Traditional Chinese medicine surgery postoperative pain management system and method

Abstract

The invention belongs to the technical field of traditional Chinese medicine surgery postoperative pain management, and particularly relates to a traditional Chinese medicine surgery postoperative pain management system and method. The postoperative pain risk level can be evaluated more accurately by collecting and analyzing the clinical data information of the patient, so that the most suitable pain management measure is quickly matched for the patient to be determined, the pain management is more personalized and accurate, the pain feedback data of the patient is monitored in real time, the intervention measure is adjusted according to the data, and the pain management accuracy is improved. The system can effectively reduce the pain feeling of a patient, improves the life quality of the patient, can help a doctor make a more reasonable judgment in the aspect of pain management through real-time monitoring and adjustment of intervention measures, reduces the waste of medical resources, and improves the medical efficiency.

Description

Technical Field

[0001] The present invention belongs to the technical field of postoperative pain management for traditional Chinese surgical surgery, and in particular relates to a postoperative pain management system and method for traditional Chinese surgical surgery. Background Art

[0002] With the development of modern medical technology, postoperative pain management has become an important part of improving the quality of postoperative recovery of patients. However, traditional pain management methods often rely on the doctor's experience and judgment, lack of objective quantitative standards and dynamic adjustment mechanisms, resulting in uneven pain management effects and difficulty in meeting the specific needs of different patients. Therefore, it is particularly urgent to develop and research a system that can effectively manage patients' postoperative pain.

[0003] In the prior art, there are a variety of pain management methods, but they usually lack the ability of personalization and dynamic adjustment. For example, some methods may only rely on a single pain assessment tool and cannot fully reflect the patient's pain condition. In addition, the adjustment of pain management measures often lags behind the patient's pain changes, resulting in untimely pain control and affecting the patient's recovery process. Therefore, the present invention proposes a pain management method for postoperative surgery in traditional Chinese medicine to solve the above problems. Summary of the invention

[0004] The purpose of the present invention is to provide a pain management system and method for postoperative surgery in traditional Chinese medicine, which can dynamically adjust pain management measures according to the patient's pain feedback data to achieve personalized and timely pain control.

[0005] The technical solution adopted by the present invention is as follows: A method for postoperative pain management in traditional Chinese medicine surgery, comprising: Acquiring clinical data information of the patient, wherein the clinical data information includes the patient's age, gender, and surgery type; According to the clinical data information, assess the patient's postoperative pain risk level, and match a preset intervention plan according to the pain risk level; Real-time monitoring of the pain feedback data of the patient to be determined after the intervention plan is implemented, and outputting an intervention score based on the pain feedback data; Determining an execution state of the intervention plan according to the intervention score, wherein the execution state includes a valid state and an invalid state; In the effective state, the pain intervention is continued to be performed on the patient according to the intervention plan, and the intervention score is continued to be recorded and summarized as a control parameter set; In the invalid state, the pain management measures in the intervention plan are adjusted in real time until the execution state corresponding to the pain feedback data is valid.

[0006] In a preferred embodiment, after the clinical data information is output, the clinical data information is classified and processed, and clinical data of the same age, gender and surgical type are aggregated into the same classification subset, wherein each classification subset corresponds to a pain risk level; Collect the same subset of the classification for pain relief and implement effective pain management measures; Count the number of times each effective pain management measure is performed and record it as a ranking condition parameter; According to the values ​​of the sorting condition parameters, the execution priority of each effective pain management measure is determined in descending order.

[0007] In a preferred embodiment, the step of assessing the patient's postoperative pain risk level based on the clinical data information includes: Obtain clinical data information of pending patients and match corresponding classification subsets; Obtaining a pain risk level corresponding to a classification subset, wherein the same classification subset corresponds to multiple pain risk levels; Collecting preset physiological index data at each pain risk level, wherein the physiological index data includes heart rate, blood pressure and respiratory rate; The physiological indicator data of the patient to be diagnosed is collected in real time and compared with the preset physiological indicator data to match the pain risk level of the patient to be diagnosed.

[0008] In a preferred embodiment, the step of matching the pain risk level of the patient to be determined comprises: The acquired physiological index data of the patient to be determined is vectorized to obtain a first eigenvector, wherein the vectorization processing includes: Obtain the tongue coating thickness and tongue coating crack index, and use convolutional neural network to generate tongue image feature sub-vectors; Collect the pulse depth number and pulse strength parameters, and summarize them into pulse characteristic sub-vectors; The patient's heart rate, blood pressure, respiratory rate, the tongue image feature subvector, and the pulse image feature subvector are weighted and spliced ​​according to preset weights, and output as a first feature vector; Performing time point matching on the first feature vector and the second feature vector in the preset physiological indicator database, and performing similarity calculation after the matching is completed to obtain a matching score, wherein the time point matching satisfies the timing alignment path constraint condition; The matching scores are arranged in descending order, and the maximum matching score is output as the pain risk level of the patient to be determined.

[0009] In a preferred embodiment, the intervention plan includes Chinese medicine application, acupuncture therapy, massage and oral Chinese medicine treatment measures, and the pain feedback data of the patient after the implementation of each treatment measure is recorded separately and summarized into a feedback data subset.

[0010] In a preferred embodiment, the step of outputting an intervention score based on the pain feedback data includes: Acquiring pain feedback data of the patient to be determined, and performing quantification processing to obtain a quantified value of the pain feedback data of the patient to be determined; Obtaining an evaluation interval, wherein a plurality of evaluation intervals are provided, and each evaluation interval corresponds to an intervention score; The quantized value of the pain feedback data of the pending patient is compared with each evaluation interval to determine the evaluation interval to which the pain feedback data of the pending patient belongs, and then the corresponding intervention score is synchronously output.

[0011] In a preferred embodiment, the step of determining the execution status of the intervention plan according to the intervention score comprises: Obtain intervention scores for pending patients; obtaining an assessment threshold, and comparing the assessment threshold with the intervention score; If the intervention score is less than or equal to the evaluation threshold, it indicates that the execution of the intervention plan is effective, and the execution status of the intervention plan is recorded as a valid status; If the intervention score is greater than the evaluation threshold, it indicates that the execution of the intervention plan is invalid, and the execution status of the intervention plan is recorded as an invalid status.

[0012] In a preferred embodiment, in the effective state, a monitoring period is constructed, and a plurality of sampling nodes are set within the monitoring period; Collecting pain feedback data of the patient to be determined at each sampling node, and performing quantification processing to obtain sample parameters; Obtaining an evaluation function, inputting the sample parameter into the evaluation function, and recording an output result of the evaluation function as a decay trend value; Obtaining a prediction function, and inputting the attenuation trend value and the evaluation threshold into the prediction function, and recording the output result of the prediction function as the safety intervention duration; According to the safety intervention duration, the current intervention node is offset, and the offset result is recorded as a risk node, and the intervention plan is adjusted under the risk node.

[0013] The present invention also provides a postoperative pain management system for traditional Chinese medicine surgery, using the above-mentioned postoperative pain management method for traditional Chinese medicine surgery, comprising: A data acquisition module, wherein the data acquisition module is used to obtain clinical data information of the patient, wherein the clinical data information includes the patient's age, gender and operation type; A scheme matching module, wherein the scheme matching module is used to evaluate the patient's postoperative pain risk level according to the clinical data information, and match a preset intervention scheme according to the pain risk level; A data feedback module, the data feedback module is used to monitor in real time the pain feedback data of the pending patient after the intervention plan is executed, and output an intervention score based on the pain feedback data; A state evaluation module, the state evaluation module is used to determine the execution state of the intervention plan according to the intervention score, wherein the execution state includes a valid state and an invalid state; A feedback recording module, wherein the feedback recording module is used to continue to perform pain intervention on the patient in the effective state according to the intervention plan, and continue to record the intervention score and summarize it into a control parameter set; A program adjustment module is used to adjust the pain management measures in the intervention program in real time in the invalid state until the execution state corresponding to the pain feedback data is valid.

[0014] And, an electronic device, the electronic device comprising: at least one processor; and a memory communicatively coupled to the at least one processor; Wherein, the memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the above-mentioned traditional Chinese medicine surgical postoperative pain management method.

[0015] The technical effects achieved by the present invention are: By collecting and analyzing the patient's clinical data information, the present invention can more accurately assess the postoperative pain risk level, thereby quickly matching the most suitable pain management measures for the pending patients, making pain management more personalized and accurate. By real-time monitoring of the patient's pain feedback data and adjusting intervention measures based on these data, the patient's pain perception can be effectively reduced and the patient's quality of life can be improved. In addition, real-time monitoring and adjustment of intervention measures can help doctors make more reasonable judgments in pain management, reduce waste of medical resources, and improve medical efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic flow chart of the method of the present invention; Figure 2 It is a schematic diagram of the system module of the present invention; Figure 3 It is a schematic diagram of the structure of an electronic device of the present invention. DETAILED DESCRIPTION

[0017] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0018] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0019] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure or characteristic that may be included in at least one implementation of the present invention. The phrase "in a preferred embodiment" that appears in different places in this specification does not refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments.

[0020] With the continuous development of traditional Chinese medicine technology, pain management plays an increasingly important role in postoperative recovery. Pain management can not only improve the patient's quality of life, but also accelerate the patient's recovery process. However, traditional pain management methods often rely on the doctor's experience and judgment and lack objective quantitative standards, which may lead to inaccurate and untimely pain management. The postoperative pain management method for traditional Chinese medicine surgery proposed in the present invention realizes the personalization and precision of pain management by introducing feature vector matching, pain risk level assessment, intervention score calculation and dynamic adjustment of intervention plans.

[0021] See also Figure 1 As shown, the present invention provides a method for managing pain after surgery in traditional Chinese medicine, comprising: S1. Obtain clinical data information of the patient, including the patient's age, gender and surgery type; In step S1, when taking pain management measures for patients, it is first necessary to obtain detailed clinical data information of similar patients. The clinical data information includes multiple aspects such as the patient's age, gender, and surgery type, so that existing patients can be compared with historical data to provide a basis for subsequent pain assessment and management.

[0022] Specifically, after the clinical data information is output, the clinical data information is classified and processed, and clinical data of the same age, gender and surgical type are aggregated into the same classification subset, wherein each classification subset corresponds to a pain risk level; Collect the same subset of categories for pain relief and implement effective pain management measures; Count the number of times each effective pain management measure is performed and record it as a ranking condition parameter; According to the values ​​of the sorting condition parameters, the execution priority of each effective pain management measure is determined in descending order; In the above, after the clinical data information is output, it is necessary to perform detailed classification processing on the clinical data information. Specifically, clinical data with the same characteristics in age, gender and surgical type will be merged into the same classification subset. In this process, each classification subset will correspond to a specific pain risk level for subsequent analysis and processing, and then effective pain management measures will be found so that the same means can be taken in actual operations to alleviate the pain of patients of the same type. In order to further optimize the execution effect of pain management measures, each effective pain management measure will be counted, and their execution times will be recorded as sorting condition parameters. According to the values ​​of these sorting condition parameters, they will be arranged in descending order to determine the execution priority of each effective pain management measure. The purpose is to ensure that in actual clinical operations, those pain management measures with more execution times and better effects can be given priority, thereby improving the overall pain management efficiency and the patient's treatment experience; S2. Evaluate the patient's postoperative pain risk level based on clinical data information, and match the preset intervention plan based on the pain risk level; In step S2, the patient's possible postoperative pain risk will be evaluated based on the collected clinical data information. During the evaluation process, the patient's age, gender, surgery type and other factors will be comprehensively considered to determine the patient's postoperative pain risk level, and then applied in the actual postoperative operation, that is, according to the evaluation results, the corresponding preset intervention plan is matched to ensure that the postoperative pain of the pending patient is effectively controlled, so as to quickly respond to the patient's pain needs. The preset intervention plan includes Chinese medicine plasters, acupuncture therapy, massage and oral Chinese medicine treatment measures, and the patient's pain feedback data after each treatment measure is implemented is recorded separately and summarized into a feedback data subset for subsequent analysis and program adjustment; in the preset intervention plan, each treatment measure has its specific execution standard and time interval to ensure the standardization and effectiveness of the treatment; in actual application, the doctor can dynamically adjust the intervention plan according to the patient's pain feedback data to achieve the best pain management effect; S3, real-time monitoring of the pain feedback data of the patient after the intervention plan is implemented, and outputting the intervention score based on the pain feedback data; In step S3, after the intervention plan is implemented, the pain feedback data of the pending patients needs to be monitored in real time. The pain feedback data includes the patient's subjective feeling of pain and the change of pain degree. In practical applications, it will be quantified into numerical indicators for objective evaluation. Specifically, the quantification can be implemented by asking the pending patients to score. By collecting the pain feedback data, the implementation effect of the intervention plan can be evaluated and the corresponding intervention score can be output; The steps of outputting intervention scores based on pain feedback data include: Acquiring pain feedback data of the patient to be determined, and performing quantification processing to obtain a quantified value of the pain feedback data of the patient to be determined; Obtaining an evaluation interval, wherein a plurality of evaluation intervals are set, and each evaluation interval corresponds to an intervention score; Compare the quantified value of the pain feedback data of the pending patient with each evaluation interval, determine the evaluation interval to which the pain feedback data of the pending patient belongs, and then synchronously output the corresponding intervention score; In the above, in order to output the intervention score according to the pain feedback data, it is first necessary to obtain the pain feedback data of the pending patient and convert it into a quantifiable value for further analysis. The quantification process can be performed by various methods, such as using a pain rating scale, such as a visual analog scale (VAS) or a numerical rating scale (NRS), to convert the patient's subjective feelings into a specific value. After the quantification process is completed, the quantified value of the pain feedback data of the pending patient can be obtained. Then, it is necessary to obtain a preset evaluation interval. Each evaluation interval has a specific range for evaluating the patient's pain level. For example, the evaluation interval can correspond to 0-10 points, where 0 represents no pain and 10 represents extreme pain. Each evaluation interval corresponds to a specific intervention score, which is pre-set based on clinical experience and research results. Finally, the quantified value of the pain feedback data of the pending patient is compared with each evaluation interval. By comparison, it can be determined to which evaluation interval the pain feedback data of the pending patient belongs. Once the evaluation interval is determined, the corresponding intervention score can be output synchronously to help doctors or nursing staff formulate corresponding treatment plans and intervention measures to alleviate the patient's pain and ensure that the pending patients can receive appropriate medical intervention according to their pain level.

[0023] S4. determining the execution status of the intervention plan according to the intervention score, wherein the execution status includes a valid state and an invalid state; In step S4, the execution status of the intervention plan can be determined according to the intervention score. The execution status is divided into two types: effective status and invalid status. The step of determining the execution status of the intervention plan according to the intervention score includes: Obtain intervention scores for pending patients; obtaining an assessment threshold and comparing the assessment threshold to the intervention score; If the intervention score is less than or equal to the evaluation threshold, it indicates that the implementation of the intervention plan is effective, and the implementation status of the intervention plan is recorded as effective; If the intervention score is greater than the evaluation threshold, it indicates that the implementation of the intervention plan is ineffective, and the implementation status of the intervention plan is recorded as invalid; Specifically, in the process of determining the execution status of the intervention plan, it is first necessary to obtain the intervention score of the determined pending patient and simultaneously introduce a pre-set evaluation threshold, which is the standard for judging whether the intervention plan is effective. Then, the patient's intervention score is compared with the evaluation threshold. If the comparison shows that the patient's intervention score is less than or equal to the evaluation threshold, it means that the intervention plan has achieved the expected effect on the pending patient. At this time, it means that the execution of the intervention plan is effective. In this case, the execution status of the intervention plan needs to be recorded as an effective state for subsequent management and analysis. On the contrary, if the patient's intervention score is greater than the evaluation threshold, it means that the intervention plan has not achieved the expected effect on the patient, so the execution of the intervention plan is considered invalid. In this case, the execution status of the intervention plan needs to be recorded as an invalid state so that we can adjust and improve the intervention measures in a timely manner. S5. In the effective state, continue to perform pain intervention on the patient according to the intervention plan, continue to record the intervention score, and summarize it as a control parameter set; In step S5, in the effective state, it means that the current intervention plan can effectively control the patient's pain, so the pain intervention can continue to be performed on the patient according to the plan. At the same time, the intervention scores are continued to be recorded, and the recorded intervention scores are summarized as a control parameter set for subsequent analysis and improvement. Specifically, in the effective state, a monitoring period is constructed, and multiple sampling nodes are set within the monitoring period; Collect the pain feedback data of the patients to be determined at each sampling node, perform quantitative processing, and obtain sample parameters; Obtain an evaluation function, input the sample parameter into the evaluation function, and record the output result of the evaluation function as a decay trend value; Obtain a prediction function, input the attenuation trend value and the evaluation threshold into the prediction function, and record the output result of the prediction function as the safety intervention duration; According to the duration of the safety intervention, the current intervention node is offset, and the offset result is recorded as a risk node, and the intervention plan is adjusted under the risk node; Specifically, under the premise of effective state, firstly, a specific monitoring period needs to be constructed. At the same time, sampling nodes will be set at multiple predetermined time points during the monitoring period. The sampling nodes will be used to collect pain feedback data of patients who have not yet been diagnosed. The collected pain feedback data needs to be quantified so as to convert it into sample parameters that can be analyzed. For details, refer to the quantification process of the pain feedback data mentioned above, which will not be repeated here. Then, a preset evaluation function is introduced to perform corresponding analysis and calculation on the sample parameters, wherein the expression of the evaluation function is: , where Represents the decay trend value, Indicates the length of the monitoring period. represents the number of sample parameters, and Represents the sample parameters under adjacent sampling nodes. Generally speaking, after the same pain management measure is implemented, its effect on the patient will gradually weaken over time. Therefore, the output result of the evaluation function, that is, the attenuation trend value, can reflect the effect of the pain management measure over time. At this time, it is necessary to introduce a preset prediction function to predict the future pain condition of the patient to be determined. Specifically, the attenuation trend value and an evaluation threshold are input into the prediction function for comprehensive calculation. The expression of the prediction function is: , where Indicates the duration of safety intervention. represents the evaluation threshold, It represents the pain feedback data at the current node, that is, the length of time the patient can safely receive treatment under the current intervention measures. Finally, the current intervention node will be offset according to the safe intervention time. The purpose of the offset processing is to adjust the intervention measures to ensure the safety of the patient. The offset result is recorded as a risk node, and the intervention plan is adjusted under the risk node, so as to better deal with the patient's pain condition and ensure the safety and effectiveness of the treatment.

[0024] S6. In the invalid state, the pain management measures in the intervention plan are adjusted in real time until the execution state corresponding to the pain feedback data is valid.

[0025] In step S6, in the invalid state, it means that the current intervention plan fails to effectively control the patient's pain. At this time, the pain management measures in the intervention plan need to be adjusted in real time (the specific adjustment process needs to be determined according to the actual situation, such as increasing the drug dosage, changing the drug type, using different pain relief techniques, etc.). During the adjustment process, it is necessary to pay close attention to the patient's pain feedback data until the execution state corresponding to the pain feedback data is changed to a valid state. Then the adjustment can be stopped, thereby ensuring that the patient's postoperative pain is effectively controlled and improving the patient's postoperative recovery quality.

[0026] In a preferred embodiment, the step of assessing the patient's postoperative pain risk level based on clinical data information includes: Obtain clinical data information of pending patients and match corresponding classification subsets; Obtaining the pain risk level corresponding to the classification subset, wherein the same classification subset corresponds to multiple pain risk levels; Collect preset physiological index data at each pain risk level, including heart rate, blood pressure and respiratory rate; The physiological indicator data of the patient to be diagnosed is collected in real time and compared with the preset physiological indicator data to match the pain risk level of the patient to be diagnosed.

[0027] In this embodiment, in order to accurately assess the pain risk level that the pending patient may experience after surgery, it is first necessary to obtain the clinical data information of the pending patient, and then match the clinical data information of the pending patient with a pre-set classification subset. After matching the corresponding classification subset, it is necessary to refer to the pain risk level corresponding to these classification subsets. It is worth noting that the same classification subset may correspond to multiple pain risk levels, because individual differences of each patient may lead to different pain reactions. In order to further refine the assessment of the pain risk level, it is also necessary to collect preset physiological indicator data under each pain risk level. The physiological indicator data generally include key vital signs such as heart rate, blood pressure, and respiratory rate. Finally, it is necessary to collect the physiological indicator data of the pending patient in real time, and compare and analyze these data with the preset physiological indicator data in detail. In this way, the pain risk level of the pending patient can be more accurately matched.

[0028] In a preferred embodiment, the step of matching the pain risk level of the patient to be determined includes: The acquired physiological index data of the patient to be determined is vectorized to obtain a first eigenvector. The vectorization processing includes: Obtain the tongue coating thickness and tongue coating crack index, and use convolutional neural network to generate tongue image feature sub-vectors; Collect the pulse depth number and pulse strength parameters, and summarize them into pulse characteristic sub-vectors; The patient's heart rate, blood pressure, respiratory rate, tongue image feature subvector, and pulse image feature subvector are weighted and spliced ​​according to preset weights, and output as a first feature vector; Perform time point matching on the first feature vector and the second feature vector in the preset physiological indicator database, and perform similarity calculation after the matching is completed to obtain a matching score, wherein the time point matching satisfies the timing alignment path constraint condition; Arrange the matching scores in descending order, and output the maximum matching score as the pain risk level of the patient to be determined; In this embodiment, in order to accurately assess the pain risk level of the patient to be determined, the physiological indicator data of the patient to be determined is firstly vectorized to obtain a comprehensive first eigenvector. The vectorization process includes the following steps: The tongue coating thickness and crack index of the patient are collected by the tongue image acquisition device, and the convolutional neural network technology (this is an existing mature technology and will not be described in detail here) is used to analyze the tongue image data to generate a tongue image feature sub-vector with high discrimination. Here, the tongue image feature sub-vector is recorded as ; A pulse detection instrument is used to collect the pulse depth and pulse strength parameters of the patient, and the pulse depth and pulse strength parameters are systematically summarized and processed to form a sub-vector reflecting the patient's pulse characteristic. Here, the pulse characteristic sub-vector is recorded as ; The patient's heart rate, blood pressure, respiratory rate and other basic physiological indicators are weighted and spliced ​​with the generated tongue image feature sub-vector and pulse feature sub-vector according to the pre-set weight ratio to ensure that different physiological indicators account for a reasonable proportion in the feature vector, and finally output as a comprehensive first feature vector. ,in, , It represents the basic physiological vector formed by summarizing the patient's heart rate, blood pressure, respiratory rate and other basic physiological indicators. and Respectively represent the preset weights of the tongue image feature subvector and the pulse feature subvector. Generally speaking, the weight of the tongue image feature subvector is greater than the weight of the pulse feature subvector. The specific setting needs to be made according to actual needs; Then the obtained first feature vector is matched with the second feature vector in the preset physiological indicator database at the corresponding time point (the matching accuracy is preferably less than or equal to 3s). This matching process strictly satisfies the timing alignment path constraint condition to ensure the accuracy and reliability of the matching result. The timing alignment path constraint condition is used to ensure that the changes of the patient's postoperative physiological indicator data over time are consistent with the changes of the data in the preset physiological indicator database, so as to reduce the matching error caused by time misalignment. After the matching is completed, the preset similarity calculation formula is used to calculate the matching score between the first feature vector and the second feature vector. The similarity calculation formula is: , where represents the matching score, represents the optimal alignment path, satisfying , Represents the second eigenvector. Finally, the matching scores are arranged in descending order, and the maximum matching score is selected and used as the pain risk level of the patient to be determined. This process not only simplifies the determination process of the pain risk level, but also improves the scientificity and accuracy of the determination.

[0029] See also Figure 2 The invention also provides a TCM surgical postoperative pain management system, using the TCM surgical postoperative pain management method, including: Data collection module, the data collection module is used to obtain the patient's clinical data information, the clinical data information includes the patient's age, gender and surgery type; The program matching module is used to evaluate the patient's postoperative pain risk level based on clinical data information, and match the preset intervention plan according to the pain risk level; The data feedback module is used to monitor the pain feedback data of the pending patients in real time after the intervention plan is implemented, and output the intervention score based on the pain feedback data; A status evaluation module, which is used to determine the execution status of the intervention plan according to the intervention score, wherein the execution status includes a valid state and an invalid state; A feedback recording module, which is used to continue to perform pain intervention on the patient in accordance with the intervention plan in an effective state, and continue to record the intervention score and summarize it into a control parameter set; The program adjustment module is used to adjust the pain management measures in the intervention plan in real time when the pain feedback data is in an invalid state, and stop when the execution state corresponding to the pain feedback data is in a valid state.

[0030] In the above, the system includes a data acquisition module, a program matching module, a data feedback module, a status evaluation module, a feedback recording module and a program adjustment module. The main function of the data acquisition module is to collect and obtain the patient's clinical data information, including but not limited to the patient's age, gender and the type of surgery received. The core function of the program matching module is to evaluate the patient's risk level of postoperative pain based on the collected clinical data information. Based on the evaluation, the system will automatically match the preset intervention plan. The intervention plan is formulated based on traditional Chinese medicine theory and clinical experience, and aims to minimize the patient's postoperative pain. In this way, the system can provide personalized pain management plans for different patients. The data feedback module is responsible for real-time monitoring of the patient's pain feedback data after the implementation of the intervention plan. Through continuous monitoring, the system can timely understand the effect of the intervention plan and output the corresponding intervention score based on the pain feedback data. The intervention score reflects the implementation effect of the intervention plan. The main function of the status evaluation module is The execution status of the intervention plan is determined according to the intervention score. The execution status is divided into effective and invalid states. When the intervention plan is in an effective state, it means that the pain management measures are appropriate and can effectively relieve the patient's pain. When the intervention plan is in an invalid state, the pain management measures need to be adjusted. The feedback recording module works when the intervention plan is in an effective state. It will continue to perform pain intervention on the designated patients according to the intervention plan, and continue to record the intervention score, and summarize it as a control parameter set to provide more abundant data support for subsequent pain management. The plan adjustment module works when the intervention plan is in an invalid state. It will adjust the pain management measures in the intervention plan in real time in order to achieve better pain control effects. Through continuous adjustment and optimization, the system will continue to monitor the pain feedback data until the execution status corresponding to the pain feedback data is in an effective state and then stop adjusting. This can ensure that every patient can obtain the pain management plan that best suits them, thereby minimizing postoperative pain.

[0031] See also Figure 3 , the invention provides an electronic device, the electronic device comprising: at least one processor; and a memory communicatively coupled to the at least one processor; The memory stores a computer program that can be executed by at least one processor, and the computer program is executed by at least one processor so that the at least one processor can execute the above-mentioned traditional Chinese medicine surgical postoperative pain management method.

[0032] The processor of the above electronic device may be a general-purpose processor, a special-purpose processor or a digital signal processor. The memory may be a random access memory (RAM), a read-only memory (ROM), a flash memory or other types of storage devices. A computer program may include multiple modules, each of which is responsible for performing a specific function, and may also include an operator, an input device and an output device, wherein the operator is responsible for performing the computing task, the input device is used to receive data or instructions input by the user, and the output device is used to display the processing results or information.

[0033] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0034] The above is only a preferred embodiment of the present invention. It should be noted that, for those skilled in the art, several improvements and modifications can be made without departing from the principles of the present invention, and these improvements and modifications should also be considered as the protection scope of the present invention. The structures, devices and operating methods not specifically described and explained in the present invention shall be implemented according to the conventional means in the art unless otherwise specified and limited.

Claims

1. A method for postoperative pain management in traditional Chinese medicine surgery, characterized in that: include: Acquiring clinical data information of the patient, wherein the clinical data information includes the patient's age, gender, and surgery type; According to the clinical data information, assess the patient's postoperative pain risk level, and match a preset intervention plan according to the pain risk level; Real-time monitoring of the pain feedback data of the patient to be determined after the intervention plan is implemented, and outputting an intervention score based on the pain feedback data; Determining an execution state of the intervention plan according to the intervention score, wherein the execution state includes a valid state and an invalid state; In the effective state, the pain intervention is continued to be performed on the patient according to the intervention plan, and the intervention score is continued to be recorded and summarized as a control parameter set; In the invalid state, the pain management measures in the intervention plan are adjusted in real time until the execution state corresponding to the pain feedback data is valid.

2. A method for postoperative pain management in traditional Chinese medicine surgery according to claim 1, characterized in that: After the clinical data information is output, the clinical data information is classified and processed, and clinical data of the same age, gender and surgical type are aggregated into the same classification subset, wherein each classification subset corresponds to a pain risk level; Collect the same subset of the classification for pain relief and implement effective pain management measures; Count the number of times each effective pain management measure is performed and record it as a ranking condition parameter; According to the values ​​of the sorting condition parameters, the execution priority of each effective pain management measure is determined in descending order.

3. A method for postoperative pain management in traditional Chinese medicine surgery according to claim 2, characterized in that: The step of assessing the patient's postoperative pain risk level based on the clinical data information includes: Obtain clinical data information of pending patients and match corresponding classification subsets; Obtaining a pain risk level corresponding to a classification subset, wherein the same classification subset corresponds to multiple pain risk levels; Collecting preset physiological index data at each pain risk level, wherein the physiological index data includes heart rate, blood pressure and respiratory rate; The physiological indicator data of the patient to be diagnosed is collected in real time and compared with the preset physiological indicator data to match the pain risk level of the patient to be diagnosed.

4. A method for postoperative pain management in traditional Chinese medicine surgery according to claim 3, characterized in that: The step of matching the pain risk level of the patient to be determined comprises: The acquired physiological index data of the patient to be determined is vectorized to obtain a first eigenvector, wherein the vectorization processing includes: Obtain the tongue coating thickness and tongue coating crack index, and use convolutional neural network to generate tongue image feature sub-vectors; Collect the pulse depth number and pulse strength parameters, and summarize them into pulse characteristic sub-vectors; The patient's heart rate, blood pressure, respiratory rate, the tongue image feature subvector, and the pulse image feature subvector are weighted and spliced ​​according to preset weights, and output as a first feature vector; Performing time point matching on the first feature vector and the second feature vector in the preset physiological indicator database, and performing similarity calculation after the matching is completed to obtain a matching score, wherein the time point matching satisfies the timing alignment path constraint condition; The matching scores are arranged in descending order, and the maximum matching score is output as the pain risk level of the patient to be determined.

5. A method for postoperative pain management in traditional Chinese medicine surgery according to claim 1, characterized in that: The intervention program includes Chinese medicine plasters, acupuncture therapy, massage and oral Chinese medicine treatment measures. The pain feedback data of the patient after each treatment measure is recorded separately and summarized into a feedback data subset.

6. A method for postoperative pain management in traditional Chinese medicine surgery according to claim 1, characterized in that: The step of outputting an intervention score according to the pain feedback data comprises: Acquiring pain feedback data of the patient to be determined, and performing quantification processing to obtain a quantified value of the pain feedback data of the patient to be determined; Obtaining an evaluation interval, wherein a plurality of evaluation intervals are provided, and each evaluation interval corresponds to an intervention score; The quantized value of the pain feedback data of the pending patient is compared with each evaluation interval to determine the evaluation interval to which the pain feedback data of the pending patient belongs, and then the corresponding intervention score is synchronously output.

7. A method for postoperative pain management in traditional Chinese medicine surgery according to claim 1, characterized in that: The step of determining the execution status of the intervention plan according to the intervention score comprises: Obtain intervention scores for pending patients; obtaining an assessment threshold, and comparing the assessment threshold with the intervention score; If the intervention score is less than or equal to the evaluation threshold, it indicates that the execution of the intervention plan is effective, and the execution status of the intervention plan is recorded as a valid status; If the intervention score is greater than the evaluation threshold, it indicates that the execution of the intervention plan is invalid, and the execution status of the intervention plan is recorded as an invalid status.

8. A method for postoperative pain management in traditional Chinese medicine surgery according to claim 7, characterized in that: In the effective state, a monitoring period is constructed, and a plurality of sampling nodes are set within the monitoring period; Collecting pain feedback data of the patient to be determined at each sampling node, and performing quantification processing to obtain sample parameters; Obtaining an evaluation function, inputting the sample parameter into the evaluation function, and recording an output result of the evaluation function as a decay trend value; Obtaining a prediction function, and inputting the attenuation trend value and the evaluation threshold into the prediction function, and recording the output result of the prediction function as the safety intervention duration; According to the safety intervention duration, the current intervention node is offset, and the offset result is recorded as a risk node, and the intervention plan is adjusted under the risk node.

9. A pain management system for postoperative surgery in traditional Chinese medicine, characterized in that: The method for postoperative pain management of traditional Chinese medicine surgery according to any one of claims 1 to 8 comprises: A data acquisition module, wherein the data acquisition module is used to obtain clinical data information of the patient, wherein the clinical data information includes the patient's age, gender and operation type; A scheme matching module, wherein the scheme matching module is used to evaluate the patient's postoperative pain risk level according to the clinical data information, and match a preset intervention scheme according to the pain risk level; A data feedback module, the data feedback module is used to monitor in real time the pain feedback data of the pending patient after the intervention plan is executed, and output an intervention score based on the pain feedback data; A state evaluation module, the state evaluation module is used to determine the execution state of the intervention plan according to the intervention score, wherein the execution state includes a valid state and an invalid state; A feedback recording module, wherein the feedback recording module is used to continue to perform pain intervention on the patient in the effective state according to the intervention plan, and continue to record the intervention score and summarize it into a control parameter set; A program adjustment module is used to adjust the pain management measures in the intervention program in real time in the invalid state until the execution state corresponding to the pain feedback data is valid.

10. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively coupled to the at least one processor; Wherein, the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for postoperative pain management in traditional Chinese medicine surgery as described in any one of claims 1 to 8.

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