An intelligent home pain management system based on Internet hospital

Abstract

The present invention relates to the technical field of data management, and in particular to an intelligent home pain management system based on an Internet hospital; the system comprises: a wearable monitoring unit for acquiring various physiological data of a patient; an Internet hospital unit for analyzing pain patterns according to the various physiological data of the patient; the present invention tracks the physiological state of a patient at home in real time through the wearable monitoring unit, and transmits various data of the patient's physiological state to the Internet hospital unit, and establishes an individual database of the patient in the Internet hospital, so as to facilitate medical staff to use a remote management unit to view the patient's treatment stage and drug use, and effectively solves the problem that the pain patient still needs to orally tell the doctor about various conditions at the time of the onset of the disease during a follow-up visit, which is conducive to further improving the patient's medical treatment efficiency, and at the same time has a preventive effect, and can make a prediction based on the changes in the characteristics of the patient's various physiological data, when the changing trend of the data characteristics approaches the critical value of the pain onset, to remind the patient to take preventive measures.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and in particular to an intelligent home pain management system based on an Internet hospital. Background Art

[0002] Internet hospital is a new medical model that combines Internet technology and traditional medical services. Through Internet hospital, patients can consult, consult, follow up and other medical services with doctors online without having to go to the hospital in person.

[0003] The patent application number is CN202311790170.X, which states in the specification that "the present invention relates to the field of data management technology, and discloses an Internet hospital data management system, including: medical record card, registration machine, data center, processing unit and app. Several registration machines are set up in the hospital, and a data center is established inside the registration machine. A user personal database and a doctor database are established inside the data center. The patient receives the medical record card and swipes the card at the registration machine. After swiping the card, the patient registers personal information in the patient's personal database. Then the registration machine pops up a page for the patient to fill in the patient's physical condition according to the requirements and upload it to the data center. The processing unit processes the patient's visit according to the filled-in patient information. Departments are classified and queue numbers are assigned according to the existing patients in the department. The system has the advantages of planning the patient treatment process in the hospital, making the scheduling more reasonable while improving the treatment efficiency of patients in the hospital, reducing disputes and other situations that occur when patients queue up for treatment, protecting the personal privacy of patients, and facilitating user use. Although the above technology has the advantage of improving the efficiency of treatment, in the face of complex conditions, such as cancer pain, the pain intensity, duration and frequency of pain at home are not systematically collected, resulting in patients still needing to orally describe various situations to the doctor during follow-up visits, which is not conducive to further improving the treatment efficiency of patients with complex conditions.

[0004] In summary, the development of an intelligent home pain management system based on an Internet hospital is still a key issue that needs to be urgently addressed in the field of data management technology. Summary of the invention

[0005] In order to solve the problem in the prior art that when facing complex medical conditions, such as cancer pain, the index data such as pain intensity, duration and frequency when the disease occurs at home are not systematically collected, resulting in the patient still needing to orally tell the doctor a variety of situations during the follow-up visit, which is not conducive to further improving the medical treatment efficiency of patients with complex medical conditions, the present invention provides an intelligent home pain management system based on an Internet hospital.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] The present invention provides an intelligent home pain management system based on an Internet hospital, comprising: a wearable monitoring unit for acquiring various physiological data of a patient;

[0008] The Internet hospital unit analyzes pain patterns based on the patient’s various physiological data;

[0009] Customized management unit to manage and adjust medications based on the patient's pain pattern;

[0010] Remote management unit, used for patient medication management and understanding of various physiological data of the patient's body.

[0011] Further, the wearable monitoring unit comprises:

[0012] The smart bracelet module is used to be conveniently worn on the patient's body and to come into direct contact with the patient's skin. It is also used to send and receive information and generate abnormal alarms through the Internet.

[0013] The sensor module is used to monitor the patient's heart rate, body temperature, blood pressure, blood oxygen, electromyography, movement and posture. It is integrated into the wearable monitoring unit, so that the patient's multi-dimensional physiological data can be collected to provide real-time data for subsequent management plans;

[0014] The Internet Hospital unit includes:

[0015] The pain assessment system forms subjective and objective pain assessments when pain occurs;

[0016] The data center module receives the multi-dimensional physiological data of the patient, and stores and files the data after pre-processing;

[0017] The data analysis module analyzes the patient's multi-dimensional physiological data to understand the impact of pain on various physical indicators of the patient, and then identify the pattern of pain attacks.

[0018] Furthermore, the custom management unit includes:

[0019] The personalized management module generates a personalized management plan based on the patient's subjective pain assessment, objective pain assessment, and the patient's physiological condition when in pain;

[0020] The assistance module helps patients take preventive measures in advance based on the patient's pain attack pattern, subjective pain assessment and objective pain assessment;

[0021] The Remote Management Unit includes:

[0022] The medication management system develops a personalized medication plan based on the doctor's prescription and records the patient's medication status in real time;

[0023] The remote intervention module is used by medical staff to select appropriate medications for replacement based on the patient's real-time physiological data.

[0024] Furthermore, the operation process of the wearable monitoring unit includes:

[0025] The smart bracelet module is worn on the patient's wrist. The surface of the smart bracelet module is in direct contact with the patient's skin. The internal integrated sensor can monitor the patient's physiological data in real time. The built-in communication module of the bracelet module exchanges data with the Internet hospital unit through the Internet. The smart bracelet module has a built-in alarm function. Once abnormal physiological parameters are detected, an alarm will be sent to the guardian and medical staff through the Internet, and the patient will be reminded by vibration and sound.

[0026] The sensor module is used to monitor and record the patient's heart rate changes, body surface temperature, blood pressure fluctuations, blood oxygen saturation, and muscle activity electrical signals in real time. It is also used to record the patient's steps, activity intensity, and posture changes to help identify abnormal physiological data and dangerous situations of falling.

[0027] Furthermore, the operation process of the smart bracelet module includes:

[0028] The operation process of the alarm function is: the physiological data obtained by monitoring is standardized, and the standardization formula is: ,in is a physiological parameter actually measured. is the reasonable minimum value of this physiological parameter, is the reasonable maximum value of this physiological parameter, It is the normalized physiological parameter value, which indicates the relative position of the physiological parameter between 0 and 1. The physiological data of different dimensions are unified to the same scale for comparison, and then the risk score of abnormal data is calculated. The comprehensive anomaly detection formula is: ,in It is the result of comprehensive weighted calculation of multiple physiological parameters and is used to assess overall health status. is the weighted sum of all normalized physiological parameters, is the number of physiological parameters monitored, is the weight of each physiological parameter, which is used to reflect the importance of the parameter. yes The total risk threshold is set after the normalized value of each physiological parameter , ,in When the risk score is greater than the threshold, an alarm is triggered to alert guardians, medical staff and patients.

[0029] Furthermore, the Internet hospital unit operation process includes:

[0030] Pain assessment system: After feeling pain, patients can enter numbers (0-10 for pain rating) into the wearable monitoring unit by handwriting or voice to express the intensity of the pain, forming a subjective pain assessment. At the same time, changes in physiological data such as heart rate, body temperature, blood pressure, blood oxygen, electromyography, movement and posture during the onset of pain will form an objective pain assessment.

[0031] The data center module is connected to the wearable monitoring unit via the Internet, and is used to collect the patient's real-time physiological data and physiological data during pain attacks, and process the data to remove noise or incomplete data. The data is then classified according to timestamps and categories and stored in the patient's individual database, which is shared with the external hospital system;

[0032] The data analysis module accesses the individual database of the patient, analyzes the correlation between the patient's various physiological indicators and pain scores, analyzes the physiological characteristics of different pain states when pain occurs, summarizes the frequency of pain attacks including but not limited to specific time periods and specific physiological states, and uses the Pearson correlation coefficient to express the correlation as follows: ,in is a variable and The correlation coefficient of is the sample size, is a variable and The expression formula of physiological characteristics using K-means clustering analysis is: Formula, where is the total clustering error, is the number of clusters, It is The sample set of the class, is a sample point belonging to a certain cluster, is the center point of the first class, is a regularization parameter used to control the distance between cluster centers. is the total number of samples, is the total number of classes. Fourier transform is used to convert time domain signals into frequency domain signals to help identify periodic features. The expression formula is: ,in is the frequency domain signal, is the value of the time domain signal at time, is the total number of time points, is the frequency, is an imaginary unit, is the index of the frequency component, is the number of frequency components used for identification.

[0033] Furthermore, the custom management unit operation process includes:

[0034] The personalized management module analyzes the characteristics of each patient's data, evaluates the importance of each feature to the system performance, and then compares the system accuracy before and after removing a feature to identify the physiological indicators that have the greatest impact on the prediction of pain onset. The expression formula for key feature identification is: ,in is the feature importance score, is the baseline accuracy of the model when all features are used, is the model accuracy after removing a feature, is the total number of features, is the variance of the feature, It is a feature The weight of is a regularization term used to prevent the model from overfitting. is a hyperparameter of the regularization strength, which is introduced into the loss function of the system , evaluate the changes in the model after removing a certain feature, and the expression formula is: ,in is the true label value, Is the use of features and parameters The prediction function of is a regularization term, which can be used to obtain a more stable system accuracy through cross-validation. The expression formula is: ,in is the average model accuracy after removing a feature, It is in The accuracy of the model in the fold cross validation, by evaluating all features Sort and select the feature set with the greatest impact , the expression formula is: ,in is a set threshold that determines which traits are considered most important, generating a personalized management plan based on the traits of importance.

[0035] Furthermore, the custom management unit operation process includes:

[0036] The assistance module analyzes the changes of various physiological indicators of patients over time at the onset of the disease according to the pattern of pain attacks, subjective pain assessment and objective pain assessment, and then predicts the future pain attacks. The expression formula is: ,in is the number of autoregressive terms, is the number of differences, is the number of moving average terms, is the autoregressive coefficient, is the moving average coefficient, is the lag operator, which represents the time delay, It's in time The observed value of is a white noise term, representing random error. Based on the current real-time data, it predicts the future pain attacks and generates a prediction curve , the expression formula is: ,in is the future time step, indicating the prediction of the future The changes in the patient's physiological indicators after the time step are based on the prediction and combined with the patient's subjective pain assessment to adjust the system's prediction results. The expression formula is: ,in is the final pain prediction value, It is an objective prediction based on physiological data. It is a subjective score based on the patient's self-assessment. is a weight coefficient.

[0037] Furthermore, the remote management unit operation process includes:

[0038] The drug management system automatically generates a personalized drug plan for patients based on the prescription information issued by the doctor, and sets the patient's daily medication time and dosage. The expression formula is: ,in is the optimized drug plan cost, which is the objective function to be minimized. is the number of medications the patient is taking, It is The actual dose of the drug, It is recommended by the doctor The target dose of the drug, It is The actual time of taking the drug, It is target time to take the drug, It is The actual frequency of taking the drug, It is The target frequency of taking the drug, are weight coefficients, which are used to control the importance of dosage, time and frequency in the optimization process. The real-time recording of the patient's medication status combined with physiological data is as follows: ,in It's in time medication status, Indicates that the patient took the medicine at this time. Indicated that no medication was taken. ,in It is the difference between the actual time of taking the medicine and the planned time of taking the medicine. is the time when the patient actually takes the medication. It is the planned medication time point, which feeds back the effect of the drug to the doctor. The feedback formula is: ,in It is a comprehensive evaluation value of the drug effect over time. It is various physiological indicators The weight of the drug effect, It's in time Various physiological indicators, including but not limited to heart rate and blood pressure, is the number of physiological indicators, is the error term, representing random noise or error, ,in is the average feedback value of the drug effect, is the length of the observation period, It's time The real-time evaluation value of the drug effect is generated by integrating the physiological data during the drug action period to generate an average feedback value of the drug effect. , doctors can adjust the patient's treatment plan based on this value.

[0039] Furthermore, the remote management unit operation process includes:

[0040] The remote intervention module analyzes the historical and real-time physiological data in the patient's individual database, and combines the effects of drug use to provide medical staff with a reference range and direction for adjusting the drug regimen. The expression formula is: ,in It is the magnitude or direction of the adjustment of the drug regimen, indicating the degree of adjustment of dosage or change of drug. is the weight coefficient of historical physiological data for drug adjustment, It is The weight of each physiological index, It's time The moment Real-time values ​​of physiological indicators, It is The historical average values ​​of physiological indicators, It is The historical standard deviation of each physiological indicator, is the weight coefficient of drug effect feedback on drug adjustment, It is The weight of each drug effect indicator, It's time The moment Drug effect feedback value, It is The historical average of drug effect feedback, It is The standard deviation of the drug effect feedback, is the decision coefficient for drug substitution, It is the decision value for drug replacement or adjustment. If the threshold is reached, the drug will be replaced. After the drug is replaced, the patient's medication plan will be updated in real time.

[0041] Beneficial Effects

[0042] Compared with the known public technology, the technical solution provided by the present invention has the following beneficial effects:

[0043] The present invention tracks the patient's physiological state at home in real time through a wearable monitoring unit, and transmits various data of the patient's physiological state to the Internet hospital unit, establishes an individual database of the patient in the Internet hospital, and facilitates medical staff to use the remote management unit to view the patient's treatment stage and drug use. It effectively solves the problem that pain patients still need to orally tell the doctor various conditions when the disease occurs during follow-up visits, which is conducive to further improving the patient's medical treatment efficiency. At the same time, it has a preventive effect. According to the changes in the patient's various physiological data characteristics, when the changing trend of the data characteristics approaches the critical value of the pain onset, it can make a prediction to remind the patient to take preventive measures. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 This is a system diagram of an intelligent home pain management system based on an Internet hospital according to the present invention. DETAILED DESCRIPTION

[0045] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings 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 should fall within the scope of protection of the present invention.

[0046] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0047] The present invention is further described in detail with reference to the accompanying drawings:

[0048] like Figure 1 As shown, the present invention provides an intelligent home pain management system based on an Internet hospital, comprising: a wearable monitoring unit for acquiring various physiological data of the patient.

[0049] Wearable monitoring unit, including:

[0050] The smart bracelet module is used to be conveniently worn on the patient's body and to come into direct contact with the patient's skin. It is also used to send and receive information and generate abnormal alarms through the Internet.

[0051] The sensor module is used to monitor the patient's heart rate, body temperature, blood pressure, blood oxygen, electromyography, movement and posture. It is integrated into the wearable monitoring unit, so that the patient's multi-dimensional physiological data can be collected and real-time data can be provided for subsequent management plans.

[0052] The operation process of the wearable monitoring unit includes:

[0053] The smart bracelet module is worn on the patient's wrist. The surface of the smart bracelet module is in direct contact with the patient's skin. The internal integrated sensor can monitor the patient's physiological data in real time. The built-in communication module of the bracelet module exchanges data with the Internet hospital unit through the Internet. The smart bracelet module has a built-in alarm function. Once abnormal physiological parameters are detected, an alarm will be sent to the guardian and medical staff through the Internet, and the patient will be reminded by vibration and sound.

[0054] The sensor module is used to monitor and record the patient's heart rate changes, body surface temperature, blood pressure fluctuations, blood oxygen saturation, and muscle activity electrical signals in real time. It is also used to record the patient's steps, activity intensity, and posture changes to help identify abnormal physiological data and dangerous situations of falling.

[0055] The operation process of the smart bracelet module includes:

[0056] The operation process of the alarm function is: the physiological data obtained by monitoring is standardized, and the standardization formula is: ,in is a physiological parameter actually measured. is the reasonable minimum value of this physiological parameter, is the reasonable maximum value of this physiological parameter, It is the normalized physiological parameter value, which indicates the relative position of the physiological parameter between 0 and 1. The physiological data of different dimensions are unified to the same scale for comparison, and then the risk score of abnormal data is calculated. The comprehensive anomaly detection formula is: ,in It is the result of comprehensive weighted calculation of multiple physiological parameters and is used to assess overall health status. is the weighted sum of all normalized physiological parameters, is the number of physiological parameters monitored, is the weight of each physiological parameter, which is used to reflect the importance of the parameter. yes The total risk threshold is set after the normalized value of each physiological parameter , ,in When the risk score is greater than the threshold, an alarm is triggered to alert guardians, medical staff and patients.

[0057] Specifically, the smart bracelet module is worn on the patient's wrist, and the sensor module is used to obtain various data of the patient's body to achieve real-time understanding of the patient's physical condition. The acquired data is then transmitted to the Internet hospital unit via a wireless network. The smart bracelet module also has an alarm function, so when a patient living alone encounters an emergency, he or she can call for help from the outside world.

[0058] The Internet hospital unit analyzes pain patterns based on the patient's various physiological data.

[0059] The Internet Hospital unit includes:

[0060] The pain assessment system forms subjective and objective pain assessments when pain occurs;

[0061] The data center module receives the multi-dimensional physiological data of the patient, and stores and files the data after pre-processing;

[0062] The data analysis module analyzes the patient's multi-dimensional physiological data to understand the impact of pain on various physical indicators of the patient, and then identify the pattern of pain attacks.

[0063] The operation process of the Internet hospital unit includes:

[0064] Pain assessment system: After feeling pain, patients can enter numbers (0-10 for pain rating) into the wearable monitoring unit by handwriting or voice to express the intensity of the pain, forming a subjective pain assessment. At the same time, changes in physiological data such as heart rate, body temperature, blood pressure, blood oxygen, electromyography, movement and posture during the onset of pain will form an objective pain assessment.

[0065] The data center module is connected to the wearable monitoring unit via the Internet, and is used to collect the patient's real-time physiological data and physiological data during pain attacks, and process the data to remove noise or incomplete data. The data is then classified according to timestamps and categories and stored in the patient's individual database, which is shared with the external hospital system;

[0066] The data analysis module accesses the individual database of the patient, analyzes the correlation between the patient's various physiological indicators and pain scores, analyzes the physiological characteristics of different pain states when pain occurs, summarizes the frequency of pain attacks including but not limited to specific time periods and specific physiological states, and uses the Pearson correlation coefficient to express the correlation as follows: ,in is a variable and The correlation coefficient of is the sample size, is a variable and The expression formula of physiological characteristics using K-means clustering analysis is: Formula, where is the total clustering error, is the number of clusters, It is The sample set of the class, is a sample point belonging to a certain cluster, is the center point of the first class, is a regularization parameter used to control the distance between cluster centers. is the total number of samples, is the total number of classes. Fourier transform is used to convert time domain signals into frequency domain signals to help identify periodic features. The expression formula is: ,in is the frequency domain signal, is the value of the time domain signal at time, is the total number of time points, is the frequency, is an imaginary unit, is the index of the frequency component, is the number of frequency components used for identification.

[0067] Specifically, the pain assessment system collects the patient's pain data when they are in pain, and the data center module is used to establish an individual database for the patient's data to protect the privacy of the patient's data. At the same time, it is convenient for doctors to understand the patient's real-time and historical data. The data analysis module analyzes the patient's physical data to understand the changes in different data when pain occurs.

[0068] Customized management unit to manage and adjust medications based on the patient's pain pattern.

[0069] Custom snap-ins include:

[0070] The personalized management module generates a personalized management plan based on the patient's subjective pain assessment, objective pain assessment, and the patient's physiological condition when in pain;

[0071] The assistance module helps patients take preventive measures in advance based on the patient's pain attack patterns, subjective pain assessment and objective pain assessment.

[0072] The custom management unit operation process includes:

[0073] The personalized management module analyzes the characteristics of each patient's data, evaluates the importance of each feature to the system performance, and then compares the system accuracy before and after removing a feature to identify the physiological indicators that have the greatest impact on the prediction of pain onset. The expression formula for key feature identification is: ,in is the feature importance score, is the baseline accuracy of the model when all features are used, is the model accuracy after removing a feature, is the total number of features, is the variance of the feature, It is a feature The weight of is a regularization term used to prevent the model from overfitting. is a hyperparameter of the regularization strength, which is introduced into the loss function of the system , evaluate the changes in the model after removing a certain feature, and the expression formula is: ,in is the true label value, Is the use of features and parameters The prediction function of is a regularization term, which can be used to obtain a more stable system accuracy through cross-validation. The expression formula is: ,in is the average model accuracy after removing a feature, It is in The accuracy of the model in the fold cross validation, by evaluating all features Sort and select the feature set with the greatest impact , the expression formula is: ,in is a set threshold that determines which traits are considered most important, generating a personalized management plan based on the traits of importance.

[0074] The custom management unit operation process includes:

[0075] The assistance module analyzes the changes of various physiological indicators of patients over time at the onset of the disease according to the pattern of pain attacks, subjective pain assessment and objective pain assessment, and then predicts the future pain attacks. The expression formula is: ,in is the number of autoregressive terms, is the number of differences, is the number of moving average terms, is the autoregressive coefficient, is the moving average coefficient, is the lag operator, which represents the time delay, It's in time The observed value of is a white noise term, representing random error. Based on the current real-time data, it predicts the future pain attacks and generates a prediction curve , the expression formula is: ,in is the future time step, indicating the prediction of the future The changes in the patient's physiological indicators after the time step are based on the prediction and combined with the patient's subjective pain assessment to adjust the system's prediction results. The expression formula is: ,in is the final pain prediction value, It is an objective prediction based on physiological data. It is a subjective score based on the patient's self-assessment. is a weight coefficient.

[0076] Specifically, the personalized management module targets the changes in the patient's physiological data when in pain, and has an in-depth understanding of the extent to which changes in different physiological data affect the prediction of pain attacks, and further identifies important features. By understanding the important features, doctors can choose corresponding drugs and adjust the dosage of drugs, providing an important basis for formulating personalized management plans. The assistance module predicts that the patient's physiological data is close to a certain critical value through the patient's pain attack patterns and real-time physiological data changes, and when there is a high probability of triggering a pain attack, the system will promptly remind the patient through the voice of the wearable monitoring unit to take preventive measures, including but not limited to taking painkillers or adjusting posture to avoid the risk of falling.

[0077] Remote management unit, used for patient medication management and understanding of various physiological data of the patient's body.

[0078] The Remote Management Unit includes:

[0079] The medication management system develops a personalized medication plan based on the doctor's prescription and records the patient's medication status in real time;

[0080] The remote intervention module is used by medical staff to select appropriate medications for replacement based on the patient's real-time physiological data.

[0081] The remote management unit operation process includes:

[0082] The drug management system automatically generates a personalized drug plan for patients based on the prescription information issued by the doctor, and sets the patient's daily medication time and dosage. The expression formula is: ,in is the optimized drug plan cost, which is the objective function to be minimized. is the number of medications the patient is taking, It is The actual dose of the drug, It is recommended by the doctor The target dose of the drug, It is The actual time of taking the drug, It is target time to take the drug, It is The actual frequency of taking the drug, It is The target frequency of taking the drug, are weight coefficients, which are used to control the importance of dosage, time and frequency in the optimization process. The real-time recording of the patient's medication status combined with physiological data is as follows: ,in It's in time medication status, Indicates that the patient took the medicine at this time. Indicated that no medication was taken. ,in It is the difference between the actual time of taking the medicine and the planned time of taking the medicine. is the time when the patient actually takes the medication. It is the planned medication time point, which feeds back the effect of the drug to the doctor. The feedback formula is: ,in It is a comprehensive evaluation value of the drug effect over time. It is various physiological indicators The weight of the drug effect, It's in time Various physiological indicators, including but not limited to heart rate and blood pressure, is the number of physiological indicators, is the error term, representing random noise or error, ,in is the average feedback value of the drug effect, is the length of the observation period, It's time The real-time evaluation value of the drug effect is generated by integrating the physiological data during the drug action period to generate an average feedback value of the drug effect. , doctors can adjust the patient's treatment plan based on this value.

[0083] The remote management unit operation process includes:

[0084] The remote intervention module analyzes the historical and real-time physiological data in the patient's individual database, and combines the effects of drug use to provide medical staff with a reference range and direction for adjusting the drug regimen. The expression formula is: ,in It is the magnitude or direction of the adjustment of the drug regimen, indicating the degree of adjustment of dosage or change of drug. is the weight coefficient of historical physiological data for drug adjustment, It is The weight of each physiological index, It's time The moment Real-time values ​​of physiological indicators, It is The historical average values ​​of physiological indicators, It is The historical standard deviation of each physiological indicator, is the weight coefficient of drug effect feedback on drug adjustment, It is The weight of each drug effect indicator, It's time The moment Drug effect feedback value, It is The historical average of drug effect feedback, It is The standard deviation of the drug effect feedback, is the decision coefficient for drug substitution, It is the decision value for drug replacement or adjustment. If the threshold is reached, the drug will be replaced. After the drug is replaced, the patient's medication plan will be updated in real time.

[0085] Specifically, the drug management system formulates a medication plan based on the prescription information issued by the doctor, and cooperates with the wearable monitoring unit to track the patient's medication use and follow up on the effectiveness of medication when pain occurs, so that doctors can adjust the patient's medication plan in subsequent treatment. The remote intervention module views the patient's individual database to understand the patient's treatment stage and medication use, and provides medical staff with reference data for adjusting medication plans.

[0086] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An intelligent home pain management system based on an Internet hospital, characterized in that: include: Wearable monitoring unit, used to obtain various physiological data of patients; The Internet hospital unit analyzes pain patterns based on the patient’s various physiological data; Customized management unit to manage and adjust medications based on the patient's pain pattern; Remote management unit, used for patient medication management and understanding of various physiological data of the patient's body; The wearable monitoring unit includes a smart bracelet module, which is worn on the patient's wrist. The surface of the smart bracelet module is in direct contact with the patient's skin. The internal integrated sensor can monitor the patient's physiological data in real time. The built-in communication module of the bracelet module exchanges data with the Internet hospital unit through the Internet. The smart bracelet module has a built-in alarm function. Once an abnormal physiological parameter is detected, an alarm will be sent to the guardian and medical staff through the Internet, and the patient himself will be reminded through vibration and sound. The operation process of the smart bracelet module includes: The operation process of the alarm function is: the physiological data obtained by monitoring is standardized, and the standardization formula is: Where S is a physiological parameter actually measured, S zx is the reasonable minimum value of this physiological parameter, S zd is the reasonable maximum value of this physiological parameter, S bz It is the normalized physiological parameter value, which indicates the relative position of the physiological parameter between 0 and 1. The physiological data of different dimensions are unified to the same scale for comparison, and then the risk score of abnormal data is calculated. The comprehensive anomaly detection formula is: Where F is the result of comprehensive weighted calculation of multiple physiological parameters, which is used to evaluate the overall health status. is the weighted sum of all normalized physiological parameters, m is the number of monitored physiological parameters, and α l is the weight of each physiological parameter, which is used to reflect the importance of the parameter. bz，l is the value of l physiological parameters after standardization, and the total risk threshold J is set. If F>J, trigger alarm, where trigger alarm is triggered when the risk score is greater than the threshold, reminding the guardian, medical staff and patients to pay attention; The personalized management module analyzes the characteristics of each patient's data, evaluates the importance of each feature to the system performance, and then compares the system accuracy before and after removing a feature to identify the physiological indicators that have the greatest impact on the prediction of pain onset. The expression formula for key feature identification is: where pg i is the importance score of the feature, N is the baseline accuracy of the model when all features are used, I is the model accuracy after removing a feature, J is the total number of features, Var(Z j ) is the variance of the feature, σ j It is feature Z j The weight, λ||σ|| 2 is a regularization term used to prevent the model from overfitting, and λ is a hyperparameter of the regularization strength. By introducing the system loss function F(χ), the model changes after removing a certain feature are evaluated. The expression formula is: where r i is the true label value, f(d i χ) is the use of feature d i and the prediction function with parameter χ, λ||χ|| 2 is a regularization term, which can be used to obtain a more stable system accuracy through cross-validation. The expression formula is: Where L is the average model accuracy after removing a certain feature, Bdd(g) is the accuracy of the model in the g-fold cross-validation, and pg is the average model accuracy after removing a certain feature. i Sort and select the most influential feature set U * , the expression formula is: U * = {Z j |pg j ≥Y}, where Y is a set threshold that determines which features are considered most important, and generates personalized management plans based on the features of importance.

2. According to claim 1, the intelligent home pain management system based on Internet hospital is characterized in that: The wearable monitoring unit comprises: The smart bracelet module is used to be conveniently worn on the patient's body and to come into direct contact with the patient's skin. It is also used to send and receive information and generate abnormal alarms through the Internet. The sensor module is used to monitor the patient's heart rate, body temperature, blood pressure, blood oxygen, electromyography, movement and posture. It is integrated into the wearable monitoring unit, so that the patient's multi-dimensional physiological data can be collected to provide real-time data for subsequent management plans; The Internet hospital unit includes: The pain assessment system forms subjective and objective pain assessments when pain occurs; The data center module receives the multi-dimensional physiological data of the patient, and stores and files the data after pre-processing; The data analysis module analyzes the patient's multi-dimensional physiological data to understand the impact of pain on various physical indicators of the patient, and then identify the pattern of pain attacks.

3. According to claim 2, the intelligent home pain management system based on Internet hospital is characterized in that: The customization management unit includes: The personalized management module generates a personalized management plan based on the patient's subjective pain assessment, objective pain assessment, and the patient's physiological condition when in pain; The assistance module helps patients take preventive measures in advance based on the patient's pain attack pattern, subjective pain assessment and objective pain assessment; The Remote Management Unit includes: The medication management system develops a personalized medication plan based on the doctor's prescription and records the patient's medication status in real time; The remote intervention module is used by medical staff to select appropriate medications for replacement based on the patient's real-time physiological data.

4. The intelligent home pain management system based on Internet hospital according to claim 3 is characterized in that: The wearable monitoring unit also includes: The sensor module is used to monitor and record the patient's heart rate changes, body surface temperature, blood pressure fluctuations, blood oxygen saturation, and muscle activity electrical signals in real time. It is also used to record the patient's steps, activity intensity, and posture changes to help identify abnormal physiological data and dangerous situations of falling.

5. The intelligent home pain management system based on Internet hospital according to claim 4 is characterized in that: The Internet hospital unit operation process includes: The pain assessment system, when the patient feels pain, inputs a number from 0 to 10 into the wearable monitoring unit by handwriting or voice to express the intensity of the pain, forming a subjective pain assessment. At the same time, changes in physiological data such as heart rate, body temperature, blood pressure, blood oxygen, electromyography, movement and posture during the onset of pain will form an objective pain assessment; The data center module is connected to the wearable monitoring unit via the Internet, and is used to collect the patient's real-time physiological data and physiological data during pain attacks, and process the data to remove noise or incomplete data. The data is then classified according to timestamps and categories and stored in the patient's individual database, which is shared with the external hospital system; The data analysis module accesses the individual database of the patient, analyzes the correlation between the patient's various physiological indicators and pain scores, analyzes the physiological characteristics of different pain states when pain occurs, summarizes the frequency of pain attacks including but not limited to specific time periods and specific physiological states, and uses the Pearson correlation coefficient to express the correlation as follows: in is the correlation coefficient between variables n and m, ∞ is the sample size, is the mean of variables n and m. The expression formula of physiological characteristics using K-means cluster analysis is: Formula, where T is the total clustering error, k is the number of clusters, and B t is the sample set of the tth class, n is the sample point belonging to a certain cluster, y t is the center point of the cluster, ε is the regularization parameter used to control the distance between cluster centers, Z is the total number of samples, V is the total number of classes, and Fourier transform is used to convert time domain signals into frequency domain signals to help identify periodic features. The expression formula is: Where R(f) is the frequency domain signal, n(t) is the value of the time domain signal at time, Z is the total number of time points, f is the frequency, u is the imaginary unit, c is the index of the frequency component, and C is the number of frequency components used for identification.

6. The intelligent home pain management system based on Internet hospital according to claim 5 is characterized in that: The operation process of the customized management unit includes: The assistance module analyzes the changes of various physiological indicators of patients over time at the onset of the disease according to the pattern of pain attacks, subjective pain assessment and objective pain assessment, and then predicts the future pain attacks. The expression formula is: where b is the number of autoregressive terms, h is the number of differences, y is the number of moving average terms, and β i is the autoregressive coefficient, δ j is the moving average coefficient, P is the lag operator, indicating the time delay, S t is the observed value at time t, ∈ t is a white noise term, representing random error. Based on the current real-time data, it predicts the future pain attacks and generates a prediction curve The expression formula is: Where s is the future time step, which means predicting the changes in the patient's physiological indicators after s time steps in the future. Based on the prediction, combined with the patient's subjective pain assessment, the prediction results of the system are adjusted. The expression formula is: T final =κT objective +(1-κ)T subjective , where T final is the final pain prediction value, T objective is an objective prediction based on physiological data, T subjective It is a subjective score based on the patient's self-assessment, and κ is a weighting coefficient.

7. The intelligent home pain management system based on Internet hospital according to claim 6 is characterized in that: The remote management unit operation process includes: The drug management system automatically generates a personalized drug plan for patients based on the prescription information issued by the doctor, and sets the patient's daily medication time and dosage. The expression formula is: Where H is the optimized drug plan cost, that is, the objective function to be minimized, Z is the number of drugs taken by the patient, and b i is the actual dose of the ith drug, b target，i is the target dose of the ith drug recommended by the physician, s i is the actual time of taking the i-th drug, s target，i is the target medication time for the ith drug, p i is the actual frequency of taking the i-th drug, p target，i is the target frequency of medication for the i-th drug, M1, M2, and M3 are weight coefficients, which are used to control the importance of dose, time, and frequency in the optimization process. The patient's medication status is recorded in real time and combined with physiological data. The real-time tracking formula is: Where X(t) is the medication status at time t, 1 means the patient took the medication at that time, 0 means the patient did not take the medication, Δt = S actual -S plan , where Δt is the difference between the actual medication time and the planned medication time, S actual is the time when the patient actually took the medicine, S plan It is the planned medication time point, which feeds back the effect of the drug to the doctor. The feedback formula is: Where P(t) is the comprehensive evaluation value of the drug effect over time, are the physiological indices w1(t), w2(t)..., w n (t) is the weight of the drug effect, w1(t), w2(t)..., w n (t) is the physiological index at time t, including but not limited to heart rate and blood pressure, n is the number of physiological indexes, ∈(t) is the error term, representing random noise or error, Where Y(t) is the average feedback value of the drug effect, T is the length of the observation period, and P(t) is the immediate evaluation value of the drug effect at time t. By integrating the physiological data during the drug action period, an average feedback value Y(t) of the drug effect is generated, and the doctor can adjust the patient's treatment plan based on this value.

8. The intelligent home pain management system based on Internet hospital according to claim 7 is characterized in that: The remote management unit operation process includes: The remote intervention module analyzes the historical and real-time physiological data in the patient's individual database, and combines the effects of drug use to provide medical staff with a reference range and direction for adjusting the drug regimen. The expression formula is: Where ΔY t is the magnitude or direction of drug regimen adjustment, indicating the degree of dosage adjustment or drug replacement, ε is the weight coefficient of historical physiological data on drug adjustment, is the weight of the ith physiological index, is the real-time value of the ith physiological index at time t, is the historical average value of the ith physiological index, is the historical standard deviation of the ith physiological index, φ is the weight coefficient of drug effect feedback on drug adjustment, ι j is the weight of the j-th drug effect indicator, is the j-th drug effect feedback value at time t, is the historical average of the j-th drug effect feedback, is the standard deviation of the j-th drug effect feedback, λ is the decision coefficient for drug replacement, and A is the decision value for drug replacement or adjustment. If the threshold is reached, drug replacement will be performed. After drug replacement, the patient's medication plan will be updated in real time.

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