. This eliminated numerical scale differences caused by different units, ensuring that all multimodal features have equal computational weight in subsequent dimensionality reduction and matrix operations.

[0062] Example 1: The target child is a boy born on January 1, 2014.

[0063] Determine the target time window and natural time parameters: The system sets the start timestamp for data acquisition. The deadline is January 1, 2023. The birth date is January 1, 2024. (Birth timestamp) It was January 1, 2014.

[0064] Target time window duration: .

[0065] Natural time parameter at the deadline timestamp:

[0066] .

[0067] Obtain and construct the morphological feature vector: The child's height was measured on January 1, 2024. Meters, weight measurement kilogram.

[0068] Calculate Body Mass Index .

[0069] Integrating to obtain morphological feature vectors .

[0070] Obtain and construct biochemical feature sequences: Within the target time window (1.0 year), the system extracts two biochemical test results ( The selected biochemical indicators were growth hormone concentration and insulin-like growth factor concentration. ).

[0071] Feature subset of the first sampling (January 1, 2023) .

[0072] Feature subset of the second sampling (January 1, 2024) .

[0073] Integrating to obtain biochemical characteristic sequences .

[0074] Perform data normalization: use the growth hormone concentration value from the second sampling ( Normalization is performed using an example. The system calls the reference database and sets the lower limit for growth hormone statistics in 10-year-old boys. Statistical upper limit .

[0075] Calculate normalized values .

[0076] Through system calculations, all numerical values ​​in the biochemical feature sequence and the morphological feature vectors are converted into real numbers between 0 and 1 in this step, thus completing the construction of the standard health dataset.

[0077] The aforementioned techniques acquire datasets that include not only current morphological results but also biochemical dynamic sequences driving development, providing a complete data foundation for subsequent accurate risk assessment. They establish multidimensional spatiotemporal correspondences, anchoring data from different modalities onto a unified time axis using natural time parameters, thus solving the alignment problem caused by heterogeneous multi-source data. Through normalization processing and standardized feature extraction, they eliminate dimensional biases caused by manual recording or different measurement devices, ensuring the professionalism and accuracy of subsequent quantitative evaluations.

[0078] Because the original biochemical feature sequences are high-dimensional and discrete medical data, and there are complex synergistic and antagonistic relationships among biochemical indicators, directly using the raw data introduces information redundancy. The static concentration of biochemical indicators cannot directly reflect the dynamic activity of tissue growth. Directly using high-dimensional data in subsequent time parameter compensation calculations leads to high system computational overhead and is prone to the curse of dimensionality, causing overfitting of the evaluation model. Therefore, this paper proposes to perform multi-dimensional feature reduction processing on the biochemical feature sequences to extract maturity characterization factors reflecting tissue growth rate, specifically including:

[0079] Perform time-domain difference operation on each biochemical component in the biochemical feature sequence, calculate the rate of change of each biochemical component in the current acquisition cycle relative to the historical acquisition cycle, and construct an evolutionary feature vector reflecting the biochemical trend;

[0080] The evolutionary feature vector is input into a preset maturity contribution matrix, and a linear projection operation is performed to extract latent feature components from high-dimensional biochemical data.

[0081] Calculate the cumulative contribution weights of latent feature components to obtain maturity characterization factors that represent the real-time growth dynamics of an individual.

[0082] Time-domain difference operation: refers to calculating the numerical difference of the same indicator at different time points in time series data, and dividing it by the time interval to obtain the rate of change of the indicator over time.

[0083] Evolutionary feature vector: refers to a one-dimensional array composed of the change rates of multiple biochemical components, used to structurally express the dynamic change trend of the internal physiological environment of a target object within a specific time period.

[0084] Maturity contribution matrix: refers to the linear transformation constant matrix pre-configured by the system, used to map high-dimensional and correlated original feature data to a low-dimensional and uncorrelated orthogonal space.

[0085] Latent feature components: These refer to the new data dimensions generated after matrix projection operations. These dimensions cannot be directly measured in the physical world, but mathematically they represent the deep physiological dynamics that drive the changes of multiple biochemical indicators.

[0086] Maturity characterization factor: refers to a single scalar value synthesized from multiple latent characteristic components in a specific ratio, which serves as the final evaluation index for quantifying the current tissue growth and development rate of a target individual.

[0087] Step 1: Constructing Evolutionary Feature Vectors by Performing Temporal Difference Operations: The system extracts the normalized biochemical feature sequences from the previous steps and calculates the rate of change of each biochemical component in the current acquisition cycle and historical acquisition cycles. ;

[0088] in: Indicates the first The rate of change of each biochemical component; Indicates the first Normalized measurement values ​​of each biochemical component in the current collection period; Indicates the first Normalized measurement values ​​of individual biochemical components over historical collection periods; This represents the time interval between the current acquisition cycle and the historical acquisition cycle, and its value is the difference in physical time.

[0089] Subsequently, the system will... Evolutionary eigenvectors, combined in an inherent order, are in column vector form.

[0090] ;

[0091] in: This represents the completed evolutionary feature vector; This represents the total number of biochemical components involved in the calculation; This represents the transpose operation of a matrix. Biochemical concentrations at individual time points are affected by short-term fluctuations such as diet and lifestyle. By using a difference formula to calculate the temporal slope, static concentration baselines can be filtered out, extracting the true evolutionary rate of index increases or decreases, and transforming static state data into dynamic velocity data characterizing the developmental process.

[0092] Step 2: Perform linear projection operation to extract latent feature components: The system inputs the evolutionary feature vectors calculated above into the algorithm module, and performs matrix multiplication projection with the preset maturity contribution matrix to achieve dimensionality reduction of the data. ;

[0093] in, This represents the column vector of latent feature components generated after dimensionality reduction, with its dimension denoted as . ,and Less than ; This represents the preset maturity contribution matrix, with dimensions of... OK List; This represents the evolutionary eigenvector obtained in the previous step. Maturity contribution matrix. The internal elements of the matrix take values ​​between [-1.0, 1.0]. This matrix does not rely on manual setting, but is obtained by pre-extracting and fixing the eigenvalues ​​of historical standard children's developmental group data through principal component analysis algorithm.

[0094] The expanded latent feature component column vector is represented as follows: ;

[0095] in, Indicates the extracted first The values ​​of each independent latent feature component ( The value range is 1 to Multiple endocrine hormones (such as growth hormone and thyroid hormone) exhibit statistical multicollinearity in their growth-promoting effects. Using matrix linear projection formulas to eliminate redundant correlations between variables compresses high-dimensional complex data into a small number of independent features, reducing the computational overhead of subsequent calculations.

[0096] Step 3: Calculate the cumulative contribution weight to obtain the maturity characterization factor: The system assigns a corresponding weight to each extracted latent feature component, sums them up, and outputs the final scalar result.

[0097] ;

[0098] in, This represents the calculated maturity characterization factor, which is a one-dimensional real number. Indicates the first The values ​​of each latent feature component; Indicates the preset first The contribution weights corresponding to each latent feature component. The value range is between (0.0, 1.0), and all The sum is 1.0. This weight value is predetermined by the variance contribution rate of the corresponding principal component in the historical sample set. Even after dimensionality reduction, there are still multiple latent feature components, which cannot be directly used as a reference for subsequent additions or subtractions of a single time parameter. Using a weighted summation formula, multiple dimensions are combined based on their information content ratio, merging the scattered physiological information into a scalar value with a unified dimension, providing a direct mathematical benchmark for subsequent time parameter correction.

[0099] Example 2: Continuing from the previous example, the target child is within the target time window ( Within (year):

[0100] Normalized biochemical values ​​for the historical collection period (January 1, 2023): Growth hormone Insulin-like growth factor .

[0101] Normalized biochemical values ​​for the current collection period (January 1, 2024): Growth hormone Insulin-like growth factor .

[0102] Number of biochemical components involved in the calculation .

[0103] Step 1: Perform temporal difference operations to construct evolutionary feature vectors:

[0104] Calculate the rate of change of the first component (growth hormone):

[0105] ;

[0106] Calculate the rate of change of the second component (insulin-like growth factor):

[0107] ;

[0108] Constructing evolutionary feature vectors: .

[0109] Step 2: Perform linear projection operation to extract latent feature components: The system calls the preset backend function to reduce 2D to 1D (i.e., The maturity contribution matrix of ) is set to a value of .

[0110] Perform matrix multiplication to calculate latent eigencomponents:

[0111] ;

[0112] ;

[0113] ;

[0114] This yields the unique value of the latent feature component. .

[0115] Step 3: Calculate the cumulative contribution weight to obtain the maturity characterization factor: Since the feature space after dimensionality reduction contains only one dimension, the system presets the contribution weight for that dimension. .

[0116] Perform a weighted summation calculation to determine the maturity characterization factors:

[0117] ;

[0118] Through the above calculations, the system quantifies and merges the boy's complex two-year biochemical test fluctuations into a single maturity characterization factor value of 0.204, representing his real-time growth momentum, and outputs it to the next stage for processing.

[0119] In the aforementioned techniques, matrix projection dimensionality reduction removes coupling information between multiple biochemical indicators, extracts the core micro-dynamic features that truly drive bone and muscle development, improves the effectiveness of input data, and differential operations enable the algorithm to capture subtle physiological fluctuation trends. Clinical biochemical data is structured into engineering indicators that reflect growth rate, improving the system's response sensitivity. Multi-dimensional biochemical data is unified and integrated into a single-dimensional maturity characterization factor scalar, significantly simplifying the algorithmic complexity of subsequent weight compensation calculations for natural time parameters and improving the system's computational efficiency.

[0120] Current technologies typically use an individual's chronological age as the sole retrieval criterion for extracting standard morphological reference values. This approach ignores the differences in physiological maturity among children of the same physical age. For example, children with advanced physiological development should have morphological indicators higher than the average for their age. If their chronological age is still used for comparison, it will cause a benchmark misalignment, leading to the misjudgment of normal precocious physiological development as potential health risks and reducing the objectivity of the assessment conclusions. Therefore, this paper proposes: calculating weighted compensation for chronological time parameters based on maturity characterization factors to generate a physiological offset step size, and then superimposing and correcting the chronological time parameters based on this physiological offset step size to obtain corrected time parameters, specifically including:

[0121] Using natural time parameters as an index, the standard maturity benchmark value corresponding to the current age is extracted from a preset physiological development comparison table;

[0122] The difference between the maturity characterization factor and the standard maturity benchmark value is calculated to obtain the deviation increment that reflects the speed of physiological development.

[0123] The deviation increment is multiplied by a preset time dimension mapping operator to generate a physiological offset step size in units of time.

[0124] Using natural time parameters as the original time series coordinates and physiological offset step size as the translation vector, the corrected time parameters are obtained through algebraic addition.

[0125] Physiological Development Comparison Table: This refers to a numerical mapping table that is pre-stored in the system database and records the average maturity index levels of a large-scale homogeneous sample of children at different natural age points.

[0126] Standard maturity benchmark: refers to the expected physiological development value corresponding to the current natural age of the target child, obtained by searching the physiological development comparison table.

[0127] Deviation increment: refers to the mathematical difference between the actual maturity level of the target child and the standard level of the same age, used to quantify the degree of advancement or lag in developmental progress.

[0128] Time dimension mapping operator: refers to a preset conversion coefficient used to convert dimensionless physiological deviation values ​​into physical quantities with time units (such as years or months).

[0129] Physiological deviation step size: refers to the displacement of the target child's physiological age relative to their chronological age, calculated from the maturity deviation.

[0130] Corrected time parameter: refers to the physiological age coordinates that accurately reflect the individual's true developmental stage after superimposing natural time with physiological offset.

[0131] Step 1: Extracting the standard maturity baseline value: The system uses the obtained natural time parameter as the primary key and extracts the corresponding standard value from the physiological development control table through a lookup table algorithm.

[0132] ;

[0133] in, This represents the standard maturity benchmark value obtained from the search; The natural time parameter (physical age) of the target child; This represents the index retrieval function for the physiological development comparison table. It establishes an average developmental reference point for a peer group. Through the retrieval function, the system can quickly locate the expected level of biochemical maturity that children should possess at that age, providing a zero-point baseline for subsequent deviation calculations.

[0134] Step 2: Calculate the deviation increment reflecting the rate of physiological development: The system subtracts the calculated maturity characterization factor from the extracted baseline value.

[0135] ;

[0136] in, Indicates deviation from the increment; This represents the maturity characterization factors extracted in the second step; This represents the standard maturity baseline value obtained in the first step of this process. By calculating the residual between the individual's actual value and the standard value, the individual's developmental dynamics can be intuitively quantified. When When the growth rate is high, it indicates that the individual's growth momentum is stronger than the average for its age; conversely, it indicates that the growth momentum is weaker. This transforms the qualitative description of the rate of development into a quantitative numerical deviation.

[0137] Step 3: Generate physiological offset step size in time units: The system performs a product operation on the obtained offset increment and the preset time dimension mapping operator: ;

[0138] in, Indicates the physiological offset step size; This represents the deviation increment obtained in the previous step; This represents a preset time dimension mapping operator. The value of the maturity factor is typically determined through a linear regression analysis of the deviation of the maturity characteristic factor from historical sample individuals and the actual natural time span required to reach the same bone age. The slope coefficient of this linear regression equation is pre-set, ranging from [1.0, 10.0], representing the physiological time shift sensitivity corresponding to a unit maturity deviation. Since the maturity factor is a biochemical statistic, it cannot be directly added to or subtracted from the time axis. A mapping operator is used for linear transformation, which maps the rate deviation of the biochemical dimension to the phase deviation of the time dimension, achieving unification of multimodal data in terms of physical dimensions.

[0139] Step 4: Obtain the corrected time parameters through algebraic addition: The system uses natural time parameters as the reference coordinates, and the physiological offset step size as the translation vector, and obtains the final coordinates through superposition.

[0140] ;

[0141] in, This indicates the correction time parameter (i.e., physiological age). Represents the natural time parameter; This represents the physiological offset step size obtained in the previous step. The logic of superimposing translational vectors is used to dynamically correct the assessment benchmark, thereby generating a physiological scale that represents the individual's true developmental progress, allowing subsequent morphological comparisons to be conducted under a truly homogeneous physiological benchmark.

[0142] Example 3: Following the previous example, the target child's natural age Age, maturity characterization factors have been calculated .

[0143] Step 1: Extract the standard maturity benchmark value: systematically retrieve the physiological development comparison table.

[0144] Assuming the query yields the standard maturity baseline value for a 10.0-year-old boy .

[0145] Step 2: Calculate the deviation increment: .

[0146] The calculation results show that the child's developmental dynamics are 0.024 units higher than the standard level.

[0147] Step 3: Generate physiological offset step size:

[0148] The system has a preset time dimension mapping operator. .

[0149] Year.

[0150] The calculation results show that the individual's physiological development progress is 0.12 years ahead of natural time.

[0151] Step 4: Obtain the corrected time parameters:

[0152] age.

[0153] The system revised the child's assessment baseline from 10.0 years to 10.12 years. This means that when searching for height and weight baselines in the standard database, the physiological time parameter of 10.12 years, which is more in line with the child's actual developmental status, will be used to arrive at a more accurate health assessment conclusion.

[0154] In the aforementioned technology, by correcting natural age to physiological age, the system can retrieve a reference benchmark that truly matches the individual's current physiological stage from the standard developmental benchmark database. This dynamic alignment mechanism of time coordinates eliminates developmental timing errors caused by an individual's "early growth" or "late growth," providing an accurate comparative reference system for subsequent residual calculations and improving the accuracy and scientific nature of health risk quantitative assessment.

[0155] Current technologies typically compare individual indicators such as height against a database when comparing developmental standards, resulting in multiple discrete deviation values. This one-to-one comparison method severs the structural relationships between various morphological indicators, failing to provide a comprehensive quantitative standard for measuring an individual's overall physical deviation. Furthermore, the data in standard databases are mostly discrete nodes, lacking accurate comparison benchmarks for the temporal states between nodes. Therefore, this paper proposes: retrieving corresponding benchmark morphological reference vectors from a pre-defined standard developmental benchmark database based on corrected time parameters, and calculating the spatial distance between the morphological feature vector and the benchmark morphological reference vector to obtain initial residual values, specifically including:

[0156] Using the corrected time parameter as the query key, retrieve data from two adjacent standard time points in the standard developmental benchmark database;

[0157] If the value of the corrected time parameter falls within the time interval formed by two adjacent standard time nodes, the reference vectors corresponding to the two adjacent standard time nodes are obtained respectively, and the reference vector is fitted and generated based on the distance distribution weight of the corrected time parameter within the time interval.

[0158] If the absolute value of the difference between the corrected time parameter and any standard time node is less than the preset tolerance threshold, then the reference vector corresponding to the standard time node is directly used as the reference shape reference vector.

[0159] Extract the components of height, weight and body mass index from the morphological feature vector and map them to a preset Euclidean metric space;

[0160] The Euclidean distance between the morphological feature vector and the baseline morphological reference vector in the Euclidean metric space is calculated to obtain the initial residual value representing the absolute developmental deviation.

[0161] Standard developmental benchmark database: refers to a data warehouse that is pre-built and stored in the system backend, which contains statistical mean values ​​of morphological characteristics of a large number of homogeneous healthy children at different discrete standard time points.

[0162] Standard time node data: refers to discrete age scale points in the database divided by a fixed time span (such as by month or by half a year).

[0163] Preset tolerance threshold: refers to a small time difference limit set by the system to determine whether the corrected time parameter can be directly approximated as the existing discrete time node in the database.

[0164] Reference vector for baseline morphology: refers to the set of standard features consisting of standard height, weight, and body mass index corresponding to the target child's specific physiological age.

[0165] Euclidean metric space: refers to a mathematical geometric space in which features of different dimensions (such as height and weight) are regarded as orthogonal coordinate axes, and the absolute distance between multidimensional points can be calculated through geometric rules.

[0166] Euclidean distance: refers to the linear geometric distance between two multidimensional coordinate points in Euclidean metric space.

[0167] Initial residual value: refers to the multidimensional comprehensive deviation value between the actual physical form of the target child and the standard physiological development form, calculated by Euclidean distance.

[0168] Step 1: Retrieve adjacent time nodes and perform tolerance determination:

[0169] The system inputs the corrected time parameter into the standard developmental benchmark database and retrieves data from two adjacent standard time points that contain the corrected time parameter across the time span. The system then calculates the absolute value of the time difference between the corrected time parameter and these two points and compares it to a preset tolerance threshold. The preset tolerance threshold is set to five percent of the regular data sampling period. For example, if the database progresses monthly (approximately 0.083 years), the preset tolerance threshold can be set to 0.004 years.

[0170] Step 2: Generate a baseline shape reference vector based on the judgment result: If the absolute value of the difference between the corrected time parameter and any standard time node is less than the preset tolerance threshold, the system directly extracts the baseline vector corresponding to that time node as the final reference. If the corrected time parameter falls between two nodes, the system uses distance distribution weights to perform linear interpolation.

[0171] ;

[0172] in, This represents the baseline shape reference vector generated by the fitting process; This indicates the corrected time parameters derived from the preceding steps; This indicates the standard time point adjacent to the smaller side of the retrieved data; This indicates the standard time point adjacent to the larger side of the retrieved data; Indicates corresponding to The morphological baseline vector of the time node; Indicates corresponding to A morphological baseline vector for each time point. Standard database data is stored as discrete time points, while an individual's physiological age is a continuous variable. By allocating weights according to the proportional relationship across time spans, the gaps between discrete data points are filled, generating a smooth and continuous developmental baseline for any physiological moment, eliminating systematic errors caused by direct rounding.

[0173] Step 3: Extract components and map to Euclidean metric space: The system extracts normalized numerical components for height, weight, and body mass index from the initially acquired actual morphological feature vector, constructing coordinate points. Simultaneously, the baseline shape reference vector generated in the previous step is used to construct coordinate points. Both are placed in the same-dimensional Euclidean metric space.

[0174] Step 4: Calculate spatial distance to obtain initial residual values: After establishing spatial coordinates, the system calculates the coordinate points. with coordinate points Output a single numerical value representing the Euclidean distance between them:

[0175] ;

[0176] in, This represents the calculated initial residual value;

[0177] , , These represent the normalized components of height, weight, and body mass index in the actual morphological feature vector of the target child, respectively. , , These represent the normalized values ​​of height, weight, and body mass index in the corresponding dimensions of the baseline morphological reference vector. Traditional single-indicator comparison methods (such as looking at height difference alone or weight difference alone) fragment the overall structural attributes of human development. By aggregating the deviations from multiple dimensions using the geometric sum of squares rule, multiple scattered morphological differences are merged into a comprehensive and objective multidimensional distance scale, providing a unified underlying calculation benchmark for the subsequent quantification of overall developmental risk.

[0178] Example 4: Continuing from the previous example, the corrected time parameter for the target child. The child is [age]. After normalization using preset rules, the child's actual measured height, weight, and BMI are [coordinates of the actual morphological feature vector]. .

[0179] Step 1: Retrieve adjacent time nodes and perform tolerance determination:

[0180] The system is set to a preset tolerance threshold of 0.02 years. To retrieve two adjacent standard time points from a key-value database. Years and age.

[0181] Calculation and judgment: ,because Greater than the preset tolerance threshold The system determines that interpolation fitting calculations are required.

[0182] Step 2: Generate a baseline shape reference vector based on the judgment result:

[0183] The system extracts the baseline vector of 10.0-year-olds from the database. ,as well as The baseline vector for age .

[0184] Calculate the distance distribution weights: .

[0185] Execution base form reference vector Fitting calculation:

[0186] First dimension (height standard): ;

[0187] Dimension 2 (Weight Standard): ;

[0188] The third dimension (BMI standard): ;

[0189] This generates a baseline morphological reference vector corresponding to the corrected age. .

[0190] Steps 3 and 4: Calculate the spatial distance to obtain the initial residual value:

[0191] actual vector With reference vector Substitute the values ​​into the Euclidean distance formula to measure the distance:

[0192] Calculate the squared differences in each dimension:

[0193] Dimension 1: ;

[0194] Dimension 2: ;

[0195] 3rd Dimension: ;

[0196] Calculate the initial residual value by aggregating all terms:

[0197] ;

[0198] Finally obtained .

[0199] The calculation results show that, taking into account the multidimensional framework of height, weight and BMI, the absolute developmental deviation of the child's overall morphological characteristics compared to the standard reference model at the physiological benchmark of 10.12 years old is quantified as 0.037 units in length in the metric space. This value will be used as the underlying deviation input to the subsequent risk correction module.

[0200] In traditional algorithms, determining whether interpolation is necessary typically relies on a fixed tolerance threshold (e.g., 0.05 years). This static threshold fails to consider the non-uniform nature of child development. If the threshold is set too high, significant morphological changes during the pubertal growth spurt may be missed, reducing assessment accuracy; if the threshold is set too low, numerous unnecessary linear interpolation calculations may be triggered during periods of stable development, unnecessarily increasing system computational overhead and reducing assessment efficiency. Therefore, this paper proposes an adaptive tolerance threshold that is dynamically generated based on the developmental stage of the corrected time parameter, specifically including:

[0201] In the standard developmental benchmark database, the morphological benchmark components corresponding to two adjacent standard time nodes are extracted, and the absolute value of the first derivative of the morphological benchmark components in the time interval is calculated as the evolution slope characterizing the local growth rate.

[0202] The preset base time tolerance and evolution slope are inversely mapped, and an adaptive tolerance threshold is output based on the calculation result.

[0203] Adaptive tolerance threshold: refers to the allowable range of time error that the system dynamically adjusts based on the growth rate of the target child within a specific physiological time period.

[0204] Morphological baseline components: These refer to single-dimensional standard physical data (such as standard height and standard weight) extracted from the standard developmental baseline database and corresponding to a specific discrete time point.

[0205] The absolute value of the first derivative: In mathematics and kinematics, it represents the magnitude of the rate of change. In this scheme, it specifically refers to the absolute value of the rate of change of the morphological reference component with time.

[0206] Evolutionary slope: refers to the calculated relative speed at which the standard physical form changes within a certain local time interval.

[0207] Basic time tolerance: refers to a constant time error limit set in advance by the system, which serves as the base for subsequent inverse proportional adjustments.

[0208] Inverse proportional mapping operation: refers to a mathematical operation logic in which when the independent variable (evolution slope) increases, the dependent variable (tolerance threshold) decreases by a set proportion, and vice versa.

[0209] Step 1: Extract morphological reference components and calculate the evolution slope: Based on two adjacent standard time nodes retrieved in the previous step, the system extracts the multidimensional morphological reference vectors corresponding to these two nodes. Subsequently, the system calculates the Euclidean distance between these two vectors in multidimensional space and divides it by the time span between the two time nodes to obtain the absolute value of the first derivative (i.e., the evolution slope).

[0210] ;

[0211] in, This represents the calculated evolution slope; This indicates the standard time point adjacent to the larger side of the retrieved data; This indicates the standard time point adjacent to the smaller side of the retrieved data; , , These represent the height, weight, and body mass index components in the morphological baseline vector corresponding to larger time nodes, respectively. , , These represent the height, weight, and body mass index components in the morphological baseline vector corresponding to smaller time points. Growth rate varies significantly across different developmental stages in children (e.g., infancy, puberty spurt, and growth plateau). By quantifying the overall rate of change of multidimensional morphological features within a specific time interval using a first-derivative distance formula, the local growth rate is transformed into concrete numerical indicators, providing an objective basis for subsequent dynamic tolerance adjustments.

[0212] Step 2: Perform inverse proportional mapping operation to output adaptive tolerance threshold: The system retrieves the preset base time tolerance and performs an inverse proportional mapping algebra operation on it and the evolution slope calculated in the previous step:

[0213] ;

[0214] in, This represents the adaptive tolerance threshold of the system's final output. This indicates the preset base time tolerance; This represents the evolution slope obtained in the previous step; This represents the preset mapping adjustment coefficient. The value is usually set to half of the standard data sampling interval (for example, if the sampling interval is 0.5 years, then it is set to 0.25 years). A constant, ranging from [0.5, 5.0], is used to prevent the calculation results from diverging or becoming overly sensitive. The specific method for determining this value is as follows: extract the statistical variance of the evolutionary slope of a historically homogeneous population at the corresponding age group, and use the reciprocal of the variance (or a normalized proportion of the variance) as the mapping adjustment coefficient. This formula constructs a mathematical model of the negative correlation between the tolerance threshold and the growth rate. When in a period of rapid growth (… When the growth is relatively large, even a small time deviation can lead to a significant change in the morphological baseline; while when it is in a stable growth period ( When the size is small, the shape does not change significantly over time, realizing a dynamic adjustment mechanism where faster growth results in smaller tolerance (requiring more precise alignment) and slower growth results in larger tolerance (allowing for moderately relaxed matching).

[0215] By introducing an evolution slope calculation method based on the concept of the first derivative in calculus, and using an inverse proportional mapping function to transform the fixed time tolerance into a dynamic adaptive boundary parameter that can perceive the characteristics of the local growth environment, the contradiction between evaluation accuracy and system computing power consumption is effectively resolved. Through the dynamic output of the adaptive tolerance threshold, the system automatically tightens the matching judgment conditions during the rapid development stage of children, ensuring the rigor of the basic comparison data; and automatically relaxes the judgment conditions during the slow development stage, improving the system's response and processing efficiency, making the underlying algorithm model more consistent with the objective laws of human biology.

[0216] Example 5: Continuing from the previous example, the corrected time parameter for the target child. Age. The retrieved adjacent standard time points are: age, age.

[0217] The baseline vector components corresponding to age 10.0 are: , , .

[0218] The baseline vector components corresponding to 10.5 years old are: , , .

[0219] Step 1: Extract morphological reference components and calculate the evolution slope:

[0220] The system calculates the evolutionary slope in the 10.0 to 10.5 year age range. :

[0221] Molecular (spatial distance): ;

[0222] Denominator (time interval): ;

[0223] Evolutionary slope .

[0224] Step 2: Perform an inverse proportional mapping operation to output an adaptive tolerance threshold:

[0225] The system retrieves the preset base time tolerance. Age, preset mapping adjustment coefficient .

[0226] Substituting the above values ​​into the inverse proportional mapping formula to calculate the adaptive tolerance threshold. :

[0227] age.

[0228] The calculation results show that, under the child's current local growth rate, the system dynamically adjusts the time tolerance of the matching of the judgment criteria node from a fixed threshold to 0.036 years.

[0229] Combining the tolerance determination logic mentioned above, since the absolute value of the difference between the correction time parameter and the smaller time node ( The deviation (age) is greater than the dynamically generated adaptive tolerance threshold (0.036 years). Therefore, the system accurately identifies that the deviation is not negligible and continues to perform linear interpolation fitting, ensuring high accuracy in subsequent initial residual value calculation.

[0230] In the aforementioned technology, the interpolation fitting mechanism eliminates the time rounding error caused by discrete database storage, providing a precise continuous morphological comparison benchmark for physiological age at any given time. Based on the initial residual value calculated using Euclidean distance, the originally isolated developmental data such as height and weight are integrated into a multi-dimensional structured deviation value, which objectively and quantitatively reflects the severity of the target individual's deviation from the normal physiological trajectory in terms of overall physical body shape, providing comprehensive basic data support for subsequent risk level determination.

[0231] Current child health assessment logic often directly uses absolute deviations in morphological indicators as the final basis for risk assessment, failing to consider the nonlinear amplification effect of individual intrinsic metabolic abnormalities on risk. For example, when an individual's endocrine indicators deviate significantly from normal levels, even if their initial physical deviations are the same as those of normal children, their potential health risks and the urgency of intervention are significantly higher. Indiscriminately using the original spatial distance for assessment can lead to an underestimation of risk in children with severe biochemical metabolic disorders. Therefore, this paper proposes: calculating a sensitivity correction coefficient using maturity characterization factors, and then weighting and mapping the sensitivity correction coefficient to the initial residual value to obtain the corrected residual quantification value, including the following steps:

[0232] Calculate the absolute value of the difference between the maturity characterization factor and the preset maturity median, and then perform a ratio operation between the absolute value of the difference and the preset maturity median to obtain the absolute deviation rate that characterizes the degree of deviation of the metabolic state.

[0233] The absolute deviation rate is input into a preset logical growth function to obtain a value between 0 and 1 as the sensitivity correction coefficient, where the sensitivity correction coefficient is positively correlated with the absolute deviation rate;

[0234] The initial residual value is multiplied by the sensitivity correction coefficient to obtain the residual correction component. The residual correction component is then algebraically compared with the initial residual value to obtain the corrected residual quantization value.

[0235] Preset Maturity Median: This refers to the scalar value representing the general metabolic state of the population, obtained by the system through median aggregation statistics on a homogeneous group database with the same regional characteristics and environmental exposure parameters as the target children.

[0236] Absolute deviation rate: refers to the relative proportion of the target individual's actual biochemical maturity state deviating from the above-mentioned central level of the group. It is a dimensionless percentage or decimal characteristic indicator.

[0237] Logistic growth function: refers to a nonlinear mathematical mapping model with an S-shaped curve distribution. Its characteristic is that the growth rate of the dependent variable changes significantly when the independent variable approaches a certain threshold. It is often used to simulate the dose-response feedback response in biology.

[0238] Sensitivity correction coefficient: refers to the weight value output by the logistic growth function, used to measure the strength of the system's response to individual biochemical metabolic abnormalities.

[0239] Residual correction component: refers to the additional penalty bias value that needs to be injected into the purely physical morphological deviation based on the degree of biochemical metabolic abnormality.

[0240] Corrected residual quantification value: refers to the final quantitative indicator representing the overall developmental health status of an individual after algebraically fusing the absolute deviation of physical spatial morphology with the relative deviation of biochemical metabolic environment.

[0241] Step 1: Calculate the absolute deviation rate representing the degree of deviation from metabolic state: The system extracts the maturity characterization factors obtained in the previous steps and retrieves the preset maturity median value set in the background. The absolute value of the difference between the two is calculated to quantify the absolute magnitude of the deviation. Then, this absolute value is divided by the preset maturity median value for relativization.

[0242] ;

[0243] in, This represents the calculated absolute deviation rate; This represents the maturity characterization factor calculated in the preceding steps; This represents the preset maturity median derived from statistics of a homogeneous sample. Directly using absolute differences cannot measure the severity of deviation (e.g., a difference of 0.1 has different meanings for a base of 1.0 and a base of 0.2). This ratio formula is used to eliminate the fundamental magnitude differences in the maturity factor, outputting a standardized relative deviation index, allowing children at different developmental stages and with different base levels to assess the degree of their metabolic disorders on the same mathematical scale.

[0244] Step 2: Input the absolute deviation rate into the preset logical growth function to calculate the sensitivity correction coefficient:

[0245] The system uses the absolute deviation rate obtained above as an input variable, substitutes it into the nonlinear logistic growth function model, and calculates an output coefficient with a value between 0 and 1. The specific formula for the logistic growth function is:

[0246] ;

[0247] in: This represents the absolute deviation rate.

[0248] This is the sensitivity correction factor;

[0249] This is the basic sensitivity; its value is set between [0.0, 0.2], representing the minimum correction weight when the metabolism deviates from the minimum.

[0250] This represents the saturation gain limit; its value is set between [0.8, 1.0], indicating the upper limit of the correction weight when metabolism deviates significantly.

[0251] This is the gain sensitivity; its value is set between [10.0, 30.0], and it is used to control the slope of the function curve near the inflection point.

[0252] This represents the deviation from the critical threshold; the value range is set according to clinical pathological statistics, usually between [0.1, 0.3], representing the clinical dividing point between normal physiological fluctuations and pathological metabolic abnormalities;

[0253] It is a natural constant.

[0254] The compensatory mechanisms of organisms are non-linear; slight metabolic fluctuations are usually within the body's self-regulating range, but once a certain threshold is exceeded, health risks increase exponentially. The reason for using the logistic growth function formula is that its S-shaped curve can objectively simulate this non-linear biological response mechanism. Less than At this time, the output coefficient increases slowly, avoiding the system from overreacting to normal physiological fluctuations; when Cross At this time, the output coefficient rises rapidly, enhancing the system's correction weight for potential pathological risks.

[0255] Step 3: Calculate the residual correction component and obtain the corrected residual quantization value: The system extracts the initial residual value calculated through the Euclidean metric space in the previous steps. The initial residual value is multiplied by the sensitivity correction coefficient to obtain the residual correction component. Then, the residual correction component is algebraically added to the initial residual value.

[0256] ;

[0257] ;

[0258] in, Indicates the residual correction component; This represents the initial residual value calculated in the preceding steps; This represents the sensitivity correction factor; This represents the corrected residual quantization value. The initial residual value only represents the physical morphology lag of the representation. The reason for using the product and superposition combination formula is to transform the high risk at the biochemical level into an additional penalty term at the morphological level. This achieves deep structured fusion of multimodal features in the underlying algorithm, resulting in a higher final evaluation value. It not only includes the absolute deviation of the current physical form, but also the expected developmental risks caused by abnormal metabolic environment, thus improving the comprehensiveness of the assessment results.

[0259] Example 6: Continuing from the data example above. The maturity characterization factors calculated from the preceding steps for the target child are known. Initial residual value .

[0260] Step 1: Calculate the absolute deviation rate, which characterizes the degree of deviation in metabolic state: The system retrieves the preset median maturity value of the homogeneous population in this age group and sets it as... .

[0261] The calculation results show that the proportion of the child's current metabolic state deviating from the central level of the population is about thirteen percent.

[0262] Step 2: Input the absolute deviation rate into the preset logical growth function to calculate the sensitivity correction coefficient: The system retrieves the preset configuration parameters of the logical growth function: base sensitivity. Saturation gain limit Gain Sensitivity Deviation from the critical threshold Natural constant :

[0263] Substituting the above values ​​into the logistic growth function formula:

[0264] .

[0265] Because the child's metabolic deviation rate (0.1333) has exceeded the normal critical threshold (0.10) set by the system, the logistic growth function outputs a high sensitivity correction coefficient of 0.6285 through a steep upward curve.

[0266] Step 3: Calculate the residual correction components and obtain the corrected residual quantization values:

[0267] First, calculate the residual correction components that need to be added: ;

[0268] Then calculate the final residual quantization value: .

[0269] The calculation results show that although the child's spatial distance deviation in the physical dimension is only 0.037, due to the abnormally hyperactive metabolic state, the system amplifies the quantitative assessment value of the child's comprehensive health risk to 0.0603 according to the set penalty weight, which provides an accurate numerical basis for triggering high-level clinical intervention warnings.

[0270] Using a fixed set of threshold parameters for all children in health risk assessment would violate the objective laws of human growth and development. For example, a 10% metabolic deviation rate in the relatively metabolically stable preschool years might indicate a serious endocrine disorder; however, the same deviation rate during puberty might simply be a normal physiological hormone surge. Using a one-size-fits-all static parameter could lead to over-warning (false positives) in some age groups and under-judgment (false negatives) in others. Therefore, this paper proposes a parameter combination consisting of baseline sensitivity, saturation gain limit, gain sensitivity, and deviation threshold that is dynamically configured based on the age group and gender characteristics of the target children. Specifically, this includes:

[0271] Identify the age and gender attributes of target children from health datasets, and categorize them into preset age groups based on their age attributes;

[0272] Using age group and gender attributes as a joint index, the system retrieves parameter combinations that match individuals at different physiological stages from a preset configuration matrix and substitutes them into a logistic growth function to calculate the sensitivity correction coefficient.

[0273] Age groups: These refer to the division of continuous natural age into several discrete time intervals with independent physiological characteristics, based on the clinical medical classification of human growth and development patterns (such as infancy, preschool age, school age, adolescence, etc.).

[0274] Configuration matrix: refers to a two-dimensional or multi-dimensional data table structure that is pre-built and stored in the system backend, which contains multiple sets of algorithm parameter combinations labeled for different specific groups of people.

[0275] Composite index: refers to a database or data matrix query that combines two or more data attributes (age group and gender in this solution) as a unified query key to achieve precise location of target data.

[0276] Step 1: Identify basic attributes and perform age group classification:

[0277] The system reads the natural time parameters and gender attribute records of the target children from the acquired health dataset. Then, the system uses a preset segmented mapping function to divide the continuous natural time parameters into the corresponding discrete age groups.

[0278] ;

[0279] in, This indicates the age group label in the mapped output; This represents the preset piecewise mapping function; This represents the natural time parameter of the target child. The piecewise mapping function is divided according to standard pediatric endocrinology criteria, for example... exist Group 1 exist This is the second group, and so on. Continuous age values ​​are too discrete; directly using them for parameter matching would result in an excessively large configuration matrix dimension and a lack of clinical representativeness. The reason for using a piecewise mapping function is to transform physical time into a set of groups with common physiological characteristics. Its effect is to provide a structured and standardized classification dimension for subsequent parameter retrieval.

[0280] Step 2: Build a composite index and retrieve the configuration matrix:

[0281] The system concatenates the obtained age groups with the retrieved gender attribute to generate a unique composite index. Using this composite index, the system performs a search and match within a pre-defined configuration matrix to extract the corresponding parameter combinations.

[0282] ;

[0283] in, This represents the combination of parameters in the retrieval output, specifically including the base sensitivity. Saturation gain limit Gain and sensitivity and deviation from the critical threshold ; This represents the preset configuration matrix; This indicates the age group label obtained in the previous step; This indicates the gender attribute of the target child. Configuration Matrix The various parameter combinations are obtained by collecting a large sample of historical children's health data (including samples diagnosed with metabolic diseases and healthy samples), using expert-annotated clinical intervention risk probabilities as target labels, and employing nonlinear least squares method (or specific machine learning regression algorithms, such as support vector regression, SVR) to curve fit the logistic growth function, thereby solving for the optimal values ​​for different age groups and genders. , , , The parameter values ​​are fixed. Because boys and girls have different physiological tolerances to endocrine fluctuations at different ages (especially around puberty), the reason for using a multi-attribute joint index formula is to establish a two-dimensional cross-referencing mechanism to ensure that the extracted regulatory parameters strictly conform to the objective biological laws of that specific physiological stage and specific gender population.

[0284] Step 3: Substitute into the logical growth function to calculate the sensitivity correction coefficient: The system takes the four boundary parameters obtained in the previous step and customized for the target child, together with the absolute deviation rate calculated in the previous step, and substitutes them into the logical growth function to perform nonlinear mapping calculation.

[0285] By extracting an individual's age and gender attributes and constructing a joint index, discrete individuals are mapped to specific physiological model intervals in a configuration matrix, thereby extracting the logistic growth function boundary parameters that best match their current physiological stage in real time. The system can dynamically adjust the trigger threshold and penalty intensity of risk correction based on the differences in developmental timing between males and females and the tolerance of different growth and development stages to metabolic fluctuations. This dynamic configuration mechanism significantly improves the system's specificity and accuracy in cross-age and cross-gender assessments, ensuring the clinical scientific basis of the assessment conclusions.

[0286] Example 7: Continuing from the data example above. The natural time parameters of the target child are known. Age, gender: male, absolute deviation rate calculated in previous steps. .

[0287] Step 1: Identify basic attributes and perform age group classification: The system will... The age is substituted into the segmented mapping function. Based on the system-defined age segmentation criteria (e.g., ...), ... (Divided into school-age groups), the system outputs age group labels. School age.

[0288] Step 2: Construct a composite index and retrieve the configuration matrix: The system combines age groups with gender attributes to generate a composite index of (school age, male). The system uses this composite index in the configuration matrix. The system performs a location search. Based on preset data on the physiological characteristics of the specific group of "school-aged boys" in the system backend, it extracts the corresponding parameter combinations:

[0289] Basic sensitivity ;

[0290] Saturation gain limit ;

[0291] Gain Sensitivity ;

[0292] Deviation from critical threshold .

[0293] The parameter values ​​retrieved here correspond precisely to the values ​​used in the previous example, explaining the specific source of the parameters in the previous calculation steps.

[0294] Step 3: Substitute the values ​​into the logistic growth function to calculate the sensitivity correction coefficient:

[0295] The system will dynamically acquire parameter combinations and absolute deviation rates. Substitute into the formula:

[0296] .

[0297] The calculation results show that by dynamically retrieving the appropriate parameters configured for "10-year-old boys", the system scientifically determined that the child's metabolic deviation rate of 0.1333 exceeded the normal tolerance threshold (0.10) of this group, and output the corresponding sensitivity correction coefficient of 0.6285 accordingly, ensuring the logical rigor and physiological objectivity of the previous residual correction process.

[0298] In the aforementioned techniques, the use of a logistic growth function enables nonlinear dynamic adjustment of risk weights. When developmental deviations are small, the coefficient remains at a low baseline level, avoiding oversensitivity in the assessment system. When developmental deviations cross a set critical threshold, the correction coefficient rises rapidly, enhancing the system's ability to respond to potential pathological abnormalities. Through a weighted mapping mechanism, the system achieves structured coupling of morphological and biochemical characteristics in its underlying algorithm. The corrected residual quantification value not only includes the intuitive size difference but also the degree of abnormality in intrinsic developmental dynamics, improving the objectivity and clinical application value of subsequent health risk grading outputs.

[0299] The corrected residual quantification value is a continuous real number. For family members of test subjects or routine medical examination personnel who lack relevant background knowledge, a single value cannot provide an intuitive judgment standard (i.e., it is unclear whether the value represents a healthy, sub-healthy, or diseased state). Without standardized grading and conversion of the quantified value, the assessment system will lack clinical readability and practical operational guidance value. Therefore, we propose: matching the corresponding health risk level based on the corrected residual quantification value and outputting it, as follows:

[0300] Health risk level: refers to a discrete classification label used to qualitatively characterize the severity of abnormal growth and development in children. It is usually divided into different levels (such as low risk, medium risk, high risk) based on the urgency of clinical intervention or pathological criteria.

[0301] Risk threshold: refers to the numerical standard that is pre-set by the system to divide the boundaries of different health risk levels in a continuous numerical space.

[0302] Step 1: Obtain the assessment input values ​​and preset risk thresholds: The system reads the corrected residual quantification values ​​calculated in the previous steps. Simultaneously, based on the target child's age group and gender attributes, the system retrieves the corresponding risk thresholds from the system's backend database. The system typically sets two thresholds: a first risk threshold and a second risk threshold. These threshold values ​​are derived from the statistical percentiles of the residual distribution in a large-scale, homogeneous group of healthy children (for example, the value corresponding to the 90th percentile is set as the first risk threshold, ranging from [0.03, 0.06]; the value corresponding to the 99th percentile is set as the second risk threshold, ranging from [0.07, 0.12]).

[0303] Step 2: Perform interval segmentation matching operation: The system takes the corrected residual quantization value as an independent variable, substitutes it into the preset interval segmentation matching function, determines its numerical interval through logical comparison operation, and maps the corresponding classification level label.

[0304] ;

[0305] Among them, the segmented matching function The specific judgment criteria are as follows:

[0306] like ,but ;

[0307] like ,but ;

[0308] like ,but ;

[0309] in, This indicates the final health risk level matched and output by the system;

[0310] This represents a function for matching interval segments;

[0311] This represents the corrected residual quantization value obtained from the preceding steps;

[0312] This represents the preset first risk threshold.

[0313] This represents the preset second risk threshold.

[0314] These labels indicate a level of health risk, representing normal or low risk.

[0315] This indicates a level label representing a critical or moderate health risk in the developmental stage;

[0316] These are labels indicating levels of developmental deviation or high health risk.

[0317] Step 3: Generate and output the assessment report: After obtaining the specific health risk level label, the system encapsulates the target child's identity, natural time parameters, various characteristic values, and the final health risk level. The system then sends the encapsulated data packet to a terminal display device for visualization via a standard communication protocol, or stores it in the hospital's electronic medical record system.

[0318] In the aforementioned technology, the abstract multimodal computation residuals are transformed into intuitive health risk levels through interval mapping, which lowers the threshold for interpreting the assessment results and improves the usability of the system. The standardized risk levels output by the system can be directly connected to the clinical triage system or medical intervention decision tree, enabling children with low health risks to enter the routine follow-up procedure, while children with high health risks can trigger specialist medical intervention in a timely manner, thereby improving the efficiency of health management.

[0319] A quantitative assessment system for children's health risks based on multimodal data fusion, comprising:

[0320] The data acquisition module is used to acquire the health dataset of the target children. The health dataset includes morphological feature vectors, biochemical feature sequences, and corresponding natural time parameters covering the target time window.

[0321] The feature extraction module is used to perform multidimensional feature reduction on biochemical feature sequences and extract maturity characterization factors that reflect tissue growth rate.

[0322] The time parameter correction module is used to calculate the weight compensation of the natural time parameter based on the maturity characterization factor, generate the physiological offset step size, and perform superposition correction on the natural time parameter based on the physiological offset step size to obtain the corrected time parameter.

[0323] The initial residual calculation module is used to retrieve the corresponding baseline morphological reference vector from the preset standard developmental benchmark database based on the correction time parameter, and calculate the spatial distance between the morphological feature vector and the baseline morphological reference vector to obtain the initial residual value.

[0324] The residual correction quantization module is used to calculate the sensitivity correction coefficient using the maturity characterization factor, and to perform a weighted mapping between the sensitivity correction coefficient and the initial residual value to obtain the corrected residual quantization value. The sensitivity correction coefficient is a weighted value used to measure the strength of the system's response to individual biochemical metabolic abnormalities.

[0325] The risk assessment and output module is used to match the corresponding health risk level and output it based on the corrected residual quantification value.

[0326] The technical scope of this invention is not limited to the content described above. Those skilled in the art can make various modifications and variations to the above embodiments without departing from the technical concept of this invention, and all such modifications and variations should fall within the protection scope of this invention.

Claims

1. A method for quantitative assessment of children's health risks based on multimodal data fusion, characterized in that, Includes the following steps: Obtain the health dataset of the target child, which includes morphological feature vectors, biochemical feature sequences, and corresponding natural time parameters covering the target time window; Multidimensional feature reduction was performed on the biochemical feature sequences to extract maturity characterization factors that reflect tissue growth rate; The natural time parameters are weighted and compensated based on the maturity characterization factors to generate the physiological offset step size. The natural time parameters are then superimposed and corrected based on the physiological offset step size to obtain the corrected time parameters. Based on the corrected time parameter, the corresponding baseline morphological reference vector is retrieved from the preset standard developmental benchmark database, and the spatial distance between the morphological feature vector and the baseline morphological reference vector is calculated to obtain the initial residual value. The sensitivity correction coefficient is calculated using the maturity characterization factor, and the sensitivity correction coefficient is weighted and mapped with the initial residual value to obtain the corrected residual quantification value. The sensitivity correction coefficient is a weighted value used to measure the strength of the system's response to individual biochemical metabolic abnormalities. Based on the corrected residual quantization value, match the corresponding health risk level and output it.

2. The method for quantitative assessment of children's health risks based on multimodal data fusion according to claim 1, characterized in that: The sensitivity correction coefficient is calculated using maturity characterization factors, and then a weighted mapping is performed between the sensitivity correction coefficient and the initial residual value to obtain the corrected residual quantification value. This includes the following steps: Calculate the absolute value of the difference between the maturity characterization factor and the preset maturity median, and then perform a ratio operation between the absolute value of the difference and the preset maturity median to obtain the absolute deviation rate that characterizes the degree of deviation of the metabolic state. The absolute deviation rate is input into a preset logical growth function to obtain a value between 0 and 1 as the sensitivity correction coefficient, wherein the sensitivity correction coefficient is positively correlated with the absolute deviation rate; The initial residual value is multiplied by the sensitivity correction coefficient to obtain the residual correction component. The residual correction component is then algebraically compared with the initial residual value to obtain the corrected residual quantization value.

3. The method for quantitative assessment of children's health risks based on multimodal data fusion according to claim 2, characterized in that: The preset maturity median is dynamically obtained through the following steps: Extracting group attribute features of target children from a health dataset, the group attribute features including geographic coding and environmental exposure parameters; Based on the characteristics of the group attributes, a homogeneous subset of children's samples that match the group are retrieved from the standard developmental benchmark database; Maturity characterization factors were extracted from a homogeneous subset of children's samples, and the extracted results were aggregated and calculated using the median. The calculated result was then determined as the preset maturity median.

4. The method for quantitative assessment of children's health risks based on multimodal data fusion according to claim 2, characterized in that: The specific formula for the logical growth function is as follows: ； in: This represents the absolute deviation rate. This is the sensitivity correction factor; Basic sensitivity; This represents the saturation gain limit. Gain sensitivity; This is due to deviation from the critical threshold; It is a natural constant.

5. The method for quantitative assessment of children's health risks based on multimodal data fusion according to claim 4, characterized in that: The parameter combination consisting of baseline sensitivity, saturation gain limit, gain sensitivity, and deviation from critical threshold is dynamically configured based on the age group and gender characteristics of the target children, specifically including: Identify the age and gender attributes of target children from health datasets, and categorize them into preset age groups based on their age attributes; Using age group and gender attributes as a joint index, the system retrieves parameter combinations that match individuals at different physiological stages from a preset configuration matrix and substitutes them into a logistic growth function to calculate the sensitivity correction coefficient.

6. The method for quantitative assessment of children's health risks based on multimodal data fusion according to claim 1, characterized in that: Multidimensional feature reduction was performed on the biochemical feature sequences to extract maturity characterization factors reflecting tissue growth rate, specifically including: Perform temporal difference operations on each biochemical component in the biochemical feature sequence, calculate the rate of change of each biochemical component in the current acquisition cycle relative to the historical acquisition cycle, and construct an evolutionary feature vector that reflects the biochemical trend. The evolutionary feature vector is input into a preset maturity contribution matrix, and a linear projection operation is performed to extract latent feature components from high-dimensional biochemical data. Calculate the cumulative contribution weights of latent feature components to obtain maturity characterization factors that represent the real-time growth dynamics of an individual.

7. The method for quantitative assessment of children's health risks based on multimodal data fusion according to claim 1, characterized in that: The natural time parameters are weighted and compensated based on maturity characterization factors to generate a physiological offset step size. The natural time parameters are then superimposed and corrected based on this physiological offset step size to obtain the corrected time parameters, specifically including: Using natural time parameters as an index, the standard maturity benchmark value corresponding to the current age is extracted from a preset physiological development comparison table; The difference between the maturity characterization factor and the standard maturity benchmark value is calculated to obtain the deviation increment that reflects the speed of physiological development. The deviation increment is multiplied by a preset time dimension mapping operator to generate a physiological offset step size in units of time. Using natural time parameters as the original time series coordinates and physiological offset step size as the translation vector, the corrected time parameters are obtained through algebraic addition.

8. The method for quantitative assessment of children's health risks based on multimodal data fusion according to claim 1, characterized in that: Based on the corrected time parameter, the corresponding baseline morphological reference vector is retrieved from the preset standard developmental benchmark database, and the spatial distance between the morphological feature vector and the baseline morphological reference vector is calculated to obtain the initial residual value, specifically including: Using the corrected time parameter as the query key, retrieve data from two adjacent standard time points in the standard developmental benchmark database; If the value of the corrected time parameter falls within the time interval formed by two adjacent standard time nodes, the reference vectors corresponding to the two adjacent standard time nodes are obtained respectively, and the reference vector is fitted and generated based on the distance distribution weight of the corrected time parameter within the time interval. If the absolute value of the difference between the corrected time parameter and any standard time node is less than the preset tolerance threshold, then the reference vector corresponding to the standard time node is directly used as the reference shape reference vector. Extract the components of height, weight and body mass index from the morphological feature vector and map them to a preset Euclidean metric space; The Euclidean distance between the morphological feature vector and the baseline morphological reference vector in the Euclidean metric space is calculated to obtain the initial residual value representing the absolute developmental deviation.

9. The method for quantitative assessment of children's health risks based on multimodal data fusion according to claim 8, characterized in that: The preset tolerance threshold is an adaptive tolerance threshold dynamically generated based on the developmental stage of the correction time parameter, specifically including: In the standard developmental benchmark database, the morphological benchmark components corresponding to two adjacent standard time nodes are extracted, and the absolute value of the first derivative of the morphological benchmark components in the time interval is calculated as the evolution slope characterizing the local growth rate. The preset base time tolerance and evolution slope are inversely mapped, and an adaptive tolerance threshold is output based on the calculation result.

10. A quantitative assessment system for children's health risks based on multimodal data fusion, characterized in that, include: The data acquisition module is used to acquire the health dataset of the target child. The health dataset includes morphological feature vectors, biochemical feature sequences, and corresponding natural time parameters covering the target time window. The feature extraction module is used to perform multidimensional feature reduction on biochemical feature sequences and extract maturity characterization factors that reflect tissue growth rate. The time parameter correction module is used to calculate the weight compensation of the natural time parameter based on the maturity characterization factor, generate the physiological offset step size, and perform superposition correction on the natural time parameter based on the physiological offset step size to obtain the corrected time parameter. The initial residual calculation module is used to retrieve the corresponding baseline morphological reference vector from the preset standard developmental benchmark database based on the correction time parameter, and calculate the spatial distance between the morphological feature vector and the baseline morphological reference vector to obtain the initial residual value. The residual correction quantization module is used to calculate the sensitivity correction coefficient using the maturity characterization factor, and to perform a weighted mapping between the sensitivity correction coefficient and the initial residual value to obtain the corrected residual quantization value. The sensitivity correction coefficient is a weighted value used to measure the strength of the system's response to individual biochemical metabolic abnormalities. The risk assessment and output module is used to match the corresponding health risk level and output it based on the corrected residual quantification value.

Images