Medical advice information generation method based on patient identification

Abstract

The invention relates to the technical field of doctor's advice information generation based on patient identification, and discloses a doctor's advice information generation method based on patient identification. According to the method, by establishing a health data multi-dimensional uncertainty model, multi-dimensional health data are integrated, and scale differences are eliminated. Firstly, physiological data and medical history data are collected from a health record authorized by a patient, dimension differences of the data are eliminated through maximum-minimum normalization, all the data are made to be within the same range, and deviation caused by unit and range differences is avoided. Then, the fluctuation amplitude and correlation of the health data are calculated, the health indexes with large fluctuation are recognized, the mutual influence of the health indexes is clear, and a basis is provided for uncertainty propagation analysis. And finally, by allocating a weight for each health data and introducing a regulatory factor, the influence of the index is dynamically adjusted to adapt to the health conditions of different patients, so that the individuation and accuracy of doctor's advice optimization are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of generating medical order information based on patient identification, and in particular to a method for generating medical order information based on patient identification. Background Art

[0002] With the continuous development of medical technology, patient health management is gradually moving towards personalization and precision, especially in the generation and adjustment of medical orders. Through in-depth analysis of patient health data, doctors can provide patients with more personalized treatment plans. However, existing medical plans generally face multiple problems, including incomplete data, high data uncertainty, inaccurate health risk prediction, etc., which often lead to large errors in the generation and adjustment of medical orders.

[0003] Most existing medical order generation technologies rely on traditional medical record data and simple medical rules, lacking comprehensive modeling and uncertainty analysis of the multi-dimensional characteristics of patient health data. Traditional methods usually rely on historical medical data and doctor experience to formulate medical orders, but this method cannot adapt to the real-time changes and individual differences in patients' health status, and cannot effectively deal with the noise and uncertainty in health data. In order to improve the accuracy of medical orders, some existing technologies have introduced rule-based decision-making systems and combined with certain algorithms to process medical record data, but these methods still rely on fixed rules, lack flexibility, and cannot adaptively adjust medical orders. At the same time, the existing technology's modeling of the multi-dimensional characteristics of patient health data is also relatively rough, and cannot fully capture the complexity and uncertainty of patient health changes.

[0004] To this end, this case proposes a method for generating medical order information based on patient identification, which generates more personalized, accurate and safe medical order plans through more accurate health data analysis, uncertainty modeling and dynamic optimization algorithms. Summary of the invention

[0005] The present invention provides a method for generating medical advice information based on patient identification, which helps solve the problems mentioned in the above background technology.

[0006] The present invention provides the following technical solution: a method for generating medical advice information based on patient identification, comprising: S1. Establish a multidimensional uncertainty model for health data: Set the health indicator dataset, denoted as ; Collect multiple indicators from the patient's authorized health records; The multiple indicators include physiological data and medical history data. The collected data are added to the health indicator data set to obtain , where n is the total number of health data dimensions, It is the first Items of health indicator data; Perform maximum-minimum normalization on each health indicator data: ; in, and They are respectively The maximum and minimum values ​​of the health indicator data; is the value after maximum-minimum normalization; The maximum-minimum normalized value of each health indicator data is updated to the data at the corresponding position in the health indicator data set; Calculate the fluctuation range of each data in the health indicator data set; Calculate the health index data set Item and The correlation coefficient between the items of data; Get the dataset The mean value of ; Set a weight for each health data in the health indicator dataset, calculated as follows: ; in, is the regulating factor; For the Weighting coefficients of health indicator data; S2, uncertainty propagation analysis; S3. Optimization and generation of medical order uncertainty.

[0007] Optionally, the calculating of the fluctuation range of each data in the health indicator data set specifically includes: Get the health indicator data set The variance of the maximum-minimum normalized health indicator data is recorded as ; Get the health indicator data set The mean of the maximum-minimum normalized health indicator data is recorded as ; The fluctuation range is estimated for each data in the health indicator data set. The fluctuation range calculation formula is: ; in, , For the The fluctuation range of the health indicator data; Set the fluctuation range data set, denoted as ; All calculated fluctuation ranges are added to the fluctuation range dataset in sequence.

[0008] Optionally, the calculation of the health indicator data set Item and The correlation coefficient between the data items includes: Get the health indicator data set The variance of the maximum-minimum normalized health indicator data is recorded as ; Calculate the health index data set Item and The correlation coefficient between the items of data is recorded as , the calculation formula is: .

[0009] Optionally, the uncertainty propagation analysis specifically includes: Set the function of overall health risk as , specifically: ; in, For the Risk weights of health indicator data; For the The fluctuation range of the health indicator data; For the The maximum-minimum normalized value of the health indicator data; The influence of each health indicator data item on the overall risk is calculated by partial derivatives, and the sensitivity measurement formula is: ; Set the sensitivity distribution set, denoted as ; Each calculated Add to the sensitivity distribution set, we get ; Through correlation and sensitivity, the risk propagation matrix is ​​constructed and recorded as , used to describe the mutual influence between variables: ; in, The risk propagation matrix Middle Line Column data; is the first individual data; This is the health indicator dataset. Item and The correlation coefficient between the items of data; Calculate the total transmission effect for each variable in the health indicators dataset: ; For all Sort and get the maximum dissemination effect.

[0010] Optionally, the uncertainty optimization generation of the medical order specifically includes: Get the target health value of each data in the health indicator dataset, denoted as , This is the health indicator dataset. The target health value of the item data; Get the set of doctor's order parameters, denoted as u, and add the first Item data is recorded as ; Get the target dose corresponding to each data in the doctor's order parameter set, and The target dose of the data item is recorded as ; Get the expected fluctuation range of each health indicator data in the health indicator data set, and The expected volatility is recorded as ; Set the objective function of medical order optimization as : ; in, This is the health indicator dataset. The weight coefficient of the item data in the objective function; The first The weight coefficient of the item data in the objective function; This is the health indicator dataset. The weight coefficient of the fluctuation range of the item data in the objective function; is the total number of data in the medical order parameter set; Updating medical order parameters based on gradient descent method and volatility ; Set the iteration stop condition; After each iteration, the fluctuation amplitude make corrections; Get the final optimized Item medical order parameter, denoted as ; Get the final optimized health indicator data set The fluctuation range of the data item is recorded as ; Set the optimized medical order set, denoted as : .

[0011] Optionally, the doctor's order parameters are updated based on the gradient descent method. and volatility , including: Updating medical order parameters based on gradient descent method and volatility : ; ; , ; in, The first The value of the item data at the tth iteration; This is the health indicator dataset. The fluctuation range of the item data at the tth iteration; is the learning rate.

[0012] Optionally, the setting of the iteration stop condition specifically includes: Set the tolerance error value threshold, denoted as ; Set the maximum number of iterations, denoted as ; Get the value of the objective function at the tth iteration, denoted as ; Get the value of the objective function at the t+1th iteration, denoted as ; When the objective function When the change is less than the tolerance error threshold, stop the iteration: ; When the number of iterations is greater than the maximum number of iterations, the iteration is forced to stop: .

[0013] Optionally, after each iteration, the fluctuation amplitude Make corrections, including: ; in, This is the health indicator dataset. The fluctuation range of the item data at the tth iteration; This is the health indicator dataset. Correction coefficient of item data; The risk propagation matrix Middle Line Column data; The sensitivity distribution set Item data.

[0014] The present invention has the following beneficial effects: 1. By establishing a multidimensional uncertainty model for health data, this method solves the problem of how to integrate multidimensional health data and eliminate the scale differences between different data. Through maximum-minimum normalization processing, the dimensional differences between different health indicator data are eliminated, so that the data are within the same standard range, which is convenient for subsequent analysis and comparison. This step avoids the analytical deviation that may be caused by different data units and ranges. By calculating the fluctuation range and correlation of health data, it is possible to identify which health indicators have large fluctuations, and clarify the mutual influence between different health data. This provides a valuable reference for subsequent uncertainty propagation analysis. By assigning weights to each health data and introducing adjustment factors, this method can dynamically adjust the influence of health indicators according to the health status of different patients, making the data model more personalized and targeted in practical applications, thereby improving the accuracy and effectiveness of medical order generation.

[0015] 2. By obtaining the variance and mean of each health indicator data after maximum-minimum normalization, the volatility of each data can be accurately quantified. This step effectively solves the problem of how to convert volatility into quantifiable numerical values, so that the volatility of health data can be clearly presented for further analysis. By calculating the fluctuation range, this method can identify and quantify the fluctuation range of each health data, so that when facing multi-dimensional and different sources of health data, the uncertainty of the data can be accurately assessed. This not only enhances the understanding of the patient's health status, but also provides a reliable basis for subsequent health risk assessment, avoiding misjudgment caused by ignoring data fluctuations. Setting the fluctuation range data set and adding the fluctuation range of each health data in turn lays the foundation for subsequent uncertainty propagation analysis. This data set provides a comprehensive fluctuation range reference framework for the system, so that when analyzing the impact of health data on patient health, a comprehensive assessment can be made from multiple dimensions, improving the accuracy and comprehensiveness of the analysis. By quantifying the fluctuation range of health data, the changing trend of patient health status can be predicted more accurately. The introduction of this volatility data makes the formulation of medical advice and treatment plans more in line with the actual conditions of individual patients, reduces the possibility of misdiagnosis and overtreatment, and thus improves the treatment effect and patient satisfaction.

[0016] 3. By calculating the correlation coefficient, this method can effectively quantify the relationship between various health data and solve the problem of how to identify potential associations between multiple health indicators. Health data usually has multidimensional characteristics. Analyzing each indicator separately may not fully reflect the true health status. By calculating the correlation coefficient, it can reveal which health indicators have strong relationships and which relationships are weak, thereby providing strong support for data analysis. By calculating the correlation between various health indicators, the dependence between different health parameters can be deeply analyzed, avoiding the problem of relying solely on individual data points for health assessment. This method enables multidimensional data to complement each other during the analysis process, thereby enhancing the comprehensiveness of the assessment. For example, abnormal fluctuations in a certain health indicator may be caused by changes in other related indicators. Through correlation analysis, potential health problems can be discovered early. Calculating the correlation coefficient between health indicators helps to more accurately predict the health risks of patients. In the process of patient health assessment, correlation analysis can reveal the causal relationship and influence between different health data, especially in complex health conditions, which can help doctors discover potential health problems and intervene in time, reducing the possibility of misdiagnosis and missed diagnosis. By calculating the correlation coefficient between health data, a more accurate basis can be provided for the formulation of medical advice. By identifying highly relevant health indicators, it is possible to optimize the formulation of medical advice and ensure that it is more in line with the patient's actual health status. This not only improves the effectiveness of personalized treatment, but also reduces unnecessary interference factors during the treatment process.

[0017] 4. By constructing a risk propagation matrix, this step can effectively quantify the risk coupling relationship between different health indicators, and calculate the degree of influence of each health indicator on the overall health risk through sensitivity analysis. Traditional health risk analysis often focuses on a single indicator and ignores the interaction between various indicators. The implementation of this method can effectively solve this problem and make health risk assessment more comprehensive and accurate. By calculating the sensitivity of health indicators, it is possible to identify which health indicators contribute more to the overall health risk, helping doctors and medical personnel to focus on those key health factors, so as to develop more personalized and accurate treatment plans. For example, some health indicators contribute more to risk propagation and may be the key to potential health problems. Early intervention in these key indicators can significantly reduce the health risks of patients. By quantifying the risk propagation effects between different health indicators, the risk propagation matrix provides a comprehensive and dynamic health risk map, enabling decision makers to evaluate the impact of changes in various health indicators on overall health status from a global perspective. This process can effectively guide medical decision-making, such as adjusting treatment strategies, formulating health intervention measures, etc., to improve the accuracy and effectiveness of health management. The risk propagation matrix can provide a clear propagation path for uncertainties in health data, ensuring that the health early warning system can identify the source of risk in advance. The maximum propagation effect obtained through sorting can provide real-time monitoring priorities for the intelligent health monitoring system, giving priority to those health indicators with greater propagation effects, thereby improving the response speed of disease prevention and risk management.

[0018] 5. By obtaining the target health value of each health indicator data and optimizing the doctor's order parameters, this step can optimize the doctor's order plan according to the patient's specific situation. Traditional doctor's order generation methods are usually based on experience and general rules, and cannot accurately optimize the health status of each patient. Through this step, the doctor's order can be quantitatively analyzed based on individual health indicator data, fluctuation range and other factors, thereby improving the personalization and treatment effect of the medical plan. By adding the weight coefficient of the fluctuation range to the objective function and optimizing it, the adaptability of the doctor's order parameters and the fluctuation range of health data is ensured, thereby effectively reducing health risks. In practical applications, the patient's health status may fluctuate. By correcting the fluctuation range, it is ensured that the results after each iteration are more consistent with the patient's current health status, avoiding medical decision-making errors caused by not adapting to risk changes. By using the gradient descent method to update the parameters and setting the iteration stop condition, the optimized doctor's order can be continuously adjusted until an optimal solution is reached. The gradient descent method can gradually optimize the parameters and fluctuation range of the doctor's order, and accurately adjust the dosage of each doctor's order to match the patient's health status. In this way, not only can the treatment effect of patients be effectively improved, but also the treatment plan can be dynamically adjusted according to the changes in health data to ensure that the treatment is sustained and effective. By establishing an optimization objective function and correcting the fluctuation amplitude after each iteration, the final optimization result is ensured to adapt to the changes and sensitivity of the patient's health data, making medical decisions more accurate. Traditional medical decisions often rely on the doctor's experience and conventional treatment plans, while this method achieves accurate assessment of the patient's health risks in a data-driven manner, thereby providing a more scientific basis for decision-making. By continuously iteratively optimizing and correcting the fluctuation amplitude, the doctor's orders can adapt to the risk distribution and health status changes of different patients. With the dynamic changes of health data, this method can adjust the doctor's orders in real time to ensure the flexibility and adaptability of the treatment plan. For example, the health data of some patients may fluctuate during the treatment process. Through the optimization steps, the doctor's orders can be adjusted in time to improve the treatment effect. Through the correction and optimization of the fluctuation amplitude, this method effectively reduces the risks caused by the uncertainty of health data and improves the stability of the doctor's orders. Especially in the face of complex patient health conditions, this uncertainty optimization generation strategy can minimize the deviation of the medical plan and improve the stability and reliability of the treatment plan.

[0019] 6. The doctor's order parameters and fluctuation amplitude are gradually updated by the gradient descent method, and the current value is adjusted according to the partial derivative of the objective function in each iteration. In this way, the fluctuation amplitude of the doctor's order parameters and health data can be continuously optimized and gradually tend to the optimal state. Traditional treatment plans are usually determined once and lack dynamic adjustment for changes in patient health. The gradient descent method allows the doctor's order to be adjusted in real time according to the patient's health data, ensuring that the treatment plan after each iteration can more accurately respond to the fluctuation of the patient's health. The gradient descent method calculates the gradient of the objective function to ensure that each iteration is optimized in the direction of the target minimum value, reducing the possibility of falling into the local optimal solution. For complex medical decision-making problems, local optimality may lead to unsatisfactory treatment effects. The application of the gradient descent method can minimize the objective function as a whole, so that the doctor's order parameters and fluctuation amplitude can find the global optimal solution, thereby improving the accuracy of the treatment effect. By setting the learning rate, the size of each iteration step can be controlled to prevent the update process from being too drastic or slow. An appropriate learning rate helps to speed up the optimization process and avoid unsatisfactory convergence speed or oscillation problems caused by too large or too small update step sizes. In actual medical applications, the adjustment of the learning rate can be flexibly controlled according to the specific situation of the patient, thereby improving the efficiency and stability of the optimization of medical orders. Through the iterative optimization of the objective function, the gradient descent method ensures that the parameters and fluctuations of the medical orders are constantly adjusted to accurately reflect the changes in the patient's health needs. For patients with multiple diseases or large fluctuations in health status, this optimization method can better adapt to the changes in individual health status, provide personalized treatment plans, and significantly improve the treatment effect. The application of the gradient descent method ensures that the fluctuation range of health data can be corrected after each iteration, so that the medical order can adapt to the patient's health changes in real time. For example, some patients may experience large health fluctuations. The gradient descent method ensures that the medical order can always be adjusted according to the patient's actual health status at different stages by updating the fluctuation range. This adaptability greatly improves the flexibility of the treatment process and avoids the problem of mismatch between the medical order and the patient's health fluctuations. Through the optimization process of the gradient descent method, the medical order can be personalized according to each patient's unique health data, fluctuation range, and optimization goals. This not only avoids a one-size-fits-all treatment plan, but also ensures that the medical order plan is highly consistent with the patient's health, improving the treatment effect and patient compliance.

[0020] 7. By setting the tolerance error value threshold, the iteration can be stopped when the change of the objective function after each iteration is less than the predetermined tolerance value. This effectively avoids unnecessary calculations caused by excessive iterations and reduces the waste of computing resources. When the change of the objective function tends to be stable, the optimization process is terminated, ensuring the efficiency and economy of the optimization. By setting the maximum number of iterations, unlimited iterations caused by extremely slow changes in the objective function or ineffective convergence are prevented. In practice, the gradient descent method may fail to converge the objective function in a short time due to improper initial value selection or learning rate setting problems. The maximum number of iterations ensures that even if the objective function converges slowly, the iteration can be forced to stop within a reasonable number of times, avoiding the situation where the algorithm cannot be terminated. The iteration stop condition can avoid continuing the optimization when the objective function has not changed significantly, thereby avoiding the instability or overfitting of the model caused by too many iterations. Through reasonable stopping criteria, the optimization process can be maintained within a stable range, ensuring that the resulting medical prescription plan is both accurate and universal, avoiding the occurrence of overfitting problems. In the medical system, a long optimization process may delay the implementation of the treatment plan and affect the patient's treatment experience. By setting reasonable stop conditions, the doctor's order optimization can be completed in a shorter time and the treatment plan can be given as soon as possible, thereby improving the patient's treatment experience and satisfaction.

[0021] 8. By correcting the fluctuation amplitude according to the risk propagation matrix and sensitivity distribution, the fluctuation amplitude can be dynamically adjusted to match the current risk distribution and sensitivity status. The fluctuation amplitude of health indicator data usually varies with the patient's health status. A simple fixed fluctuation amplitude may cause the optimization plan to be inconsistent with the actual situation. By introducing the correction coefficient, this method can reflect the actual situation of health indicator fluctuations in real time, making the optimization process more adaptive and targeted. Correcting the fluctuation amplitude can ensure that after each iteration, the optimization model can better adapt to the mutual influence between different health data. In particular, when dealing with the risk of mutual coupling between multiple health indicators, the correction of the fluctuation amplitude helps to more accurately measure the contribution of each health indicator to the overall risk, thereby improving the accuracy of medical advice. This is of great significance for the generation of personalized treatment plans for patients, and can ensure that the treatment plan is more scientific and refined. By correcting the fluctuation amplitude after each iteration, the complex risk coupling relationship between health indicators can be effectively dealt with. When the risk association between health indicators changes, the corrected fluctuation amplitude can better capture these changes, thereby optimizing health risk prediction and avoiding misjudgments and medical advice errors caused by inaccurate risk coupling relationships. Dynamic adjustment of the fluctuation amplitude ensures that the optimization process remains flexible and stable when processing health data. Especially when the health data fluctuates greatly or there is a certain degree of uncertainty, correcting the fluctuation amplitude can balance the influence of various health indicators, avoid the deviation of the entire optimization process due to excessive or too small fluctuations in a certain health indicator, and thus improve the stability of the treatment effect. During the optimization process, the fluctuation amplitude correction helps guide the gradient descent method to quickly find the optimal solution. Through timely fluctuation amplitude adjustment, the convergence of the optimization target can be accelerated, avoiding the slow convergence or false convergence problems caused by improper fluctuation amplitude setting. Therefore, this method can improve the optimization efficiency and reduce the number of iterations, making the generation of treatment plans more efficient. There may be significant differences in the health data and fluctuation amplitude of each patient. By correcting the fluctuation amplitude, the weight of each data in the optimization process can be accurately adjusted, making the doctor's advice more personalized. For different patients, the dynamic adjustment of the fluctuation amplitude ensures the individual characteristics of the doctor's advice, which can better adapt to the changes in the health status of different patients and improve the treatment effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0024] Example, see Figure 1 , a method for generating medical advice information based on patient identification, comprising: S1. Establish a multidimensional uncertainty model for health data: Set the health indicator dataset, denoted as ; Collect multiple indicators from the patient's authorized health records; The multiple indicators include physiological data and medical history data. The collected data are added to the health indicator data set to obtain , where n is the total number of health data dimensions, It is the first Items of health indicator data; Perform maximum-minimum normalization on each health indicator data: ; in, and They are respectively The maximum and minimum values ​​of the health indicator data; is the value after maximum-minimum normalization; The maximum-minimum normalized value of each health indicator data is updated to the data at the corresponding position in the health indicator data set; Calculate the fluctuation range of each data in the health indicator data set; Calculate the health index data set Item and The correlation coefficient between the items of data; Get the dataset The mean value of ; Set a weight for each health data in the health indicator dataset, calculated as follows: ; in, is an adjustment factor used to amplify the weight difference; For the Weighting coefficients of health indicator data; S2, uncertainty propagation analysis; S3. Optimization and generation of medical order uncertainty.

[0025] By establishing a multidimensional uncertainty model for health data, this method solves the problem of how to integrate multidimensional health data and eliminate the scale differences between different data. Through maximum-minimum normalization processing, the dimensional differences between different health indicator data are eliminated, so that the data are within the same standard range, which is convenient for subsequent analysis and comparison. This step avoids the analytical deviation that may be caused by different data units and ranges. By calculating the fluctuation range and correlation of health data, it is possible to identify which health indicators have large fluctuations, and clarify the mutual influence between different health data. This provides a valuable reference for subsequent uncertainty propagation analysis. By assigning weights to each health data and introducing adjustment factors, this method can dynamically adjust the influence of health indicators according to the health status of different patients, making the data model more personalized and targeted in practical applications, thereby improving the accuracy and effectiveness of medical order generation.

[0026] The calculation of the fluctuation range of each data in the health indicator data set specifically includes: Get the health indicator data set The variance of the maximum-minimum normalized health indicator data is recorded as ; Get the health indicator data set The mean of the maximum-minimum normalized health indicator data is recorded as ; The fluctuation range is estimated for each data in the health indicator data set. The fluctuation range calculation formula is: ; in, , For the The fluctuation range of the health indicator data; Set the fluctuation range data set, denoted as ; All calculated fluctuation ranges are added to the fluctuation range dataset in sequence.

[0027] By obtaining the variance and mean of each health indicator data after maximum-minimum normalization, the volatility of each data can be accurately quantified. This step effectively solves the problem of how to convert volatility into quantifiable numerical values, so that the volatility of health data can be clearly presented and facilitated for further analysis. By calculating the fluctuation range, this method can identify and quantify the fluctuation range of each health data, so that when facing multi-dimensional and different sources of health data, the uncertainty of the data can be accurately assessed. This not only enhances the understanding of the patient's health status, but also provides a reliable basis for subsequent health risk assessment, avoiding misjudgment caused by ignoring data fluctuations. Setting the fluctuation range data set and adding the fluctuation range of each health data in turn lays the foundation for subsequent uncertainty propagation analysis. This data set provides a comprehensive fluctuation range reference framework for the system, so that when analyzing the impact of health data on patient health, a comprehensive assessment can be made from multiple dimensions, improving the accuracy and comprehensiveness of the analysis. By quantifying the fluctuation range of health data, the changing trend of the patient's health status can be predicted more accurately. The introduction of this volatility data makes the formulation of medical advice and treatment plans more in line with the actual conditions of individual patients, reduces the possibility of misdiagnosis and overtreatment, and thus improves the treatment effect and patient satisfaction.

[0028] The health indicator data set is calculated Item and The correlation coefficient between the data items includes: Get the health indicator data set The variance of the maximum-minimum normalized health indicator data is recorded as ; Calculate the health index data set Item and The correlation coefficient between the items of data is recorded as , the calculation formula is: .

[0029] By calculating the correlation coefficient, this method can effectively quantify the relationship between various health data and solve the problem of how to identify potential associations between multiple health indicators. Health data usually has multidimensional characteristics. Analyzing each indicator separately may not fully reflect the true health status. By calculating the correlation coefficient, it can reveal which health indicators have strong relationships and which relationships are weak, thereby providing strong support for data analysis. By calculating the correlation between various health indicators, the dependence between different health parameters can be deeply analyzed, avoiding the problem of relying solely on individual data points for health assessment. This method enables multidimensional data to complement each other during the analysis process, thereby enhancing the comprehensiveness of the assessment. For example, abnormal fluctuations in a health indicator may be caused by changes in other related indicators. Through correlation analysis, potential health problems can be discovered early. Calculating the correlation coefficient between health indicators helps to more accurately predict the health risks of patients. In the process of patient health assessment, correlation analysis can reveal the causal relationship and influence between different health data, especially in complex health conditions, which can help doctors discover potential health problems and intervene in time, reducing the possibility of misdiagnosis and missed diagnosis. By calculating the correlation coefficient between health data, a more accurate basis can be provided for the formulation of medical advice. By identifying highly relevant health indicators, it is possible to optimize the formulation of medical advice and ensure that it is more in line with the patient's actual health status. This not only improves the effectiveness of personalized treatment, but also reduces unnecessary interference factors during the treatment process.

[0030] The uncertainty propagation analysis specifically includes: Set the function of overall health risk as , specifically: ; in, For the Risk weights of health indicator data; For the The fluctuation range of the health indicator data; For the The maximum-minimum normalized value of the health indicator data; The influence of each health indicator data item on the overall risk is calculated by partial derivatives, and the sensitivity measurement formula is: ; Set the sensitivity distribution set, denoted as ; Each calculated Add to the sensitivity distribution set, we get ; Through correlation and sensitivity, the risk propagation matrix is ​​constructed and recorded as , used to describe the mutual influence between variables: ; in, The risk propagation matrix Middle Line Column data; is the sensitivity distribution set individual data; This is the health indicator dataset. Item and The correlation coefficient between the items of data; Risk Communication Matrix The risk coupling relationship between different health indicators is quantified, and the total propagation effect of each variable in the health indicator dataset is calculated: ; For all Sort and get the maximum dissemination effect.

[0031] By constructing a risk propagation matrix, this step can effectively quantify the risk coupling relationship between different health indicators, and calculate the degree of influence of each health indicator on the overall health risk through sensitivity analysis. Traditional health risk analysis often only focuses on a single indicator and ignores the interaction between various indicators. The implementation of this method can effectively solve this problem and make health risk assessment more comprehensive and accurate. By calculating the sensitivity of health indicators, it is possible to identify which health indicators contribute more to the overall health risk, helping doctors and medical personnel to focus on those key health factors, so as to develop more personalized and accurate treatment plans. For example, some health indicators contribute more to risk propagation and may be the key to potential health problems. Early intervention in these key indicators can significantly reduce the health risks of patients. By quantifying the risk propagation effects between different health indicators, the risk propagation matrix provides a comprehensive and dynamic health risk map, allowing decision makers to evaluate the impact of changes in various health indicators on overall health status from a global perspective. This process can effectively guide medical decisions, such as adjusting treatment strategies, formulating health intervention measures, etc., to improve the accuracy and effectiveness of health management. The risk propagation matrix can provide a clear propagation path for uncertainties in health data, ensuring that the health early warning system can identify the source of risk in advance. The maximum propagation effect obtained through sorting can provide real-time monitoring priorities for the intelligent health monitoring system, giving priority to those health indicators with greater propagation effects, thereby improving the response speed of disease prevention and risk management.

[0032] The uncertainty optimization generation of medical advice specifically includes: Get the target health value of each data in the health indicator dataset, denoted as , This is the health indicator dataset. The target health value of the item data; Get the set of doctor's order parameters, denoted as u, and add the first Item data is recorded as ; Get the target dose corresponding to each data in the doctor's order parameter set, and The target dose of the data item is recorded as ; Get the expected fluctuation range of each health indicator data in the health indicator data set, and The expected volatility is recorded as ; Set the objective function of medical order optimization as , with the primary goal of minimizing and optimizing patient health: ; in, This is the health indicator dataset. The weight coefficient of the item data in the objective function; The first The weight coefficient of the item data in the objective function; This is the health indicator dataset. The weight coefficient of the fluctuation range of the item data in the objective function; is the total number of data in the medical order parameter set; Updating medical order parameters based on gradient descent method and volatility ; Set the iteration stop condition; After each iteration, the fluctuation amplitude Make revisions to ensure they are appropriate to the current risk profile and sensitivities; Get the final optimized Item medical order parameter, denoted as ; Get the final optimized health indicator data set The fluctuation range of the data item is recorded as ; Set the optimized medical order set, denoted as : .

[0033] By obtaining the target health value of each health indicator data and optimizing the doctor's order parameters, this step can optimize the doctor's order plan according to the patient's specific situation. Traditional doctor's order generation methods are usually based on experience and general rules, and cannot accurately optimize the health status of each patient. Through this step, the doctor's order can be quantitatively analyzed based on individual health indicator data, fluctuation range and other factors, thereby improving the personalization and treatment effect of the medical plan. By adding the weight coefficient of the fluctuation range to the objective function and optimizing it, the adaptability of the doctor's order parameters and the fluctuation range of health data is ensured, thereby effectively reducing health risks. In practical applications, the patient's health status may fluctuate. By correcting the fluctuation range, it is ensured that the results after each iteration are more consistent with the patient's current health status, avoiding medical decision-making errors caused by not adapting to risk changes. By using the gradient descent method to update parameters and setting the iteration stop condition, the optimized doctor's order can be continuously adjusted until an optimal solution is reached. The gradient descent method can gradually optimize the parameters and fluctuation range of the doctor's order, and accurately adjust the dosage of each doctor's order to match the patient's health status. In this way, it can not only effectively improve the patient's treatment effect, but also dynamically adjust the treatment plan according to the changes in health data to ensure that the treatment is continuous and effective. By establishing an optimization objective function and correcting the fluctuation amplitude after each iteration, the final optimization result is ensured to adapt to the changes and sensitivity of the patient's health data, making medical decisions more accurate. Traditional medical decisions often rely on the doctor's experience and conventional treatment plans, while this method achieves accurate assessment of the patient's health risks in a data-driven way, thereby providing a more scientific basis for decision-making. By continuously iterating, optimizing and correcting the fluctuation amplitude, the doctor's orders can adapt to the risk distribution and health status changes of different patients. With the dynamic changes of health data, this method can adjust the doctor's orders in real time to ensure the flexibility and adaptability of the treatment plan. For example, the health data of some patients may fluctuate during the treatment process. Through the optimization steps, the doctor's orders can be adjusted in time to improve the treatment effect. Through the correction and optimization of the fluctuation amplitude, this method effectively reduces the risks caused by the uncertainty of health data and improves the stability of the doctor's orders. Especially in the face of complex patient health conditions, this uncertainty optimization generation strategy can minimize the deviation of the medical plan and improve the stability and reliability of the treatment plan.

[0034] The method of updating the medical order parameters based on the gradient descent method and volatility , including: Updating medical order parameters based on gradient descent method and volatility, , the goal is to minimize the optimization objective function : ; ; , ; in, The first The value of the item data at the tth iteration; This is the health indicator dataset. The fluctuation range of the item data at the tth iteration; is the learning rate, which controls the step size of each iteration.

[0035] The doctor's order parameters and fluctuation amplitude are gradually updated by the gradient descent method, and the current value is adjusted according to the partial derivative of the objective function in each iteration. In this way, the fluctuation amplitude of the doctor's order parameters and health data can be continuously optimized and gradually tend to the optimal state. Traditional treatment plans are usually determined once and lack dynamic adjustment for changes in patient health. The gradient descent method allows the doctor's order to be adjusted in real time according to the patient's health data, ensuring that the treatment plan after each iteration can more accurately respond to the fluctuation of the patient's health. The gradient descent method calculates the gradient of the objective function to ensure that each iteration is optimized in the direction of the target minimum value, reducing the possibility of falling into the local optimal solution. For complex medical decision-making problems, local optimality may lead to unsatisfactory treatment effects. The application of the gradient descent method can minimize the objective function as a whole, so that the doctor's order parameters and fluctuation amplitude can find the global optimal solution, thereby improving the accuracy of the treatment effect. By setting the learning rate, the size of each iteration step can be controlled to prevent the update process from being too drastic or slow. An appropriate learning rate helps to speed up the optimization process and avoid unsatisfactory convergence speed or oscillation problems caused by too large or too small update step sizes. In actual medical applications, the adjustment of the learning rate can be flexibly controlled according to the specific situation of the patient, thereby improving the efficiency and stability of the optimization of medical orders. Through the iterative optimization of the objective function, the gradient descent method ensures that the parameters and fluctuations of the medical orders are constantly adjusted to accurately reflect the changes in the patient's health needs. For patients with multiple diseases or large fluctuations in health status, this optimization method can better adapt to the changes in individual health status, provide personalized treatment plans, and significantly improve the treatment effect. The application of the gradient descent method ensures that the fluctuation range of health data can be corrected after each iteration, so that the medical order can adapt to the patient's health changes in real time. For example, some patients may experience large health fluctuations. The gradient descent method ensures that the medical order can always be adjusted according to the patient's actual health status at different stages by updating the fluctuation range. This adaptability greatly improves the flexibility of the treatment process and avoids the problem of mismatch between the medical order and the patient's health fluctuations. Through the optimization process of the gradient descent method, the medical order can be personalized according to each patient's unique health data, fluctuation range, and optimization goals. This not only avoids a one-size-fits-all treatment plan, but also ensures that the medical order plan is highly consistent with the patient's health, improving the treatment effect and patient compliance.

[0036] The setting of the iteration stop condition specifically includes: Set the tolerance error value threshold, denoted as ; Set the maximum number of iterations, denoted as ; Get the value of the objective function at the tth iteration, denoted as ; Get the value of the objective function at the t+1th iteration, denoted as ; When the objective function When the change is less than the tolerance error threshold, stop the iteration: ; When the number of iterations is greater than the maximum number of iterations, the iteration is forced to stop: .

[0037] By setting the tolerance error value threshold, the iteration can be stopped when the change of the objective function after each iteration is less than the predetermined tolerance value. This effectively avoids unnecessary calculations caused by excessive iterations and reduces the waste of computing resources. When the change of the objective function tends to be stable, the optimization process is terminated, ensuring the efficiency and economy of the optimization. By setting the maximum number of iterations, unlimited iterations caused by extremely slow changes in the objective function or ineffective convergence are prevented. In practice, the gradient descent method may fail to converge the objective function in a short time due to improper initial value selection or learning rate setting problems. The maximum number of iterations ensures that even if the objective function converges slowly, the iteration can be forced to stop within a reasonable number of times, avoiding the situation where the algorithm cannot be terminated. The iteration stop condition can avoid continuing the optimization when the objective function has not changed significantly, thereby avoiding the instability or overfitting of the model caused by too many iterations. Through reasonable stopping criteria, the optimization process can be maintained within a stable range, ensuring that the resulting medical advice plan is both accurate and universal, and avoiding the occurrence of overfitting problems. In the medical system, a long optimization process may delay the implementation time of the treatment plan and affect the patient's treatment experience. By setting reasonable stopping conditions, medical order optimization can be completed in a shorter time and treatment plans can be given as soon as possible, thereby improving patients' treatment experience and satisfaction.

[0038] After each iteration, the fluctuation amplitude Make corrections, including: ; in, This is the health indicator dataset. The fluctuation range of the item data at the tth iteration; This is the health indicator dataset. Correction coefficient of item data; The risk propagation matrix Middle Line Column data; The sensitivity distribution set Item data.

[0039] By correcting the fluctuation amplitude according to the risk propagation matrix and sensitivity distribution, the fluctuation amplitude can be dynamically adjusted to match the current risk distribution and sensitivity state. The fluctuation amplitude of health indicator data usually varies with the changes in the patient's health status. A simple fixed fluctuation amplitude may cause the optimization plan to be inconsistent with the actual situation. By introducing the correction coefficient, this method can reflect the actual situation of health indicator fluctuations in real time, making the optimization process more adaptive and targeted. Correcting the fluctuation amplitude can ensure that after each iteration, the optimization model can better adapt to the mutual influence between different health data. In particular, when dealing with the risk of mutual coupling between multiple health indicators, the correction of the fluctuation amplitude helps to more accurately measure the contribution of each health indicator to the overall risk, thereby improving the accuracy of medical advice. This is of great significance for the generation of personalized treatment plans for patients, and can ensure that the treatment plan is more scientific and refined. By correcting the fluctuation amplitude after each iteration, the complex risk coupling relationship between health indicators can be effectively dealt with. When the risk association between health indicators changes, the corrected fluctuation amplitude can better capture these changes, thereby optimizing health risk prediction and avoiding misjudgments and medical advice errors caused by inaccurate risk coupling relationships. Dynamic adjustment of the fluctuation amplitude ensures that the optimization process remains flexible and stable when processing health data. Especially when the health data fluctuates greatly or there is a certain degree of uncertainty, correcting the fluctuation amplitude can balance the influence of various health indicators, avoid the deviation of the entire optimization process due to excessive or too small fluctuations in a certain health indicator, and thus improve the stability of the treatment effect. During the optimization process, the fluctuation amplitude correction helps guide the gradient descent method to quickly find the optimal solution. Through timely fluctuation amplitude adjustment, the convergence of the optimization target can be accelerated, avoiding the slow convergence or false convergence problems caused by improper fluctuation amplitude setting. Therefore, this method can improve the optimization efficiency and reduce the number of iterations, making the generation of treatment plans more efficient. There may be significant differences in the health data and fluctuation amplitude of each patient. By correcting the fluctuation amplitude, the weight of each data in the optimization process can be accurately adjusted, making the doctor's advice more personalized. For different patients, the dynamic adjustment of the fluctuation amplitude ensures the individual characteristics of the doctor's advice, which can better adapt to the changes in the health status of different patients and improve the treatment effect.

[0040] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0041] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for generating medical advice information based on patient identification, characterized in that: include: S1. Establish a multidimensional uncertainty model for health data: Set the health indicator dataset, denoted as ; Collect multiple indicators from the patient's authorized health records; The multiple indicators include physiological data and medical history data. The collected data are added to the health indicator data set to obtain , where n is the total number of health data dimensions, It is the first Items of health indicator data; Perform maximum-minimum normalization on each health indicator data: ； in, and They are respectively The maximum and minimum values ​​of the health indicator data; is the value after maximum-minimum normalization; The maximum-minimum normalized value of each health indicator data is updated to the data at the corresponding position in the health indicator data set; Calculate the fluctuation range of each data in the health indicator data set; Calculate the health index data set Item and The correlation coefficient between the items of data; Get the dataset The mean value of ; Set a weight for each health data in the health indicator dataset, calculated as follows: ； in, is the regulating factor; For the Weighting coefficients of health indicator data; S2, uncertainty propagation analysis; S3. Optimization and generation of medical order uncertainty.

2. A method for generating medical advice information based on patient identification according to claim 1, characterized in that: The calculation of the fluctuation range of each data in the health indicator data set specifically includes: Get the health indicator data set The variance of the maximum-minimum normalized health indicator data is recorded as ; Get the health indicator data set The mean of the maximum-minimum normalized health indicator data is recorded as ; The fluctuation range is estimated for each data in the health indicator data set. The fluctuation range calculation formula is: ； in, , For the The fluctuation range of the health indicator data; Set the fluctuation range data set, denoted as ; All calculated fluctuation ranges are added to the fluctuation range dataset in sequence.

3. A method for generating medical advice information based on patient identification according to claim 2, characterized in that: The health indicator data set is calculated Item and The correlation coefficient between the data items includes: Get the health indicator data set The variance of the maximum-minimum normalized health indicator data is recorded as ; Calculate the health index data set Item and The correlation coefficient between the items of data is recorded as , the calculation formula is: 。 4. A method for generating medical advice information based on patient identification according to claim 3, characterized in that: The uncertainty propagation analysis specifically includes: Set the function of overall health risk as , specifically: ； in, For the Risk weights of health indicator data; For the The fluctuation range of the health indicator data; For the The maximum-minimum normalized value of the health indicator data; The influence of each health indicator data item on the overall risk is calculated by partial derivatives, and the sensitivity measurement formula is: ； Set the sensitivity distribution set, denoted as ; Each calculated Add to the sensitivity distribution set, we get ; Through correlation and sensitivity, the risk propagation matrix is ​​constructed and recorded as , used to describe the mutual influence between variables: ； in, The risk propagation matrix Middle Line Column data; is the sensitivity distribution set individual data; This is the health indicator dataset. Item and The correlation coefficient between the items of data; Calculate the total transmission effect for each variable in the health indicators dataset: ； For all Sort and get the maximum dissemination effect.

5. A method for generating medical advice information based on patient identification according to claim 4, characterized in that: The uncertainty optimization generation of medical advice specifically includes: Get the target health value of each data in the health indicator dataset, denoted as , This is the health indicator dataset. The target health value of the item data; Get the set of doctor's order parameters, denoted as u, and add the first Item data is recorded as ; Get the target dose corresponding to each data in the doctor's order parameter set, and The target dose of the data item is recorded as ; Get the expected fluctuation range of each health indicator data in the health indicator data set, and The expected volatility is recorded as ; Set the objective function of medical order optimization as : ； in, This is the health indicator dataset. The weight coefficient of the item data in the objective function; The first The weight coefficient of the item data in the objective function; This is the health indicator dataset. The weight coefficient of the fluctuation range of the item data in the objective function; is the total number of data in the medical order parameter set; Update medical order parameters based on gradient descent method and volatility ; Set the iteration stop condition; After each iteration, the fluctuation amplitude make corrections; Get the final optimized Item medical order parameter, denoted as ; Get the final optimized health indicator data set The fluctuation range of the data item is recorded as ; Set the optimized medical order set, denoted as : 。 6. A method for generating medical advice information based on patient identification according to claim 5, characterized in that: The method of updating the medical order parameters based on the gradient descent method and volatility , specifically including: Update medical order parameters based on gradient descent method and volatility : ； ； ， ； in, The first The value of the item data at the tth iteration; This is the health indicator dataset. The fluctuation range of the item data at the tth iteration; is the learning rate.

7. A method for generating medical advice information based on patient identification according to claim 6, characterized in that: The setting of the iteration stop condition specifically includes: Set the tolerance error value threshold, denoted as ; Set the maximum number of iterations, denoted as ; Get the value of the objective function at the tth iteration, denoted as ; Get the value of the objective function at the t+1th iteration, denoted as ; When the objective function When the change is less than the tolerance error threshold, stop the iteration: ； When the number of iterations is greater than the maximum number of iterations, the iteration is forced to stop: 。 8. A method for generating medical advice information based on patient identification according to claim 7, characterized in that: After each iteration, the fluctuation amplitude Make corrections, including: ； in, This is the health indicator dataset. The fluctuation range of the item data at the tth iteration; This is the health indicator dataset. Correction coefficient of item data; The risk propagation matrix Middle Line Column data; The sensitivity distribution set Item data.

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