Children asthmatic attack risk assessment and prediction method

Abstract

The invention discloses a children asthmatic attack risk assessment and prediction method, and relates to the technical field of medical data processing, and the method comprises the steps: collecting the physiological data and environment humidity data of a child; performing fluctuation analysis on the respiratory rate data to obtain a respiratory fluctuation index, performing correlation analysis on the heart rate data and the body temperature data to obtain body state parameters, and performing cross calculation to generate an initial score of the asthma risk; calling a reference data set matched with the age of the child from a preset historical database, and performing comparative analysis on the initial score of the asthma risk and the reference data set to obtain a correction coefficient; and multiplying the initial asthma risk score by the correction coefficient to generate a final risk prediction value, and judging the asthma attack risk level according to a numerical value interval of the final risk prediction value. According to the invention, children's physiological data and environmental humidity data are collected, and a data processing and analysis method is combined, so that children's physical conditions and environmental factors are monitored in real time, and the risk of asthmatic attack is assessed.

Description

Technical Field

[0001] The present invention relates to the technical field of medical data processing, and in particular to a method for assessing and predicting the risk of childhood asthma attacks. Background Art

[0002] As a common chronic respiratory disease, the incidence of childhood asthma continues to rise worldwide. Traditional asthma management mainly relies on clinical symptom observation and lung function testing. This passive monitoring method often makes it difficult to achieve early warning of asthma attacks. With the rapid development of Internet of Things technology and biosensors, intelligent health monitoring systems based on multi-source heterogeneous data have gradually become a research hotspot. Existing childhood asthma monitoring technologies mainly use single physiological parameter monitoring or simple multi-parameter superposition analysis. These methods still have obvious deficiencies in terms of continuity of data collection, fusion analysis of multi-dimensional data, and individualized evaluation. In particular, in key technical links such as the coupling analysis of environmental factors and physiological indicators, the real-time nature of dynamic risk assessment, and the adaptability of prediction models, a systematic solution has not yet been formed.

[0003] The main technical difficulties in the field of childhood asthma monitoring include: the traditional single-parameter monitoring method cannot fully reflect the respiratory status of children, and the data collection process is often intermittent, affecting the accuracy of the assessment; secondly, the existing multi-parameter fusion algorithm generally adopts a linear superposition model, which fails to fully consider the nonlinear correlation effect and time series characteristics between parameters; thirdly, the mechanism of environmental factors affecting asthma attacks is complex, and existing technologies rarely consider the dynamic coupling relationship between environmental parameters and physiological indicators; finally, the physiological characteristics of children of different age groups vary significantly, and there is a lack of targeted individualized evaluation standards and adaptive prediction models. These technical bottlenecks have seriously restricted the clinical application effect of the asthma early warning system. Summary of the invention

[0004] In view of the problems existing in the prior art, the present invention proposes a method for assessing and predicting the risk of asthma attacks in children, which can solve the problems mentioned in the background technology.

[0005] The technical solution is as follows: In a first aspect, the present invention provides a method for assessing and predicting the risk of asthma attacks in children, comprising: Collecting the child's respiratory rate data, heart rate data, body temperature data and environmental humidity data, and inputting the respiratory rate data, heart rate data, body temperature data and environmental humidity data into a preset data processing module; Based on the data processing module, the respiratory frequency data is subjected to fluctuation analysis to obtain a respiratory fluctuation index, and the heart rate data is subjected to correlation analysis with the body temperature data to obtain a body state parameter; Perform multi-dimensional cross-calculation on the respiratory fluctuation index, the physical state parameter and the environmental humidity data to generate an initial asthma risk score; Retrieving a reference data set matching the age of the child from a preset historical database, and comparing and analyzing the initial asthma risk score with the reference data set to obtain a correction coefficient; The initial asthma risk score is multiplied by the correction coefficient to generate a final risk prediction value, and the asthma attack risk level is determined according to the numerical range of the final risk prediction value.

[0006] As a preferred embodiment of the method for assessing and predicting the risk of asthma attacks in children of the present invention, obtaining the physical state parameters includes: The respiratory frequency data are processed in sections using a sliding window method, the standard deviation and mean of the respiratory frequency data in each sliding window are calculated, and the respiratory fluctuation rate is calculated; The respiratory fluctuation rate within 24 consecutive hours was accumulated and summed to obtain the respiratory fluctuation index; At the same time, the heart rate data is correlated with the body temperature data to obtain a standard heart rate score; Convert the body temperature data into a body temperature change curve and calculate the temperature difference value; A state assessment is performed on the heart rate standard score and the temperature difference value to obtain the body state parameter.

[0007] As a preferred embodiment of the method for assessing and predicting the risk of childhood asthma attacks of the present invention, the respiratory fluctuation rate obtains a basic fluctuation value based on the discrete degree of the respiratory signal, and then introduces the rate of change of the respiratory state as an adjustment factor; when the respiratory fluctuation index of the respiratory pattern is detected to exceed a preset threshold, it indicates that the stability of the respiratory system decreases, and at this time, the basic fluctuation value is amplified by the adjustment coefficient, and finally the respiratory fluctuation rate is obtained; The calculation of the respiratory fluctuation index includes: Acquire the respiratory fluctuation rate for 24 consecutive hours; construct a periodic adjustment function based on the circadian rhythm characteristics of the human body; perform weighted processing on the respiratory fluctuation rates in different time periods using the periodic adjustment function; accumulate and sum the weighted respiratory fluctuation rates to obtain the respiratory fluctuation index; The calculation of the heart rate standard score includes: Based on the deviation level of the measured heart rate relative to the basal heart rate, the deviation level is standardized by the age coefficient. On this basis, the heart rate change rate is used as a dynamic adjustment factor. When rapid changes in heart rate are detected, the standard score will be enhanced or suppressed accordingly through the dynamic adjustment coefficient.

[0008] As a preferred embodiment of the method for assessing and predicting the risk of asthma attacks in children of the present invention, the generation of the initial asthma risk score includes: Extracting time series features from the environmental humidity data and building a humidity impact model; Calculating a dynamic sensitivity index based on the humidity impact model; the dynamic sensitivity index reflects the degree of irritation of the child's respiratory tract caused by changes in environmental humidity; Performing nonlinear mapping on the respiratory fluctuation index and constructing a dynamic weight function of body state parameters; The initial asthma risk score is calculated based on the above parameter combination and dynamically calibrated.

[0009] As a preferred embodiment of the method for assessing and predicting the risk of asthma attacks in children of the present invention, wherein: The method for constructing the humidity impact model includes: Setting a reference humidity value as a basic level; constructing multiple Gaussian functions with characteristic time points, fluctuation amplitudes, and time scale parameters; superimposing the reference humidity value with multiple Gaussian functions to obtain the humidity impact model; The dynamic sensitivity index The calculation includes: Obtaining the first-order derivative of the humidity impact model with respect to time to obtain the humidity change rate; Obtaining the second-order derivative of the humidity impact model with respect to time to obtain the humidity change acceleration; The humidity change acceleration is multiplied by a preset acceleration influence factor and then added to 1 to obtain a dynamic adjustment coefficient; The absolute value of the humidity change rate is multiplied by the dynamic adjustment coefficient, and an integral operation is performed within the observation time period; The integration result is divided by the observation time period to obtain the dynamic sensitivity index.

[0010] As a preferred embodiment of the method for assessing and predicting the risk of asthma attacks in children of the present invention, the nonlinear mapping method for the respiratory fluctuation index includes: Calculate the difference between the respiratory fluctuation index and a preset fluctuation threshold; normalize the difference using a preset scale parameter to obtain a relative deviation; input the relative deviation into a hyperbolic tangent function to obtain a modulation amount within the range of [-1,1]; multiply the modulation amount by a preset modulation coefficient and add the result to 1 to obtain a mapping coefficient; multiply the mapping coefficient by the respiratory fluctuation index to obtain a respiratory fluctuation index after nonlinear mapping; The dynamic weight calculation method of the body state parameter includes: Calculate the difference between the body state parameter and the preset reference state value; multiply the difference by the preset steepness coefficient and input the Sigmoid function to obtain the basic weight; calculate the time change rate of the body state parameter; multiply the absolute value of the time change rate by the preset change rate influence coefficient and add the resultant value to 1 to obtain the dynamic adjustment coefficient; multiply the basic weight by the dynamic adjustment coefficient to obtain the dynamic weight of the body state parameter.

[0011] As a preferred embodiment of the method for assessing and predicting the risk of asthma attacks in children of the present invention, the determination of the correction coefficient includes: According to the child's age characteristics, the calibrated risk score and its dynamic change characteristics, the most relevant historical data is determined through nonlinear weight allocation; Based on the filtered historical data, the benchmark value is dynamically adjusted according to the changes in the current risk score; Compare the calibrated risk score with the dynamic benchmark and calculate the final correction factor; The correction coefficient includes a static correction amount and a dynamic adjustment amount; The calculation of the static correction amount includes: Obtain the difference between the calibrated respiratory signal and the reference function; divide the difference by a preset reference standard deviation to obtain a normalized deviation; input the normalized deviation into a hyperbolic tangent function to obtain a static correction value in the interval [-1,1]; multiply the static correction value by a preset main correction coefficient to obtain a final static correction amount; The calculation of the dynamic adjustment amount includes: Calculate the derivative of the calibrated respiratory signal with respect to time; obtain the absolute value of the derivative; divide the absolute value by the sum of the absolute value and 1 to obtain a normalized change rate in the interval [0,1]; perform a square root operation on the normalized change rate; multiply the operation result by a preset dynamic adjustment coefficient and then add the result to 1 to obtain a dynamic adjustment amount; The static correction amount is multiplied by the dynamic adjustment amount and then added to 1 to obtain the correction coefficient.

[0012] As a preferred embodiment of the method for assessing and predicting the risk of asthma attacks in children of the present invention, the method for dynamically adjusting the baseline value includes: Establishing static benchmark items, including: using the preset reference mean as the basic benchmark value; Establishing a periodic modulation term includes: converting the current time into a phase angle with a period of 24 hours; inputting the phase angle into a sine function; multiplying a calculation result of the sine function by a product of a preset reference standard deviation and a preset periodic modulation coefficient to obtain a periodic modulation amount; Establishing a dynamic compensation term includes: obtaining a calibrated breathing signal at a current moment; obtaining a calibrated breathing signal at a previous moment; calculating a difference between the calibrated breathing signal at the current moment and the previous moment; multiplying the difference by a preset change rate influence coefficient to obtain a dynamic compensation amount; The basic reference value, the periodic modulation amount and the dynamic compensation amount are added together to obtain a dynamically adjusted reference value.

[0013] In a second aspect, the present invention provides a system for assessing and predicting the risk of asthma attacks in children, comprising: A collection module, used to collect the child's respiratory rate data, heart rate data, body temperature data and environmental humidity data, and input the respiratory rate data, heart rate data, body temperature data and environmental humidity data into a preset data processing module; A data processing module, configured to perform fluctuation analysis on the respiratory frequency data based on the data processing module to obtain a respiratory fluctuation index, and simultaneously perform correlation analysis on the heart rate data and the body temperature data to obtain a body state parameter; An initial scoring module, used to perform multi-dimensional cross-calculation on the respiratory fluctuation index, the physical state parameter and the environmental humidity data to generate an initial asthma risk score; A correction module, used to retrieve a reference data set matching the age of the child from a preset historical database, compare and analyze the initial asthma risk score with the reference data set, and obtain a correction coefficient; The prediction module is used to multiply the initial asthma risk score by the correction coefficient to generate a final risk prediction value, and determine the asthma attack risk level according to the numerical range of the final risk prediction value.

[0014] The beneficial effects of the present invention are as follows: by comprehensively collecting children's respiratory rate, heart rate, body temperature and environmental humidity data, combined with data processing and analysis methods, it is possible to monitor children's physical condition and environmental factors in real time and accurately assess the risk of asthma attacks. Through fluctuation analysis, correlation analysis and multi-dimensional cross-calculation, respiratory fluctuation index and physical state parameters are generated, and further combined with the influence of environmental humidity, a dynamic risk assessment model is constructed to achieve personalized risk prediction. This method can dynamically adjust the risk score and provide accurate risk level prediction, thereby providing a scientific basis for childhood asthma management, effectively preventing asthma attacks, and improving the accuracy and timeliness of children's health management. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them: Figure 1 This is a flow chart of a method for assessing and predicting the risk of asthma attacks in children. DETAILED DESCRIPTION

[0016] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0017] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0018] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0019] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0020] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0021] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0022] Example 1: Reference Figure 1 , which is the first embodiment of the present invention, provides a method for assessing and predicting the risk of asthma attacks in children, comprising: S1: collecting the child's respiratory rate data, heart rate data, body temperature data and environmental humidity data, and inputting the respiratory rate data, heart rate data, body temperature data and environmental humidity data into a preset data processing module; The respiratory rate data, heart rate data, body temperature data and environmental humidity data of the child are collected, wherein the respiratory rate data is collected every 10 minutes by a non-contact photoelectric sensor installed at the head of the child's bed, and the photoelectric sensor is composed of an infrared transmitting tube and a receiving tube with a wavelength of 940nm; the heart rate data is continuously monitored and collected by a smart bracelet with a Bluetooth transmission function, and the smart bracelet uses photoelectric volumetric pulse wave recording to obtain the heart rate signal; the body temperature data is collected by an attached temperature sensor, and the attached temperature sensor uses a medical-grade thermistor with a temperature measurement accuracy of ±0.1°C; the environmental humidity data is collected by a digital humidity sensor installed indoors, and the sampling frequency of the digital humidity sensor is once every 5 minutes, and the measurement range is 0-100%RH; the respiratory rate data, the heart rate data, the body temperature data and the environmental humidity data are transmitted to a preset data processing module in real time through a wireless transmission module, and the data processing module timestamps and stores the received data; wherein the data processing module includes a data acquisition unit, a data storage unit and a data cleaning unit, and the data cleaning unit screens and removes abnormal data.

[0023] S2: performing fluctuation analysis on the respiratory frequency data based on the data processing module to obtain a respiratory fluctuation index, and performing correlation analysis on the heart rate data and the body temperature data to obtain a body state parameter; The respiratory rate data is subjected to fluctuation analysis based on the data processing module, wherein the data processing module performs segmented processing on the respiratory rate data using a sliding window method, wherein the time length of the sliding window is 30 minutes, and the window sliding step is 5 minutes.

[0024] Calculate the standard deviation of the respiratory rate data within each sliding window and mean , and calculate the respiratory fluctuation rate More specifically, the basic fluctuation level is calculated through the relationship between the standard deviation and the mean, and the basic fluctuation is adjusted using the dynamic change characteristics of the respiratory state. When the respiratory state changes rapidly, it means that the respiratory system is in an unstable state, and the calculation result of the fluctuation rate will be increased accordingly, as shown in the following formula: ; in, is the window standard deviation of the respiratory rate, is the exponential moving average of the respiratory rate, is the change rate influence coefficient, is the rate of change of the mean; The respiratory fluctuation rate within 24 consecutive hours is accumulated and summed to obtain the respiratory fluctuation index. ,More specifically, the calculation process introduces the influence of circadian rhythm, so that the volatility in different time periods has different weights, as shown in the following formula: ; in, The circadian rhythm regulation coefficient is 0.15. is the time series index, For the The respiratory fluctuation rate at each time point, To simulate the 24-hour cycle of physiological rhythm fluctuations.

[0025] At the same time, the heart rate data and the body temperature data are correlated and analyzed, and the correlation analysis includes: standardizing the heart rate data according to age characteristics to obtain a heart rate standard score , the heart rate standard score The calculation method uses (measured heart rate - basic heart rate) / age coefficient, which is based on the degree of deviation between the measured heart rate and the basic heart rate, and takes into account the influence of age factors. At the same time, the dynamic characteristics of heart rate changes are introduced as adjustment factors. When the heart rate changes rapidly, it indicates that cardiovascular regulation is abnormal, and the standard score will be adjusted accordingly. This calculation method takes into account both the static deviation of the heart rate level and the dynamic characteristics of the heart rate changes, and can more comprehensively reflect the cardiovascular function status, as shown in the following formula: ; in, To measure heart rate, is the basic heart rate, is the age coefficient, The dynamic adjustment coefficient is 0.25. is the heart rate change rate.

[0026] The body temperature data is converted into a body temperature change curve, the peak value and the trough value of the body temperature change curve are extracted, and the temperature difference value is calculated. , as shown below: ; in, The temperature acceleration influence coefficient is 0.3, is the second-order derivative of temperature, is the peak temperature of body temperature fluctuation, It is the trough temperature of body temperature fluctuation.

[0027] The heart rate standard score The temperature difference Substitute into the preset state assessment equation for calculation, where and is the weight coefficient obtained based on clinical data, and the calculation result of the state assessment equation is the body state parameter , as shown below: ; in, The heart rate weight coefficient is 0.4. is the temperature difference weight coefficient 0.3, The interaction coefficient is 0.2, The comprehensive impact coefficient is 0.1.

[0028] S3: performing multi-dimensional cross-calculation on the respiratory fluctuation index, the physical state parameter and the environmental humidity data to generate an initial asthma risk score; The time series features of the environmental humidity data are extracted, and a humidity impact model is constructed using an improved Gaussian kernel function group. The time response characteristics of the Gaussian kernel function match the physiological response characteristics of the children's respiratory tract to humidity changes.

[0029] This function is used to describe the dynamic change characteristics of environmental humidity over time. First, a baseline humidity value is set as the basic level, and then multiple Gaussian functions are superimposed to simulate humidity fluctuations at different time points. Each Gaussian function has its characteristic time point, fluctuation amplitude and time scale parameters, which together determine the location, intensity and duration of the fluctuation. In this way, complex humidity change patterns can be accurately portrayed, including sudden changes and continuous fluctuations. As shown in the following formula: ; in, is the base humidity value, is the humidity fluctuation amplitude, is the current time point, is the total number of characteristic time points, is the characteristic time point, is the humidity effect model, is the time scale parameter, which is dynamically adjusted by an adaptive algorithm: ; in, The base time scale is 30 minutes. The adaptation coefficient is 0.3, Indicates at a characteristic time point The humidity value calculated at .

[0030] Calculation of dynamic sensitivity index based on the humidity impact model The dynamic sensitivity index reflects the degree of irritation to children's respiratory tract caused by changes in environmental humidity. This index evaluates the dynamic characteristics of humidity changes. First, the rate of change of humidity over time (first-order derivative) is calculated to reflect the speed of humidity change. At the same time, the acceleration of humidity change (second-order derivative) is introduced as an adjustment factor. When the humidity change accelerates or decelerates, the sensitivity is adjusted by the acceleration influencing factor. This comprehensive effect is integrated over the entire observation period and normalized by the observation time period to finally obtain a sensitivity index that reflects the dynamic change characteristics of humidity. As shown in the following formula: ; in, The observation time period is set to 6 hours. The acceleration impact factor is 0.25; the dynamic sensitivity index is updated every 30 minutes and is averaged with historical data: ; in, For the current time point The smoothed dynamic sensitivity index, For the previous time point The smoothed dynamic sensitivity index, For the current time point The original dynamic sensitivity index, The smoothing factor is 0.3.

[0031] The respiratory fluctuation index RVI is nonlinearly mapped to enhance the response sensitivity to abnormal fluctuations, specifically including: Calculate the difference between the respiratory fluctuation index and the preset fluctuation threshold; normalize the difference using a preset scale parameter to obtain a relative deviation; input the relative deviation into a hyperbolic tangent function to obtain a modulation amount in the range of [-1,1]; multiply the modulation amount by a preset modulation coefficient and add the result to 1 to obtain a mapping coefficient; multiply the mapping coefficient by the respiratory fluctuation index to obtain a respiratory fluctuation index after nonlinear mapping, as shown in the following formula: ; in, The modulation coefficient is 0.3, is the fluctuation threshold, is the scale parameter, Mapping parameters for respiratory fluctuation index, set according to age segmentation: Under 3 years old: = 15; 3-6 years old: = 12; 6-12 years old: = 10.

[0032] Constructing the body state parameters The dynamic weight function is used to realize adaptive weighting of different state intervals. More specifically, the calculation method of dynamic weight includes: Calculate the difference between the body state parameter and the preset reference state value; multiply the difference by the preset steepness coefficient and input the Sigmoid function to obtain the basic weight; calculate the time change rate of the body state parameter; multiply the absolute value of the time change rate by the preset change rate influence coefficient and add the result to 1 to obtain the dynamic adjustment coefficient; multiply the basic weight by the dynamic adjustment coefficient to obtain the dynamic weight of the body state parameter, as shown in the following formula: ; in, is the steepness coefficient, with a base value of 2.0 and increasing by 0.1 with age; is the reference state value, obtained through statistics of individual historical data; The influence coefficient of the rate of change is 0.2; is a dynamic weight function of the body state parameter, the dynamic weight function is updated every 15 minutes; Substitute the above parameters into the multidimensional cross-evaluation equation and calculate the initial asthma risk score RS through polynomial combination: ; Among them, is the learning rate parameter in the optimization algorithm, the weight coefficient to Adopt adaptive optimization algorithm to dynamically adjust: ; in, is the historical prediction error of each indicator, To optimize the learning rate parameter in the algorithm, is the adaptive dynamic weight.

[0033] Furthermore, considering the daily cycle variation characteristics, the initial asthma risk score RS is dynamically calibrated: ; in, To score the calibrated risk, The daily cycle adjustment coefficient is 0.1. The acceleration influence coefficient is 0.15; the calibrated risk score is divided into the following intervals: 0-0.3: Low risk 0.3-0.6: Medium risk 0.6-0.8: High risk 0.8-1.0: Extremely high risk.

[0034] S4: retrieving a reference data set matching the age of the child from a preset historical database, and comparing and analyzing the initial asthma risk score with the reference data set to obtain a correction coefficient; First, based on the child’s age characteristics, the calibrated risk score and its dynamic change characteristics, the most relevant historical data is determined through nonlinear weight allocation, as shown in the following formula: ; in, Indicates the degree of match between historical data and current data. is the age value of the sample in the database, is the actual age of the target child, is the standard deviation parameter of age matching, which controls the tolerance range of age matching. The calibrated risk score for the current moment, Scoring baseline risk in historical data, is the coupling coefficient (0.3), which controls the impact of risk score on matching. is the dynamic response coefficient (0.2), which adjusts the system's sensitivity to the rate of change of risk scores. is the time rate of change of risk score, is a natural exponential function.

[0035] Furthermore, based on the screened historical data, combined with the daily cycle characteristics and the changing trend of the risk score, an adaptive algorithm is used to dynamically adjust the benchmark value according to the change of the current risk score, so that the assessment result is more in line with individual characteristics, wherein the adaptive algorithm is shown in the following formula: ; in, is the average risk score of the reference group, is the standard deviation of the reference group, is the periodic modulation coefficient (0.15), which controls the amplitude of daily periodic fluctuations. is the change rate impact coefficient (0.25), which determines the impact of the risk score change on the benchmark. is the time interval used to calculate the score change, is the current time, is the adaptive reference benchmark, Score the calibrated risk at the current moment, for The calibrated risk score at time.

[0036] Furthermore, the calibrated risk score is compared with the dynamic benchmark, and the final correction coefficient is calculated by considering the score deviation and change trend. The correction coefficient includes static correction amount and dynamic adjustment amount; The calculation of the static correction amount includes: Obtain the difference between the calibrated respiratory signal and the reference function; divide the difference by a preset reference standard deviation to obtain a normalized deviation; input the normalized deviation into a hyperbolic tangent function to obtain a static correction value in the interval [-1,1]; multiply the static correction value by a preset main correction coefficient to obtain a final static correction amount; The calculation of the dynamic adjustment amount includes: Calculate the derivative of the calibrated respiratory signal with respect to time; obtain the absolute value of the derivative; divide the absolute value by the sum of the absolute value and 1 to obtain a normalized change rate in the interval [0,1]; perform a square root operation on the normalized change rate; multiply the operation result by a preset dynamic adjustment coefficient and then add the result to 1 to obtain a dynamic adjustment amount; The static correction amount is multiplied by the dynamic adjustment amount and then added to 1 to obtain the correction coefficient, as shown in the following formula: ; in, is the main correction coefficient (0.35), which controls the strength of the overall correction. is the dynamic adjustment coefficient (0.2), the influence of the adjustment change rate on the correction, is the hyperbolic tangent function, which is used to limit the correction value to a reasonable range. is the standardized rating bias, is the normalized rate of change term, is the correction factor.

[0037] S5: Multiplying the initial asthma risk score by the correction coefficient to generate a final risk prediction value, and determining the asthma attack risk level according to the numerical range of the final risk prediction value.

[0038] The initial asthma risk score is precisely multiplied by the correction coefficient, and the output precision is maintained at 4 decimal places based on the 32-bit floating point operation standard; the product operation result is used as the initial value of risk prediction, and the calculation result is updated every 5 minutes.

[0039] According to the experience of medical experts and clinical statistical data, the smoothed risk prediction values ​​are divided into five risk level intervals: Safety level: The final risk prediction value is ≤0.2, indicating that the risk of asthma attack is extremely low and the patient can maintain a normal life and work schedule; Alert level: 0.2<final risk prediction value≤0.4, indicating potential risk, and the monitoring frequency should be appropriately increased; Warning level: 0.4 < final risk prediction value ≤ 0.6, indicating that the risk is significantly increased and preventive medication is required; High-risk level: The final risk prediction value is >0.6, indicating a high risk of attack. It is recommended to take preventive medications immediately and be prepared for emergency treatment.

[0040] The threshold of risk level is dynamically adjusted according to the characteristics of children of different age groups: For children under 3 years old, considering their incomplete respiratory development, the thresholds of each level will be lowered by 0.05; For children aged 3-6 years, standard threshold settings were maintained; For children aged 6-12 years old, the threshold can be appropriately adjusted upward by 0.05 based on their immune capacity.

[0041] Furthermore, this embodiment also provides a system for assessing and predicting the risk of asthma attacks in children, comprising: A collection module, used to collect the child's respiratory rate data, heart rate data, body temperature data and environmental humidity data, and input the respiratory rate data, heart rate data, body temperature data and environmental humidity data into a preset data processing module; A data processing module, configured to perform fluctuation analysis on the respiratory frequency data based on the data processing module to obtain a respiratory fluctuation index, and simultaneously perform correlation analysis on the heart rate data and the body temperature data to obtain a body state parameter; An initial scoring module, used to perform multi-dimensional cross-calculation on the respiratory fluctuation index, the physical state parameter and the environmental humidity data to generate an initial asthma risk score; A correction module, used to retrieve a reference data set matching the age of the child from a preset historical database, compare and analyze the initial asthma risk score with the reference data set, and obtain a correction coefficient; The prediction module is used to multiply the initial asthma risk score by the correction coefficient to generate a final risk prediction value, and determine the asthma attack risk level according to the numerical range of the final risk prediction value.

[0042] This embodiment also provides a computer device, which is suitable for the method of assessing and predicting the risk of asthma attacks in children, and includes a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the method of assessing and predicting the risk of asthma attacks in children proposed in the above embodiment.

[0043] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0044] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for assessing and predicting the risk of asthma attacks in children proposed in the above embodiment is implemented.

[0045] The storage medium proposed in this embodiment and the data storage method proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be found in the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0046] Example 2: This is the second example of the present invention. This example provides a method for assessing and predicting the risk of asthma attacks in children. In order to verify the beneficial effects of the present invention, a scientific demonstration is carried out through simulation experiments.

[0047] In order to verify the effectiveness of the childhood asthma risk warning method proposed in the present invention, 60 asthma children from the respiratory department of a children's hospital were selected for a three-month follow-up monitoring study. The age distribution of the research subjects was between 2 and 10 years old, including 15 under 3 years old, 25 between 3 and 6 years old, and 20 between 6 and 12 years old. All research subjects were diagnosed with mild to moderate asthma by specialists and had at least one acute attack within 3 months before the start of the experiment.

[0048] The preliminary preparations for the experiment included: equipping each child with a set of standardized monitoring equipment, including a non-contact photoelectric sensor with a 940nm wavelength infrared transmitter and receiver (sampling accuracy ±2 times / minute), a smart bracelet with Bluetooth transmission function (heart rate sampling frequency 60Hz), a medical-grade thermistor attached temperature sensor (model: temperature measurement accuracy ±0.1℃) and a digital humidity sensor (measuring range 0-100%RH, accuracy ±2%RH). All equipment was professionally calibrated to ensure the accuracy of data collection.

[0049] The test experiment was carried out according to the steps of Example 1 to obtain the following experimental data: Table 1. Data analysis of clinical trial of childhood asthma risk early warning system Age Group Sample size Early warning accuracy (%) Missing reporting rate (%) False alarm rate (%) Average warning lead time (hours) Accuracy of traditional methods (%) System response time (minutes) 2-3 years old group 15 92.5 3.2 4.3 4.8 75.3 2.5 3-4 years old group 12 93.1 2.8 4.1 5.2 76.8 2.3 4-5 years old group 8 94.2 2.5 3.3 5.5 77.5 2.2 5-6 years old group 5 94.8 2.3 2.9 5.8 78.2 2.1 6-8 years old group 12 95.3 2.1 2.6 6.2 79.1 2.0 8-10 years old group 8 96.1 1.8 2.1 6.5 80.2 1.8 The analysis results based on the above experimental data show that the childhood asthma risk warning method proposed in the present invention has significant clinical application value. First of all, in terms of warning accuracy, the system showed excellent performance in all age groups, and the overall accuracy was maintained above 92.5%. Especially in the 8-10 year old group, the accuracy reached 96.1%, which is mainly due to the multi-dimensional cross-calculation method adopted by the present invention, which effectively integrates multiple key physiological indicators such as respiratory rate, heart rate, body temperature and environmental humidity.

[0050] From the perspective of missed alarm rate and false alarm rate, the system shows extremely high reliability. Even in the 2-3 year old age group with large fluctuations in physiological indicators, the missed alarm rate and false alarm rate are only 3.2% and 4.3% respectively, which are much lower than traditional monitoring methods. This advantage mainly comes from the improved Gaussian kernel function group and adaptive optimization algorithm adopted by the present invention, which can accurately capture the changing laws of physiological characteristics of children of different age groups.

[0051] The warning lead time is an important indicator for evaluating the effectiveness of the warning system. Data show that the system can issue warnings 4.8-6.5 hours in advance, which provides a sufficient time window for clinical intervention. It is worth noting that with the increase of age, the warning lead time tends to be gradually extended, which is related to the more typical symptoms and more stable physiological indicators of older children.

[0052] Compared with traditional early warning methods, the present invention has shown significant advantages in all age groups. The accuracy of traditional methods is between 75.3% and 80.2%, while the early warning accuracy of the present invention is generally 15-20 percentage points higher. This advantage is mainly reflected in: 1) Real-time collection and analysis of multi-dimensional data, avoiding the limitations brought by a single indicator; 2) Innovative introduction of environmental humidity factors to establish a more complete risk assessment model; 3) Adaptive algorithms are used to personalize modeling of the physiological characteristics of children of different age groups.

[0053] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for assessing and predicting the risk of asthma attacks in children, characterized by: include: Collecting the child's respiratory rate data, heart rate data, body temperature data and environmental humidity data, and inputting the respiratory rate data, heart rate data, body temperature data and environmental humidity data into a preset data processing module; Based on the data processing module, the respiratory frequency data is subjected to fluctuation analysis to obtain a respiratory fluctuation index, and the heart rate data is subjected to correlation analysis with the body temperature data to obtain a body state parameter; Perform multi-dimensional cross-calculation on the respiratory fluctuation index, the physical state parameter and the environmental humidity data to generate an initial asthma risk score; Retrieving a reference data set matching the age of the child from a preset historical database, and comparing and analyzing the initial asthma risk score with the reference data set to obtain a correction coefficient; The initial asthma risk score is multiplied by the correction coefficient to generate a final risk prediction value, and the asthma attack risk level is determined according to the numerical range of the final risk prediction value.

2. The method for assessing and predicting the risk of asthma attacks in children according to claim 1, characterized in that: Acquiring the body state parameter includes: The respiratory frequency data are processed in sections using a sliding window method, the standard deviation and mean of the respiratory frequency data in each sliding window are calculated, and the respiratory fluctuation rate is calculated; The respiratory fluctuation rate within 24 consecutive hours was accumulated and summed to obtain the respiratory fluctuation index; At the same time, the heart rate data is correlated with the body temperature data to obtain a standard heart rate score; Convert the body temperature data into a body temperature change curve and calculate the temperature difference value; A state assessment is performed on the heart rate standard score and the temperature difference value to obtain the body state parameter.

3. The method for assessing and predicting the risk of asthma attacks in children according to claim 2, characterized in that: The respiratory fluctuation rate obtains a basic fluctuation value based on the discrete degree of the respiratory signal, and then introduces the change rate of the respiratory state as a regulating factor; When the respiratory fluctuation index of the detected breathing pattern exceeds the preset threshold, it indicates that the stability of the respiratory system has decreased. At this time, the basic fluctuation value is amplified by the adjustment coefficient, and the respiratory fluctuation rate is finally obtained; The calculation of the respiratory fluctuation index includes: Acquire the respiratory fluctuation rate for 24 consecutive hours; construct a periodic adjustment function based on the circadian rhythm characteristics of the human body; perform weighted processing on the respiratory fluctuation rates in different time periods using the periodic adjustment function; accumulate and sum the weighted respiratory fluctuation rates to obtain the respiratory fluctuation index; The calculation of the heart rate standard score includes: Based on the deviation level of the measured heart rate relative to the basal heart rate, the deviation level is standardized by the age coefficient. On this basis, the heart rate change rate is used as a dynamic adjustment factor. When rapid changes in heart rate are detected, the standard score will be enhanced or suppressed accordingly through the dynamic adjustment coefficient.

4. The method for assessing and predicting the risk of asthma attacks in children according to claim 3, characterized in that: The generation of the initial asthma risk score includes: Extracting time series features from the environmental humidity data and building a humidity impact model; Calculating a dynamic sensitivity index based on the humidity impact model; the dynamic sensitivity index reflects the degree of irritation of the child's respiratory tract caused by changes in environmental humidity; Performing nonlinear mapping on the respiratory fluctuation index and constructing a dynamic weight function of body state parameters; The initial asthma risk score is calculated based on the above parameter combination and dynamically calibrated.

5. The method for assessing and predicting the risk of asthma attacks in children according to claim 4, characterized in that: The method for constructing the humidity impact model includes: Setting a reference humidity value as a basic level; constructing multiple Gaussian functions with characteristic time points, fluctuation amplitudes, and time scale parameters; superimposing the reference humidity value with multiple Gaussian functions to obtain the humidity impact model; The dynamic sensitivity index The calculation includes: Obtaining the first-order derivative of the humidity impact model with respect to time to obtain the humidity change rate; Obtaining the second-order derivative of the humidity impact model with respect to time to obtain the humidity change acceleration; The humidity change acceleration is multiplied by a preset acceleration influence factor and then added to 1 to obtain a dynamic adjustment coefficient; The absolute value of the humidity change rate is multiplied by the dynamic adjustment coefficient, and an integral operation is performed within the observation time period; The integration result is divided by the observation time period to obtain the dynamic sensitivity index.

6. The method for assessing and predicting the risk of asthma attacks in children according to claim 4, characterized in that: The nonlinear mapping method for the respiratory fluctuation index includes: Calculate the difference between the respiratory fluctuation index and a preset fluctuation threshold; normalize the difference using a preset scale parameter to obtain a relative deviation; input the relative deviation into a hyperbolic tangent function to obtain a modulation amount within the range of [-1,1]; multiply the modulation amount by a preset modulation coefficient and add the result to 1 to obtain a mapping coefficient; multiply the mapping coefficient by the respiratory fluctuation index to obtain a respiratory fluctuation index after nonlinear mapping; The dynamic weight calculation method of the body state parameter includes: Calculate the difference between the body state parameter and the preset reference state value; multiply the difference by the preset steepness coefficient and input the Sigmoid function to obtain the basic weight; calculate the time change rate of the body state parameter; multiply the absolute value of the time change rate by the preset change rate influence coefficient and add the resultant value to 1 to obtain the dynamic adjustment coefficient; multiply the basic weight by the dynamic adjustment coefficient to obtain the dynamic weight of the body state parameter.

7. The method for assessing and predicting the risk of asthma attacks in children according to claim 6, characterized in that: The determination of the correction coefficient includes: According to the child's age characteristics, the calibrated risk score and its dynamic change characteristics, the most relevant historical data is determined through nonlinear weight allocation; Based on the filtered historical data, the benchmark value is dynamically adjusted according to the changes in the current risk score; Compare the calibrated risk score with the dynamic benchmark and calculate the final correction factor; The correction coefficient includes a static correction amount and a dynamic adjustment amount; The calculation of the static correction amount includes: Obtain the difference between the calibrated respiratory signal and the reference function; divide the difference by a preset reference standard deviation to obtain a normalized deviation; input the normalized deviation into a hyperbolic tangent function to obtain a static correction value in the interval [-1,1]; multiply the static correction value by a preset main correction coefficient to obtain a final static correction amount; The calculation of the dynamic adjustment amount includes: Calculate the derivative of the calibrated respiratory signal with respect to time; obtain the absolute value of the derivative; divide the absolute value by the sum of the absolute value and 1 to obtain a normalized change rate in the interval [0,1]; perform a square root operation on the normalized change rate; multiply the operation result by a preset dynamic adjustment coefficient and then add the result to 1 to obtain a dynamic adjustment amount; The static correction amount is multiplied by the dynamic adjustment amount and then added to 1 to obtain the correction coefficient.

8. The method for assessing and predicting the risk of asthma attacks in children according to claim 7, characterized in that: The dynamic adjustment method of the reference value includes: Establishing static benchmark items, including: using the preset reference mean as the basic benchmark value; Establishing a periodic modulation term includes: converting the current time into a phase angle with a period of 24 hours; inputting the phase angle into a sine function; multiplying a calculation result of the sine function by a product of a preset reference standard deviation and a preset periodic modulation coefficient to obtain a periodic modulation amount; Establishing a dynamic compensation term includes: obtaining a calibrated breathing signal at a current moment; obtaining a calibrated breathing signal at a previous moment; calculating a difference between the calibrated breathing signal at the current moment and the previous moment; multiplying the difference by a preset change rate influence coefficient to obtain a dynamic compensation amount; The basic reference value, the periodic modulation amount and the dynamic compensation amount are added together to obtain a dynamically adjusted reference value.

9. A system for assessing and predicting the risk of asthma attacks in children, based on the method for assessing and predicting the risk of asthma attacks in children according to any one of claims 1 to 8, characterized in that: include: A collection module, used to collect the child's respiratory rate data, heart rate data, body temperature data and environmental humidity data, and input the respiratory rate data, heart rate data, body temperature data and environmental humidity data into a preset data processing module; A data processing module, configured to perform fluctuation analysis on the respiratory frequency data based on the data processing module to obtain a respiratory fluctuation index, and simultaneously perform correlation analysis on the heart rate data and the body temperature data to obtain a body state parameter; An initial scoring module, used to perform multi-dimensional cross-calculation on the respiratory fluctuation index, the physical state parameter and the environmental humidity data to generate an initial asthma risk score; A correction module, used to retrieve a reference data set matching the age of the child from a preset historical database, compare and analyze the initial asthma risk score with the reference data set, and obtain a correction coefficient; The prediction module is used to multiply the initial asthma risk score by the correction coefficient to generate a final risk prediction value, and determine the asthma attack risk level according to the numerical range of the final risk prediction value.

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