A follow-up tracking system for micro-fire needle massage combined with rehabilitation curative effect

Abstract

The present application relates to the technical field of medical data processing, in particular to a follow-up tracking system for micro-fire needle massage combined with rehabilitation efficacy. The system comprises: a data acquisition module for collecting examination sample data of patients, including acupoint parameters, operation parameters and disease severity scores; a follow-up priority adjustment coefficient determination module for analyzing the change characteristics of acupoint parameters and operation parameters in the examination sample data, reflecting the stable change of data of patients in the long-term treatment process, and determining the follow-up priority adjustment coefficient; a follow-up priority determination module for calculating priority indexes according to the follow-up priority adjustment coefficient of the examination sample data and the disease severity score at each examination, so as to determine the priority of the examination sample data for follow-up tracking. In summary, the present application combines long-term and short-term results of the treatment process, can improve the determination accuracy of the follow-up priority, and ensures the reasonable allocation of medical resources.

Description

Technical Field

[0001] This invention relates to the field of medical data processing technology, specifically to a follow-up tracking system for the therapeutic effects of micro-fire needle massage combined with rehabilitation. Background Technology

[0002] Micro-fire needle massage, a therapy combining traditional Chinese medicine theory with modern rehabilitation medicine techniques, has shown significant therapeutic effects in pain management and musculoskeletal rehabilitation in recent years. This therapy controls the temperature of the fire needles, combining them with massage techniques to stimulate specific acupoints, thereby regulating Qi and blood, clearing meridians, relieving pain, and promoting tissue repair. Furthermore, medical institutions typically follow up with patients receiving micro-fire needle massage treatment to accurately identify and dynamically adjust the treatment based on individual patient needs.

[0003] Current technologies for patient follow-up typically focus only on the patient's most recent examination results. In other words, existing follow-up priorities tend to focus on the short-term performance of the patient's examination results, while failing to adequately consider changes in data and treatment methods during the long-term treatment process. Therefore, in situations with limited medical resources, the existing follow-up priority ranking can lead to inaccurate priority ranking and cannot ensure that critical medical resources are used in a timely manner for the patients who need them most. Summary of the Invention

[0004] To address the issue that existing follow-up prioritization methods often focus on short-term patient test results while neglecting long-term data changes and treatment variations, which can lead to inaccurate prioritization in situations with limited medical resources and prevent critical medical resources from being used for patients most in need, this invention aims to provide a follow-up tracking system for the therapeutic effects of micro-fire needling massage combined with rehabilitation. The specific technical solution adopted is as follows:

[0005] This invention proposes a follow-up tracking system for the therapeutic effects of micro-fire needle massage combined with rehabilitation, the system comprising:

[0006] The data acquisition module is used to acquire examination sample data for each patient. The examination sample data includes acupoint parameters, operation parameters, and disease severity scores for each examination. The acupoint parameters include acupoint prescriptions and the number of acupoints in each prescription. The operation parameters include acupuncture duration, massage duration, and massage intensity.

[0007] The follow-up priority adjustment coefficient determination module is used to analyze the changing trends and numerical characteristics of acupoint parameters in the examination sample data of each patient, and combine the discrete and continuous changes of acupoint parameters to determine the first data stability characteristic value of each examination sample data; determine the second data stability characteristic value of each examination sample data based on the changing trends and differences between operating parameters; and fuse the first data stability characteristic value and the second data stability characteristic value to determine the follow-up priority adjustment coefficient of each examination sample data.

[0008] The follow-up priority determination module is used to determine priority indicators based on the disease severity score and follow-up priority adjustment coefficient of each examination sample data, so as to follow up and track patients.

[0009] Furthermore, the method for obtaining the first stable feature value of the data includes:

[0010] Select one patient's examination sample data as the test sample data, and determine the macro-relieving factor of the test sample data based on the changing trend and numerical characteristics of the number of acupoints in the test sample data.

[0011] In the sample data to be tested, the discrete changes of acupoint parameters are analyzed to determine the data complexity factor of the sample data to be tested.

[0012] Based on the continuous changes in acupoint parameters in the sample data to be tested, the scheme change factor of the sample data to be tested is determined.

[0013] The product of the data complexity factor and the scheme change factor is normalized by negative correlation mapping, and the value is used as the micro-mitigation factor of the sample data to be tested.

[0014] The normalized value of the product of the macro-level mitigation factor and the micro-level mitigation factor of the test sample data is used as the first data stability feature value of the test sample data.

[0015] Furthermore, the method for obtaining the macroscopic mitigation factor includes:

[0016] In the sample data to be tested, the number of acupoints corresponding to all acupoint prescriptions was fitted with a straight line according to the examination time sequence based on the least squares method, and the slope value of the fitted line was normalized and used as the parameter for the change in the number of acupoints.

[0017] The macroscopic relief factor of the test sample data is obtained by negatively mapping the product of the acupoint number change parameter and the maximum acupoint number and normalizing it.

[0018] Furthermore, the method for obtaining the data complexity factor includes:

[0019] In the sample data to be tested, acupoint prescriptions with the same prescriptions are grouped together to obtain all types of acupoint prescriptions;

[0020] All types of acupoint prescriptions are paired up to obtain all unique combinations;

[0021] In each combination, the union and intersection of acupoints between the two acupoint prescriptions are obtained, and the difference between the number of acupoints in the union and the number of acupoints in the intersection is used as the distinguishing factor between the two acupoint prescriptions.

[0022] The normalized value of the product of the mean of the distinguishing factors corresponding to all combinations and the number of types of acupoint prescriptions is used as the data complexity factor of the sample data to be tested.

[0023] Furthermore, the method for obtaining the scheme change factor includes:

[0024] In the sample data to be tested, all acupoint prescriptions are arranged according to the time sequence of the examination to obtain a sorted sequence;

[0025] In the sorting sequence, adjacent acupoint prescriptions are compared and merged to obtain all subsequences;

[0026] The ratio of the number of subsequences to the total number of acupoint prescriptions in the sample data to be tested is used as the scheme change coefficient;

[0027] In the sorting sequence, the mean number of acupoints in the acupoint prescription in each subsequence is used as the quantitative feature value of each subsequence, the absolute value of the difference between the quantitative feature values ​​of each two adjacent subsequences is used as the difference factor between each two adjacent subsequences, the sum of the acupoint prescriptions in each two adjacent subsequences is used as the stationarity factor, and the ratio of the difference factor to the stationarity factor is used as the scheme change factor between each two adjacent subsequences.

[0028] In the sorted sequence, the product of the sum of the scheme change factors among all subsequences and the scheme change coefficient is normalized and used as the scheme change factor of the sample data to be tested.

[0029] Furthermore, the method for obtaining the subsequence includes:

[0030] In the sorting sequence, the first acupoint prescription is used as the first prescription to be tested and the traversal begins. Each prescription to be tested corresponds to a set.

[0031] If the last acupoint prescription in the set corresponding to the prescription to be tested is the same as the next adjacent acupoint prescription, then the next adjacent acupoint prescription is added to the set corresponding to the prescription to be tested and the traversal continues.

[0032] If the last acupoint prescription in the set corresponding to the prescription to be tested is different from the next adjacent acupoint prescription, then the set corresponding to the prescription to be tested is taken as a subsequence, and the next adjacent acupoint prescription is taken as a new prescription to be tested and the traversal continues.

[0033] After traversing all subsequences, you will obtain all the subsequences.

[0034] Furthermore, the method for obtaining the second stable data feature value includes:

[0035] In each examination sample data, the operation parameters at each examination are dimensionality reduced to obtain treatment indicators, and the treatment indicators are arranged according to the time sequence of the examination to obtain an arrangement sequence;

[0036] In each examination sample data, based on the correspondence between acupoint prescriptions and treatment indicators, the permutation sequence is divided according to the subsequences corresponding to the acupoint prescriptions to obtain all treatment indicator sequences;

[0037] In each treatment indicator sequence, all treatment indicators are fitted with a straight line according to the examination time sequence based on the least squares method, and the slope value corresponding to the fitted line is normalized and used as the treatment degree change coefficient.

[0038] In each treatment indicator sequence, the normalized value of the difference between the last treatment indicator and the first treatment indicator is used as the treatment degree change value.

[0039] In all treatment indicator sequences, the change values ​​of treatment degree are weighted and averaged based on the coefficient of change of treatment degree to obtain the change characteristic value;

[0040] The value obtained by negatively correlated mapping and normalization of the changed feature value is used as the second data stability feature value for each examined sample data.

[0041] Furthermore, the method for obtaining the follow-up priority adjustment coefficient includes:

[0042] The sum of the first and second stable eigenvalues ​​of each examination sample data is negatively correlated and normalized, and this value is used as the follow-up priority adjustment coefficient for each examination sample data.

[0043] Furthermore, the method for determining the priority indicators includes:

[0044] The normalized value of the product of the disease severity score at the last examination in each examination sample and the follow-up priority adjustment coefficient of each examination sample is used as the priority indicator for each examination sample.

[0045] Furthermore, the follow-up and tracking of patients includes:

[0046] When follow-up is required, all patients are classified according to their symptoms. Within each symptom category, the corresponding examination sample data are sorted in descending order according to the corresponding priority index to obtain a descending sequence. Within each symptom category, the corresponding patients are followed up based on the order of the examination sample data in the descending sequence.

[0047] The present invention has the following beneficial effects:

[0048] This invention, in determining patient follow-up priorities, incorporates factors such as data changes and treatment method variations during long-term treatment, thereby enabling more accurate determination of each patient's follow-up priority and contributing to the rational allocation of medical resources. First, examination sample data is collected for each patient, including acupoint parameters (acupoint prescription and number of acupoints), operational parameters (acupuncture duration, massage intensity, and massage duration), and symptom severity scores for each examination. Given that patients' symptom status changes as treatment progresses, and therefore treatment plans also change accordingly, this invention characterizes changes in patient symptom status based on changes in treatment plans. Specifically, it analyzes the trends, numerical characteristics, discrete changes, and continuous changes of acupoint parameters in the examination sample data, determining a first stable data characteristic value to reflect the changing characteristics of acupoint selection during treatment. Then, through trend analysis and difference quantification of operational parameters, a second stable data characteristic value is determined to reflect the changing characteristics of manipulation techniques during treatment. Integrating the first and second stable data characteristic values ​​helps to more accurately reflect patients' treatment response and recovery progress, and determines the follow-up priority adjustment coefficient for each examination sample data. Finally, in the follow-up priority determination module, priority indicators are determined based on the follow-up priority adjustment coefficient and the disease severity score of the examination sample data, thereby enabling personalized follow-up tracking for all patients. This function ensures the efficient use of follow-up resources, allowing patients who need the most attention to receive timely follow-up services, which is beneficial to improving the utilization rate of medical resources and patient satisfaction. Attached Figure Description

[0049] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1This is a system block diagram of a follow-up tracking system for the therapeutic effect of micro-fire needle massage combined with rehabilitation provided in one embodiment of the present invention;

[0051] Figure 2 This is a quantitative table of massage intensity provided in one embodiment of the present invention;

[0052] Figure 3 This is a flowchart of a method for obtaining a first stable data feature value according to an embodiment of the present invention;

[0053] Figure 4 This is a flowchart of a method for obtaining microscopic mitigation factors according to an embodiment of the present invention;

[0054] Figure 5 This is a schematic diagram of the system structure of a follow-up tracking system for the therapeutic effect of micro-fire needle massage combined with rehabilitation provided in an embodiment of the present invention. Detailed Implementation

[0055] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of an intelligent monitoring system and method for building suspended platforms proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0056] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0057] The following description, in conjunction with the accompanying drawings, details a specific scheme for a follow-up tracking system for the therapeutic effects of micro-fire needle massage combined with rehabilitation provided by the present invention.

[0058] Please see Figure 1 The diagram shows a system block diagram of a follow-up tracking system for the therapeutic effect of micro-fire needle massage combined with rehabilitation provided by an embodiment of the present invention. The system includes: a data acquisition module 101, a follow-up priority adjustment coefficient determination module 102, and a follow-up priority determination module 103.

[0059] The data acquisition module 101 is used to acquire examination sample data for each patient. The examination sample data includes acupoint parameters, operation parameters, and disease severity scores for each examination. The acupoint parameters include acupoint prescriptions and the number of acupoints in each prescription. The operation parameters include acupuncture duration, massage duration, and massage intensity.

[0060] Micro-fire needle massage is a traditional Chinese medicine treatment method that combines micro-fire needle therapy with massage techniques. Medical institutions follow up with patients based on their rehabilitation effects, which can help the medical team monitor the patient's recovery progress more effectively, adjust the treatment plan in a timely manner, and provide patients with continuous support and guidance.

[0061] During treatment, a patient's condition may change, and the treatment plan will also change accordingly. Therefore, in order to more accurately determine the follow-up priority of each patient and make more rational use of limited medical resources, this embodiment of the invention will focus more on the changes in patient data and treatment plans during long-term treatment.

[0062] Therefore, in the data acquisition module, the first step is to acquire examination sample data for each patient based on the medical institution's electronic medical record system or medical database. This data includes acupoint parameters, operational parameters, and symptom severity scores for each micro-needle massage treatment or examination. The acupoint parameters include the acupoint prescription (specific acupoint combinations) and the number of acupoints in each prescription, which helps analyze the patient's response to different acupoint stimulations and the therapeutic effect during each treatment or examination. The operational parameters include the needle insertion duration (from the first needle insertion to the last needle withdrawal), massage duration, and massage intensity, reflecting the treatment techniques and skills, and directly related to the patient's treatment experience and outcome. Massage intensity can be quantified using the Visual Analogue Scale (VAS). Please refer to [link to relevant documentation]. Figure 2 It displays a quantitative table of massage intensity, where a higher score indicates greater massage intensity. The severity score is obtained based on the doctor's clinical assessment record of the patient during each treatment or examination, with a specific score of 1 (normal) to 7 (very severe), with the severity level increasing sequentially.

[0063] It should be noted that the collection and acquisition of various data in the embodiments of the present invention are all authorized by the relevant users, and the process does not violate relevant laws and regulations, nor does it violate public order and good morals.

[0064] The follow-up priority adjustment coefficient determination module 102 is used to analyze the changing trends and numerical characteristics of acupoint parameters in the examination sample data of each patient, and combine the discrete and continuous changes of acupoint parameters to determine the first data stable characteristic value of each examination sample data; determine the second data stable characteristic value of each examination sample data according to the changing trends and differences between operating parameters; and combine the first data stable characteristic value and the second data stable characteristic value to determine the follow-up priority adjustment coefficient of each examination sample data.

[0065] When symptoms change, taking cervical spondylosis as an example, as the condition improves, the range and intensity of neck pain usually decrease. In this case, the treatment plan for micro-fire needle massage needs to be adjusted accordingly, including reducing the number of acupoints, decreasing the massage intensity, and shortening the treatment time. These adjustments help maintain the treatment effect while avoiding overstimulation of areas that have already improved. Conversely, as cervical spondylosis worsens, micro-fire needle massage treatment requires increasing the number of acupoints, increasing the massage intensity, and extending the treatment time. These adjustments aim to better relieve pain, improve local blood circulation, and reduce muscle tension. Therefore, in this embodiment of the invention, the follow-up priority adjustment coefficient module is used to deeply analyze the changes in acupoint parameters and operational parameters in the patient's examination sample data during long-term treatment. This allows for a comprehensive consideration of changes in examination data and treatment plans during treatment, thereby fusing together the follow-up priority adjustment coefficient for each examination sample data. This helps to more rationally adjust priorities in subsequent processes and achieve a reasonable allocation of follow-up resources.

[0066] Examining the acupoint parameters in the sample data, including acupoint prescriptions and the number of acupoints in each prescription, is a core component of the micro-fire needle massage treatment plan. By analyzing the changing trends and numerical characteristics of acupoint parameters, the adjustments made by the doctor to the treatment plan based on the patient's specific condition and recovery progress can be intuitively reflected. Simultaneously, the analysis of discrete and continuous changes in acupoint parameters can further reveal the fine-tuning of the treatment plan over time. Based on the aforementioned logical analysis, the first stable characteristic value of each sample data can be determined. By analyzing the dynamic changes in the treatment plan, the changes in the examination data can be reflected.

[0067] Preferably, in one embodiment of the present invention, the method for obtaining the first stable data feature value includes:

[0068] Please see Figure 3 The diagram illustrates a method flowchart for obtaining a first stable data feature value according to an embodiment of the present invention. The method includes the following steps:

[0069] Step S201: Select any patient's examination sample data as the test sample data. Based on the changing trend and numerical characteristics of the number of acupoints in the test sample data, determine the macroscopic relief factor of the examination sample data.

[0070] For ease of subsequent explanation and clarification, we will select one patient's examination sample data as the test sample data.

[0071] In the test sample data, the number of acupoints corresponding to all acupoint prescriptions was linearly fitted according to the examination time sequence using the least squares method. The slope value of the fitted line was then normalized and used as a parameter for the change in the number of acupoints. If the number of acupoints decreases over time, it can be considered that the area requiring acupuncture is shrinking. Therefore, the smaller the negative value of the parameter for the change in the number of acupoints, the faster the number of acupoints decreases. This characteristic of the test sample data tending towards stable change is considered to indicate a higher degree of relief. The normalization method used here can be... function.

[0072] It should be noted that the least squares method for obtaining the fitted line is a well-known technique, and the specific process will not be elaborated here.

[0073] The smaller the maximum number of acupoints, the lower the severity of the condition; and the smaller the change in the number of acupoints, the higher the degree of relief. Therefore, a negative correlation mapping and normalization are performed on the product of the change in the number of acupoints and the maximum number of acupoints to correct the logical relationship and obtain the macro-relief factor of the test sample data. The larger the macro-relief factor, the lower the data fluctuation represented by the test sample data at the overall level of the number of acupoints, indicating a higher degree of relief. This negative correlation mapping and normalization can be performed using the formula... ,in, Let x represent an exponential function with the natural constant e as the base, and let x represent the independent variable.

[0074] Step S202: Analyze the discrete and continuous changes of acupoint parameters in the test sample data to determine the microscopic relief factors of the test sample data.

[0075] In step S201, only the changes in the number of acupoints were considered at the macro level. However, since the changes in the treatment plan and the condition could not be characterized when the number of acupoints was the same, this step combined the changes in the acupoint prescription with the number of acupoints in more detail to obtain the micro-relief factor of the sample data to be tested.

[0076] Please see Figure 4 The diagram illustrates a method flowchart for obtaining microscopic mitigation factors according to an embodiment of the present invention, the method comprising the following steps:

[0077] Step S2021: In the sample data to be tested, analyze the discrete changes of acupoint parameters and determine the data complexity factor of the sample data to be tested.

[0078] Different pain ranges correspond to different acupoints. Therefore, by analyzing the changes in acupoint prescriptions, it is possible to determine whether the patient's pain range has changed, thereby quantifying the data complexity factor of the sample data.

[0079] In the sample data to be tested, acupoint prescriptions with the same prescriptions are first grouped into one category, thus obtaining all kinds of acupoint prescriptions. The more types of acupoint prescriptions there are, the more significant the changes in the acupoint prescriptions during the treatment process are, the more significant the changes in the characterization data are, and the increased complexity.

[0080] All types of acupoint prescriptions are paired up to obtain all non-repeating combinations. For example, if the acupoint prescriptions are divided into 4 categories, labeled 1, 2, 3, and 4, then the final combinations are (1, 2), (1, 3), (1, 4), (2, 3), (2, 4), and (3, 4), for a total of 6 combinations.

[0081] In each combination, the union and intersection of acupoints between the two acupoint prescriptions are obtained. The intersection reflects the degree of overlap between the two acupoint prescriptions, that is, the similarity of the treatment plans. The larger the number of acupoints in the intersection, the higher the similarity, and the lower the complexity of the data. Then, the difference between the number of acupoints in the union and the number of acupoints in the intersection is used as the distinguishing factor between the two acupoint prescriptions. The larger the distinguishing factor, the smaller the repetition between the two acupoint prescriptions, and the higher the complexity of the sample data.

[0082] Finally, the normalized value obtained by multiplying the mean of the distinguishing factor corresponding to all combinations by the number of acupoint prescription types is used as the data complexity factor of the test sample data. The larger the number of acupoint prescription types, the greater the change in the treatment plan. The larger the mean of the distinguishing factor, the higher the complexity of the data. Therefore, the larger the data complexity factor obtained by multiplying the two, the more complex the treatment plan of the test sample data has changed during the treatment process, and the higher the complexity of the data. Normalization is a technique well known to those skilled in the art. The normalization function can be linear normalization or standard normalization, etc., and the specific normalization method is not limited here.

[0083] Step S2022: Based on the continuous changes in acupoint parameters in the sample data to be tested, determine the scheme change factor of the sample data to be tested.

[0084] Under normal circumstances, if the condition gradually improves during continuous treatment, there will usually be no significant abrupt changes in adjacent treatment plans, meaning that the data stability is good. Therefore, in this step, we further analyze the continuous changes of acupoint parameters over time to more precisely determine the plan change factor of the test sample data, which is used to reflect the characteristics of stable data changes.

[0085] First, in the sample data to be tested, all acupoint prescriptions are arranged according to the examination time sequence to obtain a sorted sequence.

[0086] Then, in the sorted sequence, adjacent acupoint prescriptions are compared and merged to obtain all subsequences. Methods for obtaining subsequences include:

[0087] In the sorted sequence, the first acupoint prescription is used as the starting point for traversal, and the prescriptions to be tested correspond to a set.

[0088] If the last acupoint prescription in the set corresponding to the prescription to be tested is the same as the next adjacent acupoint prescription, then the next adjacent acupoint prescription is added to the set corresponding to the prescription to be tested and the traversal continues; if the last acupoint prescription in the set corresponding to the prescription to be tested is different from the next adjacent acupoint prescription, then the set corresponding to the prescription to be tested is treated as a subsequence, and the next adjacent acupoint prescription is treated as a new prescription to be tested and the traversal continues.

[0089] After traversing all subsequences, you will obtain all the subsequences.

[0090] Thus, all subsequences were obtained, and the acupoint prescriptions in each subsequence were the same and had temporal continuity.

[0091] The number of subsequences can reflect the changes in acupoint prescriptions over time. The more subsequences there are, the more times the acupoint prescriptions have been adjusted over time. Therefore, after obtaining all the subsequences, the ratio of the number of subsequences to the total number of acupoint prescriptions in the test sample data is used as the prescription change coefficient. The larger the prescription change coefficient is, the greater the frequency of acupoint prescription changes.

[0092] In the sorted sequence, the mean number of acupoints in the acupoint prescription in each subsequence is calculated as the quantitative characteristic value of each subsequence. The absolute value of the difference between the quantitative characteristic values ​​of each two adjacent subsequences is used as the difference factor between each two adjacent subsequences. The difference factor reflects the degree of change in the acupoint prescription in terms of the number of acupoints; the larger the value, the greater the difference in the acupoint prescription between the two adjacent subsequences, that is, the greater the degree of mutation. The sum of the acupoint prescriptions in each two adjacent subsequences is used as the stationarity factor. The smaller the stationarity factor, the fewer the number of times the acupoint prescription before and after the change occurs, which also indicates that the treatment plan changes more frequently. Therefore, the ratio of the difference factor to the stationarity factor is used as the plan change factor between each two adjacent subsequences. At this time, the larger the plan change factor, the more significant the mutation in the acupoint prescription, that is, the treatment plan, between adjacent subsequences.

[0093] Finally, in the sorted sequence, the product of the sum of the scheme change factors among all subsequences and the scheme change coefficient is normalized and used as the scheme change factor for the test sample data. Based on the aforementioned analysis, it can be seen that the larger the scheme change coefficient, the greater the frequency of acupoint prescription changes; the larger the sum of the scheme change factors, the more obvious the changes in the acupoint prescriptions of the test sample data during treatment. Therefore, a larger scheme change factor can be considered as the more frequent and greater the changes in the acupoint prescriptions of the test sample data during treatment, and thus the higher the complexity of the data. Normalization is a technique well-known to those skilled in the art. The choice of normalization function can be linear normalization or standard normalization, etc., and the specific normalization method is not limited here.

[0094] Step S2023: Integrate the data complexity factor and scheme change factor of the sample data to be tested to obtain the micro-mitigation factor.

[0095] Based on the analysis in the preceding steps, it is known that both the data complexity factor and the scheme change factor are positively correlated with the data complexity. Therefore, the product of the data complexity factor and the scheme change factor is subjected to negative correlation mapping and normalization to correct the logical relationship, thereby obtaining the micro-relief factor of the test sample data. The larger the micro-relief factor, the lower the complexity of the symptom represented by the test sample data, and the better the stability. This negative correlation mapping and normalization can be performed using the formula... ,in, Let x represent an exponential function with the natural constant e as the base, and let x represent the independent variable.

[0096] Step S203: Based on the macro-level mitigation factor and micro-level mitigation factor of the sample data to be tested, obtain the first data stability characteristic value of the sample data to be tested.

[0097] A larger macro-level remission factor indicates a more stable change in the tested sample data, signifying a higher degree of remission. Conversely, a larger micro-level remission factor indicates a lower degree of disease complexity. Therefore, the normalized product of the macro-level and micro-level remission factors of the tested sample data is used as the first data stability characteristic value. A larger first data stability characteristic value indicates better data stability and less complex changes during treatment. Normalization is a well-known technique in the field, and the normalization function can be linear or standard, etc. The specific normalization method is not limited here.

[0098] In the aforementioned steps, the changes in acupoint parameters were analyzed to obtain the first stable characteristic value of the test sample data. Since the operation parameters (acupuncture duration, massage duration, and massage intensity) can also reflect the changes in the treatment plan of the test sample data, the changing trends and differences between the operation parameters were analyzed to determine the second stable characteristic value of the test sample data.

[0099] Preferably, in one embodiment of the present invention, the method for obtaining the second data stability feature value includes:

[0100] In the sample data to be tested, the operational parameters of each examination are reduced in dimensionality: the three indicators of the operational parameters of each examination are standardized and the mean is calculated as the treatment indicator. Since the more severe the condition, the longer the acupuncture time, massage time and massage intensity will be, the smaller the treatment indicator is, the more the data tends to be in a normal trend, which can be regarded as better stability. After obtaining the treatment indicator for each examination, the treatment indicator is arranged according to the time sequence of the examination to obtain the permutation sequence.

[0101] When calculating the first stable feature value of the data, the acupoint prescriptions in the test sample data were divided into subsequences. Since the same acupoint prescription can be regarded as the same disease range, in order to analyze the changes of the operating parameters during the treatment process, the arrangement sequence of the treatment indicators was divided based on all the subsequences corresponding to the acupoint prescriptions according to the correspondence between the acupoint prescriptions and the treatment indicators, and all treatment indicator sequences were obtained. Each treatment indicator sequence is a set of treatment indicators under the same disease range.

[0102] Then, within each treatment indicator sequence, a linear fit is performed on all treatment indicators according to the examination time sequence using the least squares method. The slope value of the fitted line is then normalized and used as the treatment degree variation coefficient. The smaller the treatment degree variation coefficient, the greater the degree to which the treatment indicators tend to change towards normal within the same symptom range, i.e., the better the stability. The normalization here can be achieved using... function.

[0103] It should be noted that the least squares method for obtaining the fitted line is a well-known technique, and the specific process will not be elaborated here.

[0104] In each treatment indicator sequence, the normalized value of the difference between the last and first treatment indicators is used as the treatment severity change value. Since a smaller treatment indicator value indicates better stability, a negative and smaller difference value indicates a smaller treatment severity change value, suggesting a greater degree of normalization and stability. The normalization here can be performed using... function.

[0105] In all treatment indicator sequences, the change values ​​of treatment severity are weighted and averaged based on the coefficient of change of treatment severity to obtain the characteristic value of change: based on The function normalizes the coefficient of change in treatment severity and uses it as a weighting factor. Then, it multiplies the weighting factor corresponding to each treatment indicator sequence with the value of change in treatment severity to obtain a weighted factor. The smaller the weighted factor, the higher the degree of normal variation in the data and the better the stability. The mean of all weighted factors is normalized and used as the characteristic value of change. Based on the aforementioned analysis, the smaller the characteristic value of change, the higher the stability of the data.

[0106] Therefore, the change feature values ​​are finally negatively correlated and normalized to correct the logical relationship, resulting in the second stable feature value of the test sample data. The larger the second stable feature value, the higher the degree of normalization and the better the stability of the test sample data during treatment. Normalization is a well-known technique in the field, and the normalization function can be linear normalization or standard normalization, etc. The specific normalization method is not limited here.

[0107] After obtaining the first and second stable feature values ​​of the sample data to be tested, the two can be fused to determine the follow-up priority adjustment coefficient of the sample data to be tested.

[0108] Preferably, in one embodiment of the present invention, the method for obtaining the follow-up priority adjustment coefficient includes:

[0109] The sum of the first and second stable eigenvalues ​​of the test sample data is calculated. A larger sum indicates a higher degree of data stability. Therefore, to ensure a more rational allocation of medical resources, the follow-up priority of the test sample data should be lowered. This sum is then negatively correlated and normalized, serving as the follow-up priority adjustment coefficient for each test sample data. A larger follow-up priority adjustment coefficient indicates greater data complexity and more abnormal data variation characteristics, thus requiring more timely follow-up. The negative correlation mapping and normalization process can be performed using the formula... ,in, Let x represent an exponential function with the natural constant e as the base, and let x represent the independent variable.

[0110] At this point, the module can obtain the follow-up priority adjustment coefficient for each examination sample data.

[0111] The follow-up priority determination module 103 is used to determine priority indicators based on the disease severity score and follow-up priority adjustment coefficient of each examination sample data, so as to follow up and track patients.

[0112] The severity score of the disease at each examination can reflect the patient's current condition, while the follow-up priority adjustment coefficient of each examination sample data comprehensively analyzes the changes in the patient's examination data during long-term treatment. This allows for a more comprehensive and accurate assessment of the patient's possible condition at the next examination or treatment. Therefore, combining the two to determine the priority index of each examination sample data for follow-up tracking of patients can lead to a more rational allocation of medical resources.

[0113] Preferably, in one embodiment of the present invention, the method for obtaining the priority index includes:

[0114] A higher severity score during a particular examination indicates a more severe condition at that time, and the patient should be given a higher priority during follow-up. Similarly, a higher follow-up priority adjustment coefficient for a particular examination sample indicates weaker stability of the data during long-term treatment and requires a higher priority.

[0115] Therefore, the normalized value of the product of the disease severity score at the last examination and the follow-up priority adjustment coefficient for each examination sample is used as the priority index for each examination sample. The higher the priority index, the greater the follow-up priority of the examination sample. Normalization is a technique well-known to those skilled in the art, and the normalization function can be linear normalization or standard normalization, etc. The specific normalization method is not limited here.

[0116] After obtaining the priority indicators of all examination sample data, patients can be followed up based on the priority indicators.

[0117] Preferably, in one embodiment of the present invention, patient follow-up includes:

[0118] When follow-up is required, all patients are classified according to their symptoms (such as cervical spondylosis, frozen shoulder, and lower back pain). For each symptom, the corresponding examination sample data are sorted in descending order according to the corresponding priority index to obtain a descending sequence. Finally, the patients corresponding to each symptom are followed up based on the order of the examination sample data in the descending sequence.

[0119] In summary, this invention, when determining patient follow-up priorities, incorporates factors such as data changes and treatment method variations during long-term treatment. This allows for more accurate determination of each patient's follow-up priority, contributing to the rational allocation of medical resources. First, examination sample data for each patient is collected, including acupoint parameters (prescription and number of acupoints), operational parameters (needle insertion duration, massage intensity and duration), and symptom severity scores for each examination. Given that the patient's condition changes as treatment progresses, the treatment plan also evolves. Therefore, this invention characterizes changes in the patient's condition based on these changes in the treatment plan. Specifically, it analyzes the trends, numerical characteristics, discrete and continuous variations of acupoint parameters in the examination sample data, determining a first stable data characteristic value to reflect the changing characteristics of acupoint selection during treatment. Then, through trend analysis and difference quantification of operational parameters, a second stable data characteristic value is determined to reflect the changing characteristics of the manipulation techniques during treatment. By integrating the first and second stable data features, the system more accurately reflects patients' treatment responses and recovery progress, and determines the follow-up priority adjustment coefficient for each examination sample, which is beneficial for guiding the adjustment and optimization of treatment plans. Finally, in the follow-up priority determination module, priority indicators are determined based on the follow-up priority adjustment coefficient and the disease severity score of the examination sample data, thereby enabling personalized follow-up tracking for all patients. This function ensures the efficient use of follow-up resources, allowing patients who require the most attention to receive timely follow-up services, thus improving the utilization rate of medical resources and patient satisfaction.

[0120] Please see Figure 5 This illustration shows a schematic diagram of a system structure for a follow-up tracking system for the therapeutic effect of micro-fire needle massage combined with rehabilitation, provided by an embodiment of the present invention. The system includes a processor 500, a memory 501, a bus 502, and a communication interface 503. The processor 500, communication interface 503, and memory 501 are connected via the bus 502. The memory 501 may include a high-speed random access memory, and the bus 502 may be an ISA bus, PCI bus, or EISA bus, etc. The processor 500 may be an integrated circuit chip with signal processing capabilities. The memory 501 stores at least one instruction, at least one program, code set, or instruction set. When the processor loads and executes the at least one instruction, at least one program, code set, or instruction set, it implements the steps in the aforementioned modules.

[0121] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0122] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0123] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A follow-up tracking system for the therapeutic effects of micro-fire needle massage combined with rehabilitation, characterized in that, The system includes: The data acquisition module is used to acquire examination sample data for each patient. The examination sample data includes acupoint parameters, operation parameters, and disease severity scores for each examination. The acupoint parameters include acupoint prescriptions and the number of acupoints in each prescription. The operation parameters include acupuncture duration, massage duration, and massage intensity. The follow-up priority adjustment coefficient determination module is used to analyze the changing trends and numerical characteristics of acupoint parameters in the examination sample data of each patient, and combine the discrete and continuous changes of acupoint parameters to determine the first data stability characteristic value of each examination sample data; determine the second data stability characteristic value of each examination sample data based on the changing trends and differences between operating parameters; and fuse the first data stability characteristic value and the second data stability characteristic value to determine the follow-up priority adjustment coefficient of each examination sample data. The follow-up priority determination module is used to determine priority indicators based on the disease severity score and follow-up priority adjustment coefficient of each examination sample data, so as to follow up and track patients. The method for obtaining the first stable feature value of the data includes: Select one patient's examination sample data as the test sample data, and determine the macro-relieving factor of the test sample data based on the changing trend and numerical characteristics of the number of acupoints in the test sample data. In the sample data to be tested, the discrete changes of acupoint parameters are analyzed to determine the data complexity factor of the sample data to be tested. Based on the continuous changes in acupoint parameters in the sample data to be tested, the scheme change factor of the sample data to be tested is determined. The product of the data complexity factor and the scheme change factor is normalized by negative correlation mapping, and the value is used as the micro-mitigation factor of the sample data to be tested. The normalized value of the product of the macro-level mitigation factor and the micro-level mitigation factor of the test sample data is used as the first data stability feature value of the test sample data. The methods for obtaining the second stable feature value of the data include: In each examination sample data, the operation parameters at each examination are dimensionality reduced to obtain treatment indicators, and the treatment indicators are arranged according to the time sequence of the examination to obtain an arrangement sequence; In each examination sample data, based on the correspondence between acupoint prescriptions and treatment indicators, the permutation sequence is divided according to the subsequences corresponding to the acupoint prescriptions to obtain all treatment indicator sequences; In each treatment indicator sequence, all treatment indicators are fitted with a straight line according to the examination time sequence based on the least squares method, and the slope value corresponding to the fitted line is normalized and used as the treatment degree change coefficient. In each treatment indicator sequence, the normalized value of the difference between the last treatment indicator and the first treatment indicator is used as the treatment degree change value. In all treatment indicator sequences, the change values ​​of treatment degree are weighted and averaged based on the coefficient of change of treatment degree to obtain the change characteristic value; The value obtained by negatively correlated mapping and normalization of the changed feature values ​​is used as the second data stability feature value for each examined sample data. The acupoint prescriptions in each of the subsequences are the same and have temporal continuity.

2. The follow-up tracking system for the therapeutic effect of micro-fire needle massage combined with rehabilitation according to claim 1, characterized in that, The methods for obtaining the macro-level mitigation factors include: In the sample data to be tested, the number of acupoints corresponding to all acupoint prescriptions was fitted with a straight line according to the examination time sequence based on the least squares method, and the slope value of the fitted line was normalized and used as the parameter for the change in the number of acupoints. The macroscopic relief factor of the test sample data is obtained by negatively mapping the product of the acupoint number change parameter and the maximum acupoint number and normalizing it.

3. The follow-up tracking system for the therapeutic effect of micro-fire needle massage combined with rehabilitation according to claim 1, characterized in that, The method for obtaining the data complexity factor includes: In the sample data to be tested, acupoint prescriptions with the same prescriptions are grouped together to obtain all types of acupoint prescriptions; All types of acupoint prescriptions are paired up to obtain all unique combinations; In each combination, the union and intersection of acupoints between the two acupoint prescriptions are obtained, and the difference between the number of acupoints in the union and the number of acupoints in the intersection is used as the distinguishing factor between the two acupoint prescriptions. The normalized value of the product of the mean of the distinguishing factors corresponding to all combinations and the number of types of acupoint prescriptions is used as the data complexity factor of the sample data to be tested.

4. A follow-up tracking system for the therapeutic effect of micro-fire needle massage combined with rehabilitation according to claim 1, characterized in that, The method for obtaining the scheme change factor includes: In the sample data to be tested, all acupoint prescriptions are arranged according to the time sequence of the examination to obtain a sorted sequence; In the sorting sequence, adjacent acupoint prescriptions are compared and merged to obtain all subsequences; The ratio of the number of subsequences to the total number of acupoint prescriptions in the sample data to be tested is used as the scheme change coefficient; In the sorting sequence, the mean number of acupoints in the acupoint prescription in each subsequence is used as the quantitative feature value of each subsequence, the absolute value of the difference between the quantitative feature values ​​of each two adjacent subsequences is used as the difference factor between each two adjacent subsequences, the sum of the acupoint prescriptions in each two adjacent subsequences is used as the stationarity factor, and the ratio of the difference factor to the stationarity factor is used as the scheme change factor between each two adjacent subsequences. In the sorted sequence, the product of the sum of the scheme change factors among all subsequences and the scheme change coefficient is normalized and used as the scheme change factor of the sample data to be tested.

5. A follow-up tracking system for the therapeutic effect of micro-fire needle massage combined with rehabilitation according to claim 4, characterized in that, The method for obtaining the subsequence includes: In the sorting sequence, the first acupoint prescription is used as the first prescription to be tested and the traversal begins. Each prescription to be tested corresponds to a set. If the last acupoint prescription in the set corresponding to the prescription to be tested is the same as the next adjacent acupoint prescription, then the next adjacent acupoint prescription is added to the set corresponding to the prescription to be tested and the traversal continues. If the last acupoint prescription in the set corresponding to the prescription to be tested is different from the next adjacent acupoint prescription, then the set corresponding to the prescription to be tested is taken as a subsequence, and the next adjacent acupoint prescription is taken as a new prescription to be tested and the traversal continues. After traversing all subsequences, you will obtain all the subsequences.

6. A follow-up tracking system for the therapeutic effect of micro-fire needle massage combined with rehabilitation according to claim 1, characterized in that, The method for obtaining the follow-up priority adjustment coefficient includes: The sum of the first and second stable eigenvalues ​​of each examination sample data is negatively correlated and normalized, and this value is used as the follow-up priority adjustment coefficient for each examination sample data.

7. A follow-up tracking system for the therapeutic effect of micro-fire needle massage combined with rehabilitation according to claim 1, characterized in that, The method for determining the priority indicators includes: The normalized value of the product of the disease severity score at the last examination in each examination sample and the follow-up priority adjustment coefficient of each examination sample is used as the priority indicator for each examination sample.

8. A follow-up tracking system for the therapeutic effect of micro-fire needle massage combined with rehabilitation according to claim 1, characterized in that, The follow-up and tracking of patients includes: When follow-up is required, all patients are classified according to their symptoms. Within each symptom category, the corresponding examination sample data are sorted in descending order according to the corresponding priority index to obtain a descending sequence. Within each symptom category, the corresponding patients are followed up based on the order of the examination sample data in the descending sequence.

Images