Ankylosing spondylitis condition monitoring management method and system
By obtaining the current and historical condition monitoring results of patients with ankylosing spondylitis, arranging them in time series and deducing and processing them, the problem of failure to fully consider the development of the disease in the existing technology is solved, and accurate monitoring and early warning of the condition is achieved, and the treatment effect is improved.
Patent Information
- Application Number
- CN202510625471.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-05-15
- Publication Date
- 2025-08-12
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
The existing monitoring of ankylosing spondylitis only focuses on the current time point and fails to fully consider the development of the disease, resulting in insufficient assessment of treatment effects and predicting disease progression.
By obtaining the current and historical disease monitoring results, arranging historical monitoring results in time series, data deduction and processing are carried out, abnormal time nodes and regulatory parameters are determined, and accurate monitoring and early warning of the disease are achieved.
It provides a comprehensive data foundation, clearly grasps the dynamic changes in the disease, predicts future disease trends, adjusts treatment plans in a timely manner, and improves the efficiency and accuracy of disease control.
Smart Images
Figure CN120473151A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the technical field of medical health, and in particular to a method and system for monitoring and managing ankylosing spondylitis. Background Art
[0002] Ankylosing spondylitis (AS) is a chronic inflammatory rheumatic disease that primarily affects the axial skeleton. Characteristic manifestations include ligamentous osteophyte formation and spinal ankylosing, which lead to altered posture, limited movement, and a significant decrease in quality of life. AS not only affects the spine but also frequently involves the sacroiliac joints, causing inflammation and pain, leading to joint stiffness and fusion over time.
[0003] Currently, ankylosing spondylitis (AS) disease monitoring is typically managed and monitored using a testing instrument, a patient client, a database, a processor, and a doctor client. The testing instrument measures the CRP and / or ESR levels of AS patients. The patient client allows patients to submit real-time physical condition information, feeds this information back to the processor, and receives abnormality handling suggestions or health maintenance reminders from the processor based on the physical condition information. The database stores each patient's submitted physical condition information, the ASDAS value calculated by the processor based on each physical condition information, as well as health maintenance reminders and abnormality handling suggestions. The processor receives the physical condition information submitted by the patient client, processes it to generate an ASDAS value, and then compares the current ASDAS value with the previous ASDAS value stored in the database. The doctor client receives the ASDAS value and abnormality report sent by the processor and manually determines whether to contact the patient directly. This reduces the burden on specialists and improves the efficiency of disease monitoring.
[0004] However, current disease monitoring is limited to the current time period. For ankylosing spondylitis, a chronic, progressive disease, simply focusing on the current time point is far from enough. Disease progression is often a continuous process, and changes in the disease between different time points are crucial for evaluating treatment efficacy, predicting disease progression, and formulating and adjusting treatment plans. Summary of the Invention
[0005] In order to solve at least one of the above technical problems, the present application provides an ankylosing spondylitis condition monitoring and management method and system.
[0006] In a first aspect, the present application provides a method for monitoring and managing ankylosing spondylitis, which adopts the following technical solutions: Obtain the disease monitoring results of ankylosing spondylitis patients in the current time period and the historical monitoring results in the historical period; Determine whether the condition monitoring results contain any uncontrollable abnormalities; if not, arrange the historical monitoring results in a time series to obtain condition indicator development data; Deducing the disease indicator development data to obtain indicator trend data in a future time period; Processing the indicator trend data to obtain an indicator arrangement ratio and indicator arrangement data corresponding to the indicator arrangement ratio; Determine whether the indicator arrangement ratio exceeds a preset arrangement ratio. If so, determine the abnormal time node corresponding to the indicator arrangement ratio based on the indicator trend data record, and match the indicator arrangement data with a preset disease control standard to obtain an abnormal control parameter corresponding to the indicator arrangement data; Control and display the abnormal time node and the abnormal control parameters.
[0007] By employing the above technical solution, we can obtain both the current monitoring results for ankylosing spondylitis patients and historical monitoring results from historical time periods, providing a comprehensive data foundation for subsequent analysis. Current monitoring results reflect the patient's immediate condition, while historical monitoring results illustrate the trajectory of the disease. Combining these two data sets allows for a clear understanding of the dynamics of the disease, helping to identify potential patterns and issues. This provides an accurate basis for subsequent key steps such as determining whether the disease is controllable and deducing indicator trends, making the entire disease analysis and treatment process more targeted and scientific. The monitoring results are then evaluated for any uncontrollable anomalies. If not, the historical monitoring results are arranged in a time series to generate disease indicator development data. This eliminates emergencies and allows subsequent analysis to proceed within a relatively stable disease framework. Arranging historical monitoring results in a time series format visually demonstrates the changing trends of disease indicators over time, providing a clear framework for subsequent deductions. This resulting disease indicator development data lays a solid foundation for accurately deducing future indicator trends, helping to proactively understand disease development trends. Deducing this disease indicator development data yields indicator trend data for future time periods. Based on the previously compiled data on the development of disease indicators, scientific deduction methods can be used to predict the future direction of disease indicators. This fully considers the continuity and regularity of disease progression, making the predictions more reliable. The resulting data on future indicator trends provides critical information for preemptive response planning, helping doctors and patients prepare in advance, adjust treatment plans promptly, and effectively address potential changes in their condition. Data processing of the indicator trend data yields indicator distribution ratios and corresponding indicator distribution data. This data processing further mines key information from the indicator trend data. The indicator distribution ratios intuitively reflect the proportions of different indicators over future time periods, while the indicator distribution data provides a detailed overview of the specific distribution of each indicator. This information provides a deeper understanding of disease development trends and the impact of different indicators on the condition. It also provides clear data support for subsequent determination of abnormal indicator distribution ratios and the identification of abnormal control parameters. The indicator distribution ratio is determined to determine whether it exceeds the preset distribution ratio. If so, the abnormal time point is determined based on the indicator trend data. The indicator distribution data is then matched with the preset disease control standards to determine the abnormal control parameters. This enables precise monitoring and early warning of disease progression. When indicator ratios are abnormal, the abnormal time point can be promptly located, clarifying the timeframe within which the problem occurred. Matching the abnormality control parameters with pre-set disease control standards provides clear guidance for targeted adjustments to treatment plans, helping to quickly implement effective measures when disease abnormalities occur, control disease progression, and improve treatment outcomes. The control displays abnormal time points and abnormal control parameters. This clearly displays key information from the previous analysis, allowing doctors and patients to intuitively understand when abnormalities occurred and the corresponding control measures.The display of abnormal time points helps identify the optimal time for intervention, while the presentation of abnormal control parameters provides clear guidance for developing specific treatment plans. This intuitive display facilitates timely decision-making, improves the efficiency and accuracy of disease control, and ultimately leads to better management of ankylosing spondylitis patients.
[0008] In one possible implementation, the deducing of the disease indicator development data to obtain indicator trend data in a future time period includes: Decomposing the disease progression data to obtain disease index data of each type of parameter in the disease progression data and a disease time series length corresponding to the disease index data; Arrange the disease index data in time series according to the disease time series length to obtain a disease data development set corresponding to each type of parameter; Perform basic distribution exploration and analysis on the disease data development set to obtain the disease periodicity law of each type of parameter at different time nodes; Based on the periodicity of the disease, the data sequence in the disease data development set is extended to obtain the disease data range at different time nodes in the future time period; The disease data range is sorted in time series to obtain a future disease condition set corresponding to each type of parameter; The future disease condition set is aggregated to obtain indicator trend data in a future time period.
[0009] In one possible implementation, processing the indicator trend data to obtain the indicator arrangement ratio and the indicator arrangement data corresponding to the indicator arrangement ratio includes: Arrange the indicator trend data in a matrix according to the time series to obtain a data set matrix; Extracting the time condition data corresponding to each time node in the data set matrix; Extracting and combining the ranges of condition data corresponding to the different types of parameters in the time condition data to obtain a plurality of condition data groups; Conduct a controllable disease risk assessment on each disease data group to obtain a disease risk score corresponding to each disease data group; Classifying multiple disease condition data groups based on the disease condition risk scores to obtain disease condition data categories; Calculating the ratio of the number of condition data groups corresponding to the condition data category to the total number of the plurality of condition data groups, and obtaining the indicator arrangement ratio corresponding to each condition data category; The condition data group is correspondingly bound to the indicator arrangement ratio to obtain indicator arrangement data corresponding to the indicator arrangement ratio.
[0010] In a possible implementation, the data ranges of the condition data corresponding to the different types of parameters in the time condition data are extracted and combined to obtain multiple condition data groups, including: Determine the number of data bits in the condition data range, and combine them one by one according to the number of data bits, until each data bit is combined with each data bit in the condition data range corresponding to the other types of the parameter, terminate the data bit combination operation, and obtain multiple condition data groups, wherein the data bit number includes the first condition data, the last condition data, and the median condition data between the first condition data and the last condition data in the condition data range.
[0011] In one possible implementation, performing a disease controllable risk assessment on each disease data group to obtain a disease risk score corresponding to each disease data group includes: Collect patient identity information and daily information; Matching the patient identity information and the patient daily information with the patient identity information and the patient daily information in a preset risk assessment model library to obtain a target risk assessment model; Each of the condition data groups is input into the target risk assessment model for assessment to obtain a condition risk score corresponding to each condition data group.
[0012] In a possible implementation, the classifying of the plurality of condition data groups based on the condition risk scores to obtain condition data categories includes: Determine a preset risk interval standard based on the patient's identity information and the patient's daily information; Dividing the disease risk score into intervals according to the preset risk interval standard to obtain multiple risk score intervals; According to the correspondence between the disease risk scores and the multiple disease data groups, the multiple disease data groups are correspondingly bound to the multiple risk score intervals to obtain a set of disease data groups corresponding to each risk score interval; The disease condition data set is divided into risk categories according to the risk score interval to obtain disease condition data categories.
[0013] In a second aspect, the present application provides an ankylosing spondylitis condition monitoring and management system, which adopts the following technical solutions: An ankylosing spondylitis condition monitoring and management system, comprising: An information acquisition module is used to obtain the condition monitoring results of ankylosing spondylitis patients in the current time period and the historical monitoring results in the historical period; A data sorting module is used to determine whether the disease monitoring results contain uncontrollable abnormalities. If not, the historical monitoring results are arranged in time series to obtain disease indicator development data. A data deduction module is used to deduce the disease indicator development data to obtain indicator trend data in a future time period; A data processing module, configured to process the indicator trend data to obtain an indicator arrangement ratio and indicator arrangement data corresponding to the indicator arrangement ratio; a parameter matching module, configured to determine whether the indicator arrangement ratio exceeds a preset arrangement ratio; if so, determine an abnormal time node corresponding to the indicator arrangement ratio based on the indicator trend data record, and match the indicator arrangement data with a preset disease control standard to obtain an abnormal control parameter corresponding to the indicator arrangement data; The control display module is used to control and display the abnormal time node and the abnormal regulation parameter.
[0014] In one possible implementation, when the data deduction module deduces the disease indicator development data to obtain indicator trend data in a future time period, it is specifically configured to: Decomposing the disease progression data to obtain disease index data of each type of parameter in the disease progression data and a disease time series length corresponding to the disease index data; Arrange the disease index data in time series according to the disease time series length to obtain a disease data development set corresponding to each type of parameter; Perform basic distribution exploration and analysis on the disease data development set to obtain the disease periodicity law of each type of parameter at different time nodes; Based on the periodicity of the disease, the data sequence in the disease data development set is extended to obtain the disease data range at different time nodes in the future time period; The disease data range is sorted in time series to obtain a future disease condition set corresponding to each type of parameter; The future disease condition set is aggregated to obtain indicator trend data in a future time period.
[0015] In another possible implementation, when the data processing module processes the indicator trend data to obtain the indicator arrangement ratio and the indicator arrangement data corresponding to the indicator arrangement ratio, it is specifically configured to: Arrange the indicator trend data in a matrix according to the time series to obtain a data set matrix; Extracting the time condition data corresponding to each time node in the data set matrix; Extracting and combining the ranges of condition data corresponding to the different types of parameters in the time condition data to obtain a plurality of condition data groups; Conduct a controllable disease risk assessment on each disease data group to obtain a disease risk score corresponding to each disease data group; Classifying multiple disease condition data groups based on the disease condition risk scores to obtain disease condition data categories; Calculating the ratio of the number of condition data groups corresponding to the condition data category to the total number of the plurality of condition data groups, and obtaining the indicator arrangement ratio corresponding to each condition data category; The condition data group is correspondingly bound to the indicator arrangement ratio to obtain indicator arrangement data corresponding to the indicator arrangement ratio.
[0016] In another possible implementation, when the data processing module extracts and combines the condition data ranges corresponding to different types of parameters in the time condition data to obtain multiple condition data groups, it is specifically configured to: Determine the number of data bits in the condition data range, and combine them one by one according to the number of data bits, until each data bit is combined with each data bit in the condition data range corresponding to the other types of the parameter, terminate the data bit combination operation, and obtain multiple condition data groups, wherein the data bit number includes the first condition data, the last condition data, and the median condition data between the first condition data and the last condition data in the condition data range.
[0017] In another possible implementation, when the data processing module performs a disease controllable risk assessment on each disease data group and obtains a disease risk score corresponding to each disease data group, it is specifically configured to: Collect patient identity information and daily information; Matching the patient identity information and the patient daily information with the patient identity information and the patient daily information in a preset risk assessment model library to obtain a target risk assessment model; Each of the condition data groups is input into the target risk assessment model for assessment to obtain a condition risk score corresponding to each condition data group.
[0018] In another possible implementation, when the data processing module classifies the multiple disease data groups based on the disease risk score to obtain the disease data category, it is specifically configured to: Determine a preset risk interval standard based on the patient's identity information and the patient's daily information; Dividing the disease risk score into intervals according to the preset risk interval standard to obtain multiple risk score intervals; According to the correspondence between the disease risk scores and the multiple disease data groups, the multiple disease data groups are correspondingly bound to the multiple risk score intervals to obtain a set of disease data groups corresponding to each risk score interval; The disease condition data set is divided into risk categories according to the risk score interval to obtain disease condition data categories.
[0019] In a third aspect, the present application provides an electronic device, which adopts the following technical solution: at least one processor; Memory; At least one application, wherein the at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute a method for monitoring and managing ankylosing spondylitis as described in any one of the first aspects.
[0020] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium stores a computer program thereon. When the computer program is executed in a computer, the computer is caused to execute a method for monitoring and managing ankylosing spondylitis as described in any one of the first aspects.
[0021] In summary, this application includes at least one of the following beneficial technical effects: By employing the above technical solution, we can obtain both the current monitoring results for ankylosing spondylitis patients and historical monitoring results from historical time periods, providing a comprehensive data foundation for subsequent analysis. Current monitoring results reflect the patient's immediate condition, while historical monitoring results illustrate the trajectory of the disease. Combining these two data sets allows for a clear understanding of the dynamics of the disease, helping to identify potential patterns and issues. This provides an accurate basis for subsequent key steps such as determining whether the disease is controllable and deducing indicator trends, making the entire disease analysis and treatment process more targeted and scientific. The monitoring results are then evaluated for any uncontrollable anomalies. If not, the historical monitoring results are arranged in a time series to generate disease indicator development data. This eliminates emergencies and allows subsequent analysis to proceed within a relatively stable disease framework. Arranging historical monitoring results in a time series format visually demonstrates the changing trends of disease indicators over time, providing a clear framework for subsequent deductions. This resulting disease indicator development data lays a solid foundation for accurately deducing future indicator trends, helping to proactively understand disease development trends. Deducing this disease indicator development data yields indicator trend data for future time periods. Based on the previously compiled data on the development of disease indicators, scientific deduction methods can be used to predict the future direction of disease indicators. This fully considers the continuity and regularity of disease progression, making the predictions more reliable. The resulting data on future indicator trends provides critical information for preemptive response planning, helping doctors and patients prepare in advance, adjust treatment plans promptly, and effectively address potential changes in their condition. Data processing of the indicator trend data yields indicator distribution ratios and corresponding indicator distribution data. This data processing further mines key information from the indicator trend data. The indicator distribution ratios intuitively reflect the proportions of different indicators over future time periods, while the indicator distribution data provides a detailed overview of the specific distribution of each indicator. This information provides a deeper understanding of disease development trends and the impact of different indicators on the condition. It also provides clear data support for subsequent determination of abnormal indicator distribution ratios and the identification of abnormal control parameters. The indicator distribution ratio is determined to determine whether it exceeds the preset distribution ratio. If so, the abnormal time point is determined based on the indicator trend data. The indicator distribution data is then matched with the preset disease control standards to determine the abnormal control parameters. This enables precise monitoring and early warning of disease progression. When indicator ratios are abnormal, the abnormal time point can be promptly located, clarifying the timeframe within which the problem occurred. Matching the abnormality control parameters with pre-set disease control standards provides clear guidance for targeted adjustments to treatment plans, helping to quickly implement effective measures when disease abnormalities occur, control disease progression, and improve treatment outcomes. The control displays abnormal time points and abnormal control parameters. This clearly displays key information from the previous analysis, allowing doctors and patients to intuitively understand when abnormalities occurred and the corresponding control measures.The display of abnormal time points helps identify the optimal time for intervention, while the presentation of abnormal control parameters provides clear guidance for developing specific treatment plans. This intuitive display facilitates timely decision-making, improves the efficiency and accuracy of disease control, and ultimately leads to better management of ankylosing spondylitis patients. BRIEF DESCRIPTION OF THE DRAWINGS
[0022] Figure 1 A flowchart of a method for monitoring and managing ankylosing spondylitis provided in an embodiment of the present application.
[0023] Figure 2 This is a structural diagram of an ankylosing spondylitis disease monitoring and management system provided in an embodiment of the present application.
[0024] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION
[0025] The following is combined with Figure 1-3 This application is described in further detail.
[0026] This specific embodiment is merely an explanation of the present application and is not a limitation of the present application. After reading this specification, those skilled in the art may make non-creative modifications to the present embodiment as needed, but as long as they are within the scope of the present application, they are protected by patent law.
[0027] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.
[0028] In this document, the term "and / or" simply describes a relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this document, unless otherwise specified, generally indicates an "or" relationship between the related objects.
[0029] The embodiments of the present application are described in further detail below with reference to the accompanying drawings.
[0030] The embodiment of the present application provides a method for monitoring and managing ankylosing spondylitis, which is executed by an electronic device, wherein the electronic device can be an independent physical electronic device, or an electronic device cluster or distributed system composed of multiple physical electronic devices, or a cloud electronic device that provides cloud computing services. The embodiment of the present application is not limited here, such as Figure 1 As shown, the method includes: Step S10: Acquire the condition monitoring results of the ankylosing spondylitis patient in the current time period and the historical monitoring results in the historical period time period.
[0031] Specifically, the current time period refers to the time range currently being considered or evaluated, which can be a few minutes, hours, a day, a week, or any other defined time period, depending on the frequency and needs of monitoring. The disease monitoring results refer to the data obtained through a series of medical examinations, tests, or evaluations. These data reflect the condition of ankylosing spondylitis patients during a specific time period, including pain levels, joint mobility, inflammation indicators, etc. The historical cycle time period refers to a period of time that the patient has experienced before. This period can be weeks, months, or years, and is used to compare with the monitoring results of the current time period to evaluate the changing trend of the disease. The historical monitoring results refer to a series of data records obtained by monitoring the patient's condition during the historical cycle time period. These data are important for evaluating the development of the disease, the effectiveness of treatment, and predicting possible future changes in the disease.
[0032] Step S11: determine whether there is any uncontrollable abnormality in the disease monitoring results. If not, arrange the historical monitoring results in time series to obtain disease indicator development data.
[0033] Specifically, uncontrollable abnormalities refer to abnormalities in disease monitoring results that are beyond the doctor's expectations or the normal range. These abnormalities indicate that the patient's condition is worsening or that treatment is ineffective, requiring timely intervention by the doctor to adjust the patient's current treatment plan. A time series is a series of data points arranged in chronological order, used to represent the changes in a variable (such as a disease indicator) over time. Disease indicator development data refers to a data set obtained by arranging historical monitoring results in a time series, which is used to intuitively display the changing trends of patient disease indicators over time.
[0034] In an embodiment of the present application, the electronic device determines the relationship between the critical value of each monitoring parameter in the disease monitoring result and the preset parameter critical value, determines the current monitoring parameter range based on the critical value, determines the preset parameter range based on the preset parameter critical value, and when the monitoring parameter range exceeds the preset parameter range, determines whether there is an uncontrollable abnormality in the current disease monitoring result based on the proportion of data exceeding the range.
[0035] Step S12: deduce the disease indicator development data to obtain indicator trend data in a future time period.
[0036] Specifically, the disease progression data is decomposed to obtain the disease index data of each type of parameter in the disease progression data and the disease time series length corresponding to the disease index data. The disease index data is time-series sorted according to the disease time series length to obtain the disease data development set corresponding to each type of parameter. The disease data development set is subjected to basic distribution exploration and analysis to obtain the disease periodicity law of each type of parameter at different time nodes. Based on the disease periodicity law, the data sequence in the disease data development set is extended to obtain the disease data range at different time nodes in the future time period. The disease data range is time-series sorted to obtain the future disease set corresponding to each type of parameter. The future disease set is summarized to obtain the indicator trend data in the future time period.
[0037] Step S13: Process the indicator trend data to obtain the indicator arrangement ratio and the indicator arrangement data corresponding to the indicator arrangement ratio.
[0038] Specifically, the indicator trend data is arranged in a matrix according to the time series to obtain a data set matrix, and the time condition data corresponding to each time node in the data set matrix is extracted. The condition data range corresponding to different types of parameters in the time condition data is extracted and combined to obtain multiple condition data groups, and the controllable condition risk is evaluated for each condition data group to obtain a condition risk score corresponding to each condition data group. Based on the condition risk score, the multiple condition data groups are classified to obtain condition data categories. The ratio of the number of condition data groups corresponding to the condition data category to the total number of multiple condition data groups is calculated to obtain the indicator arrangement ratio corresponding to each condition data category, and the condition data group is correspondingly bound to the indicator arrangement ratio to obtain the indicator arrangement data corresponding to the indicator arrangement ratio.
[0039] Specifically, the number of data bits in the disease data range is determined, and the data bits are combined one by one until each data bit is combined with each data bit in the disease data range corresponding to the other types of the parameter, and then the data bit combination operation is terminated to obtain multiple disease data groups, where the data bit number includes the first disease data, the last disease data, and the median disease data between the first disease data and the last disease data in the disease data range.
[0040] For example, the current patient has three different types of condition data ranges, each containing multiple different condition data. The location of each condition data is serially marked to form the first condition data, the second condition data, the third condition data, and so on. The first condition data in the first type of condition data range is combined with the first condition data in the second type of condition data range and the first condition data in the third type of condition data range to obtain the first condition data group. Then, the first condition data in the first type of condition data range is combined with the first condition data in the second type of condition data range and the second condition data in the third type of condition data range to obtain the second condition data group. This process is repeated until the number of data bits in one type of condition data range is combined with the number of data bits in the remaining types of condition data ranges, and then this process is stopped, thereby obtaining multiple condition data groups.
[0041] Specifically, patient identity information and daily information are collected and matched with those in a pre-set risk assessment model library to generate a target risk assessment model. Each condition data set is then input into the target risk assessment model for evaluation, resulting in a corresponding condition risk score.
[0042] Specifically, a preset risk interval standard is determined based on the patient's identity information and daily information. The condition risk score is divided into intervals according to the preset risk interval standard to obtain multiple risk score intervals. Based on the correspondence between the condition risk score and multiple condition data groups, the multiple condition data groups are correspondingly bound to the multiple risk score intervals to obtain a set of condition data groups corresponding to each risk score interval. The set of condition data groups is divided into risk categories according to the risk score intervals to obtain condition data categories.
[0043] Step S14: determine whether the indicator arrangement ratio exceeds the preset arrangement ratio. If it exceeds, determine the abnormal time node corresponding to the indicator arrangement ratio based on the indicator trend data record, and match the indicator arrangement data with the preset disease control standard to obtain the abnormal control parameters corresponding to the indicator arrangement data.
[0044] Specifically, the preset arrangement ratio refers to a threshold value of an indicator arrangement ratio that is pre-set based on historical data, industry standards or expert experience, and is used to determine whether the current indicator arrangement is within the normal range. Indicator trend data records refer to indicator value data recorded continuously over time. These data constitute the change trajectory of the indicator and are used to analyze the trend and anomalies of the indicator. The abnormal time node refers to the time point in the indicator trend data when the indicator value begins to deviate significantly from the normal range or the preset standard, marking the occurrence of an abnormal situation. The preset disease control standard refers to the control target and standard set for a specific disease based on medical knowledge, clinical experience or treatment guidelines, which is used to evaluate the treatment effect and regulation plan of the disease. The abnormal regulation parameter refers to the parameter on which the treatment plan or monitoring measures are adjusted according to the abnormal situation and treatment needs when the indicator arrangement ratio exceeds the preset value and does not match the preset disease control standard.
[0045] In an embodiment of the present application, first, the system calculates the indicator arrangement ratio within the current time period and compares it with the preset arrangement ratio. If the indicator arrangement ratio exceeds the preset value, it means that the change in the indicator may be beyond the normal range and requires further analysis. Then, the system will determine the time node when the abnormality begins to appear, that is, the abnormal time node, based on the indicator trend data record. This node is the key to identifying abnormal situations and helps to trace the cause of the abnormality and take corresponding measures. Finally, the system will match the indicator arrangement data with the preset disease control standards to evaluate whether the current disease or system status meets the control standards. If not, the system will generate abnormal control parameters corresponding to the indicator arrangement data based on the matching results to guide subsequent treatment or control plans.
[0046] Step S15: Control and display abnormal time nodes and abnormal control parameters.
[0047] The present invention provides a method for monitoring and managing ankylosing spondylitis (AS). This method obtains the patient's condition monitoring results for the current time period and historical monitoring results for historical time periods, providing a comprehensive data foundation for subsequent analysis. Current monitoring results reflect the patient's immediate condition, while historical monitoring results illustrate the trajectory of the disease. Combining these two methods allows for a clear understanding of the dynamic changes in the disease, helping to identify underlying patterns and issues. This provides an accurate basis for subsequent key steps such as determining whether the disease is controllable and deducing the direction of disease indicators, making the entire disease analysis and treatment process more targeted and scientific. The condition monitoring results are then determined to contain uncontrollable anomalies. If not, the historical monitoring results are arranged in a time series to obtain disease indicator development data. This eliminates emergency and uncontrollable situations, allowing subsequent analysis to be conducted within a relatively stable disease framework. Arranging the historical monitoring results in a time series allows for a visual presentation of the changing trends of disease indicators over time, providing a clear framework for subsequent deductions. The resulting disease indicator development data lays a solid foundation for accurately deducing future indicator trends and helps to understand the disease's development trends in advance. Deducing the disease indicator development data yields indicator trend data for future time periods. Based on the previously compiled data on the development of disease indicators, scientific deduction methods can be used to predict the future direction of disease indicators. This fully considers the continuity and regularity of disease progression, making the predictions more reliable. The resulting data on future indicator trends provides critical information for preemptive response planning, helping doctors and patients prepare in advance, adjust treatment plans promptly, and effectively address potential changes in their condition. Data processing of the indicator trend data yields indicator distribution ratios and corresponding indicator distribution data. This data processing further mines key information from the indicator trend data. The indicator distribution ratios intuitively reflect the proportions of different indicators over future time periods, while the indicator distribution data provides a detailed overview of the specific distribution of each indicator. This information provides a deeper understanding of disease development trends and the impact of different indicators on the condition. It also provides clear data support for subsequent determination of abnormal indicator distribution ratios and the identification of abnormal control parameters. The indicator distribution ratio is determined to determine whether it exceeds the preset distribution ratio. If so, the abnormal time point is determined based on the indicator trend data. The indicator distribution data is then matched with the preset disease control standards to determine the abnormal control parameters. This enables precise monitoring and early warning of disease progression. When indicator ratios are abnormal, the abnormal time point can be promptly located, clarifying the timeframe within which the problem occurred. Matching the abnormality control parameters with pre-set disease control standards provides clear guidance for targeted adjustments to treatment plans, helping to quickly implement effective measures when disease abnormalities occur, control disease progression, and improve treatment outcomes. The control displays abnormal time points and abnormal control parameters. This clearly displays key information from the previous analysis, allowing doctors and patients to intuitively understand when abnormalities occurred and the corresponding control measures.The display of abnormal time points helps identify the optimal time for intervention, while the presentation of abnormal control parameters provides clear guidance for developing specific treatment plans. This intuitive display facilitates timely decision-making, improves the efficiency and accuracy of disease control, and ultimately leads to better management of ankylosing spondylitis patients.
[0048] The following is an introduction to an ankylosing spondylitis condition monitoring and management system provided by an embodiment of the present application. The ankylosing spondylitis condition monitoring and management system described below and the ankylosing spondylitis condition monitoring and management method described above can be referenced to each other. Please refer to Figure 2 , Figure 2 : is a structural diagram of an ankylosing spondylitis condition monitoring and management system 20 provided in an embodiment of the present application, including: The information acquisition module 21 is used to obtain the condition monitoring results of ankylosing spondylitis patients in the current time period and the historical monitoring results in the historical period; The data sorting module 22 is used to determine whether there are any uncontrollable abnormalities in the disease monitoring results. If not, the historical monitoring results are arranged in time series to obtain disease indicator development data; The data deduction module 23 is used to deduce the disease indicator development data to obtain the indicator trend data in the future time period; The data processing module 24 is used to process the indicator trend data to obtain the indicator arrangement ratio and the indicator arrangement data corresponding to the indicator arrangement ratio; The parameter matching module 25 is used to determine whether the indicator arrangement ratio exceeds the preset arrangement ratio. If so, the abnormal time node corresponding to the indicator arrangement ratio is determined based on the indicator trend data record, and the indicator arrangement data is matched with the preset disease control standard to obtain the abnormal control parameter corresponding to the indicator arrangement data; The control display module 26 is used to control and display abnormal time nodes and abnormal control parameters.
[0049] In one possible implementation of the embodiment of the present application, the data deduction module 23 is specifically configured to: Decomposing the disease progression data to obtain disease index data of each type of parameter in the disease progression data and the disease time series length corresponding to the disease index data; The disease index data are sorted in time series according to the disease time series length to obtain the disease data development set corresponding to each type of parameter; Conduct basic distribution exploration and analysis on the disease data development set to obtain the disease periodicity of each type of parameter at different time nodes; Based on the periodicity of the disease, the data sequence in the disease data development set is extended to obtain the disease data range at different time nodes in the future time period; The disease data range is sorted in time series to obtain the future disease condition set corresponding to each type of parameter; Summarize the future disease condition collection to obtain the indicator trend data in the future time period.
[0050] In one possible implementation of the embodiment of the present application, when the data processing module 24 processes the indicator trend data to obtain the indicator arrangement ratio and the indicator arrangement data corresponding to the indicator arrangement ratio, it is specifically configured to: Arrange the indicator trend data in a matrix according to the time series to obtain the data set matrix; Extract the time condition data corresponding to each time node in the data set matrix; Extracting and combining the disease data ranges corresponding to different types of parameters in the time disease data to obtain multiple disease data groups; Conduct a controllable disease risk assessment on each disease data group to obtain a disease risk score corresponding to each disease data group; Classify multiple disease condition data groups based on disease condition risk scores to obtain disease condition data categories; Calculate the ratio of the number of condition data groups corresponding to the condition data category to the total number of multiple condition data groups, and obtain the indicator arrangement ratio corresponding to each condition data category; The condition data group is bound to the indicator arrangement ratio to obtain the indicator arrangement data corresponding to the indicator arrangement ratio.
[0051] In one possible implementation of the embodiment of the present application, the data processing module 24 extracts and combines the disease data ranges corresponding to different types of parameters in the time disease data to obtain multiple disease data groups, specifically for: Determine the number of data bits in the disease data range, and combine them one by one according to the number of data bits, until each data bit is combined with each data bit in the disease data range corresponding to the other types of the parameter, terminate the data bit combination operation, and obtain multiple disease data groups, where the data bit number includes the first disease data, the last disease data, and the median disease data between the first disease data and the last disease data in the disease data range.
[0052] In one possible implementation of the embodiment of the present application, the data processing module 24 performs a disease controllable risk assessment on each disease data group to obtain a disease risk score corresponding to each disease data group, specifically for: Collect patient identity information and daily information; Matching the patient's identity information and daily information with the patient's identity information and daily information in a preset risk assessment model library to obtain a target risk assessment model; Each disease data group is input into the target risk assessment model for evaluation to obtain a disease risk score corresponding to each disease data group.
[0053] In one possible implementation of the embodiment of the present application, when the data processing module 24 classifies multiple disease data groups based on the disease risk score to obtain disease data categories, it is specifically configured to: Determine the preset risk interval standards based on the patient's identity information and daily information; Divide the disease risk score into intervals according to the preset risk interval standard to obtain multiple risk score intervals; According to the correspondence between the disease risk score and the multiple disease data groups, the multiple disease data groups are correspondingly bound to the multiple risk score intervals to obtain a set of disease data groups corresponding to each risk score interval; The disease condition data set is divided into risk categories according to the risk score interval to obtain the disease condition data category.
[0054] The present application embodiment provides an electronic device, such as Figure 3 As shown, Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 3 The electronic device 300 shown includes a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, for example, via a bus 302. Optionally, the electronic device 300 may further include a transceiver 304. It should be noted that in actual applications, the number of transceivers 304 is not limited to one, and the structure of the electronic device 300 does not constitute a limitation on the embodiments of the present application.
[0055] Processor 301 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It may implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the embodiments disclosed herein. Processor 301 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like.
[0056] The bus 302 may include a path for transmitting information between the above components. The bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 302 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.
[0057] The memory 303 may be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.
[0058] The memory 303 is used to store application code for executing the solution of the embodiment of the present application, and the execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 303 to implement the content shown in the above method embodiment.
[0059] Among them, electronic devices include but are not limited to: mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 3 The electronic device shown is merely an example and should not limit the functions and scope of use of the embodiments of the present application.
[0060] A computer-readable storage medium provided in an embodiment of the present application is introduced below. The computer-readable storage medium described below and the method described above can be referenced to each other.
[0061] An embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the above-mentioned ankylosing spondylitis condition monitoring and management method are implemented.
[0062] Since the embodiments of the computer-readable storage medium part and the embodiments of the method part correspond to each other, the embodiments of the computer-readable storage medium part refer to the description of the embodiments of the method part.
[0063] It should be understood that although the steps in the flowcharts of the accompanying drawings are shown in sequence as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in the flowcharts of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
[0064] The above are only some of the implementation methods of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.
Claims
1. A method for monitoring and managing ankylosing spondylitis, characterized in that: include: Obtain the disease monitoring results of ankylosing spondylitis patients in the current time period and the historical monitoring results in the historical period; Determine whether the condition monitoring results contain uncontrollable abnormalities. If not, arrange the historical monitoring results in a time series to obtain condition indicator development data. Deducing the disease indicator development data to obtain indicator trend data in a future time period; Processing the indicator trend data to obtain an indicator arrangement ratio and indicator arrangement data corresponding to the indicator arrangement ratio; Determine whether the indicator arrangement ratio exceeds a preset arrangement ratio. If so, determine the abnormal time node corresponding to the indicator arrangement ratio based on the indicator trend data record, and match the indicator arrangement data with a preset disease control standard to obtain an abnormal control parameter corresponding to the indicator arrangement data; Control and display the abnormal time node and the abnormal control parameters.
2. The ankylosing spondylitis condition monitoring and management method according to claim 1, characterized in that: The deduction of the disease indicator development data to obtain indicator trend data in a future time period includes: Decomposing the disease progression data to obtain disease index data of each type of parameter in the disease progression data and a disease time series length corresponding to the disease index data; Arrange the disease index data in time series according to the disease time series length to obtain a disease data development set corresponding to each type of parameter; Perform basic distribution exploration and analysis on the disease data development set to obtain the disease periodicity law of each type of parameter at different time nodes; Based on the periodicity of the disease, the data sequence in the disease data development set is extended to obtain the disease data range at different time nodes in the future time period; The disease data range is sorted in time series to obtain a future disease condition set corresponding to each type of parameter; The future disease condition set is aggregated to obtain indicator trend data in a future time period.
3. The method for monitoring and managing ankylosing spondylitis according to claim 1, characterized in that: The processing of the indicator trend data to obtain the indicator arrangement ratio and the indicator arrangement data corresponding to the indicator arrangement ratio includes: Arrange the indicator trend data in a matrix according to the time series to obtain a data set matrix; Extracting the time condition data corresponding to each time node in the data set matrix; Extracting and combining the ranges of condition data corresponding to the different types of parameters in the time condition data to obtain a plurality of condition data groups; Conduct a controllable disease risk assessment on each disease data group to obtain a disease risk score corresponding to each disease data group; Classifying multiple disease condition data groups based on the disease condition risk scores to obtain disease condition data categories; Calculating the ratio of the number of condition data groups corresponding to the condition data category to the total number of the plurality of condition data groups, and obtaining the indicator arrangement ratio corresponding to each condition data category; The condition data group is correspondingly bound to the indicator arrangement ratio to obtain indicator arrangement data corresponding to the indicator arrangement ratio.
4. The method for monitoring and managing ankylosing spondylitis according to claim 3, characterized in that: The data ranges of the condition data corresponding to the different types of parameters in the time condition data are extracted and combined to obtain multiple condition data groups, including: Determine the number of data bits in the condition data range, and combine them one by one according to the number of data bits, until each data bit is combined with each data bit in the condition data range corresponding to the other types of the parameter, terminate the data bit combination operation, and obtain multiple condition data groups, wherein the data bit number includes the first condition data, the last condition data, and the median condition data between the first condition data and the last condition data in the condition data range.
5. The method for monitoring and managing ankylosing spondylitis according to claim 3, characterized in that: The controllable risk assessment of each condition data group is performed to obtain a condition risk score corresponding to each condition data group, including: Collect patient identity information and daily information; Matching the patient identity information and the patient daily information with the patient identity information and the patient daily information in a preset risk assessment model library to obtain a target risk assessment model; Each of the condition data groups is input into the target risk assessment model for assessment to obtain a condition risk score corresponding to each condition data group.
6. The method for monitoring and managing ankylosing spondylitis according to claim 5, characterized in that: The method of classifying the plurality of condition data groups based on the condition risk scores to obtain condition data categories includes: Determine a preset risk interval standard based on the patient's identity information and the patient's daily information; Dividing the disease risk score into intervals according to the preset risk interval standard to obtain multiple risk score intervals; According to the correspondence between the disease risk scores and the multiple disease data groups, the multiple disease data groups are correspondingly bound to the multiple risk score intervals to obtain a set of disease data groups corresponding to each risk score interval; The disease condition data set is divided into risk categories according to the risk score interval to obtain disease condition data categories.
7. A system for monitoring and managing ankylosing spondylitis, characterized in that: include: An information acquisition module is used to obtain the condition monitoring results of ankylosing spondylitis patients in the current time period and the historical monitoring results in the historical period; A data sorting module is used to determine whether the disease monitoring results contain uncontrollable abnormalities. If not, the historical monitoring results are arranged in time series to obtain disease indicator development data. A data deduction module is used to deduce the disease indicator development data to obtain indicator trend data in a future time period; A data processing module, configured to process the indicator trend data to obtain an indicator arrangement ratio and indicator arrangement data corresponding to the indicator arrangement ratio; a parameter matching module, configured to determine whether the indicator arrangement ratio exceeds a preset arrangement ratio; if so, determine an abnormal time node corresponding to the indicator arrangement ratio based on the indicator trend data record, and match the indicator arrangement data with a preset disease control standard to obtain an abnormal control parameter corresponding to the indicator arrangement data; The control display module is used to control and display the abnormal time node and the abnormal regulation parameter.
8. The ankylosing spondylitis condition monitoring and management system according to claim 7, characterized in that: When the data deduction module deduces the disease indicator development data to obtain indicator trend data in a future time period, it is specifically used to: Decomposing the disease progression data to obtain disease index data of each type of parameter in the disease progression data and a disease time series length corresponding to the disease index data; Arrange the disease index data in time series according to the disease time series length to obtain a disease data development set corresponding to each type of parameter; Perform basic distribution exploration and analysis on the disease data development set to obtain the disease periodicity law of each type of parameter at different time nodes; Based on the periodicity of the disease, the data sequence in the disease data development set is extended to obtain the disease data range at different time nodes in the future time period; The disease data range is sorted in time series to obtain a future disease condition set corresponding to each type of parameter; The future disease condition set is aggregated to obtain indicator trend data in a future time period.
9. An electronic device, characterized in that: The electronic device includes: at least one processor; Memory; At least one application, wherein the at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute a method for monitoring and managing ankylosing spondylitis according to any one of claims 1-6.
10. A computer-readable storage medium, characterized in that include: The device stores a computer program that can be loaded by a processor and executes a method for monitoring and managing ankylosing spondylitis according to any one of claims 1 to 6.