A method and system for monitoring pain in patients for palliative care

Abstract

A method and system for monitoring patient pain used in palliative care, relating to the field of patient management. In this method, pain indexes of multiple target parts of the patient to be monitored are collected regularly; the pain index is divided into one type of parameter, two type of parameter and three type of parameter according to the cancer type of the patient to be monitored and the degree of influence of the pain part, the one type of parameter is the pain index of the part directly affected by the tumor, the two type of parameter is the pain index of the part indirectly affected by the tumor, and the three type of parameter is the pain index of the part unrelated to the tumor; the one type of parameter, the two type of parameter and the three type of parameter are combined to obtain a comprehensive pain index; a management plan is generated according to the comprehensive pain index. The implementation of the technical solution provided in this application achieves the effect of comprehensively reflecting the patient's pain condition.

Description

Technical Field

[0001] The present application relates to the technical field of patient management, and in particular to a method and system for monitoring patient pain applied to palliative care. Background Art

[0002] Palliative care, also known as hospice care, aims to provide comprehensive care for patients with incurable diseases at the end of their lives, including relief of physical pain, psychological support and spiritual comfort. With the aging of the population and people's emphasis on quality of life, the demand for palliative care is growing. For patients at the end of their lives, pain is one of the most common symptoms that seriously affects their quality of life. Effectively monitoring and managing patients' pain is crucial to improving their comfort and quality of life.

[0003] The existing technology usually uses a manual method to record the pain index, that is, the nursing staff regularly asks the patient and records the pain level. In addition, there are also some monitoring devices that are used to automatically collect the patient's physiological parameters through sensors, combined with the patient's self-report of pain, to assess their pain condition. However, when analyzing the pain index, the existing technology fails to take into account the differentiated effects of tumor types and different parts on pain, resulting in limited accuracy of the evaluation results, which cannot fully and accurately reflect the patient's pain condition.

[0004] Therefore, there is a need for a pain monitoring method that more comprehensively reflects the patient's pain condition. Summary of the invention

[0005] The present application provides a patient pain monitoring method and system for palliative care, which can accurately assess pain conditions and formulate personalized management plans.

[0006] In a first aspect of the present application, a method for monitoring patient pain applied to palliative care is provided, characterized in that the method is applied to a pain monitoring platform and comprises:

[0007] Regularly collect pain indexes of multiple target sites of the patient to be monitored;

[0008] According to the cancer type of the patient to be monitored and the degree of influence of the pain site, the pain index is divided into a first-class parameter, a second-class parameter and a third-class parameter. The first-class parameter is the pain index of the site directly affected by the tumor, the second-class parameter is the pain index of the site indirectly affected by the tumor, and the third-class parameter is the pain index of the site unrelated to the tumor.

[0009] The first type of parameters, the second type of parameters and the third type of parameters are integrated to obtain a comprehensive pain index;

[0010] A management plan is generated based on the comprehensive pain index.

[0011] By adopting the above technical solution, the pain index of multiple target parts of the patient is collected regularly, which can fully reflect the patient's pain condition and avoid the one-sidedness of single-part assessment. Dividing the pain index into one type of parameter, two type of parameter and three type of parameter can help to more accurately understand the source and impact of pain. Combining the pain index of different categories to obtain a comprehensive pain index provides a scientific basis for formulating personalized management plans. Through personalized management plans, the patient's mentality can be more effectively improved, negative emotions such as anxiety and depression can be reduced, and their psychological comfort can be improved. Comprehensive pain management and comfort care can also improve the quality of daily life of patients.

[0012] Optionally, the pain index is divided into first-class parameter, second-class parameter and third-class parameter according to the cancer type and the degree of influence of the pain site of the patient to be monitored, including:

[0013] Classifying the plurality of target parts according to preset importance to obtain a first focused part, a second focused part and a supplementary focused part, wherein the first focused part corresponds to one type of parameter, the second focused part corresponds to two types of parameters, and the supplementary focused part corresponds to three types of parameters;

[0014] Determining whether a target supplementary focus part satisfies a preset focus condition, wherein the target supplementary focus part is any one of the supplementary focus parts;

[0015] When the target supplementary attention site meets a preset attention condition, the target supplementary attention site is updated according to the pain change curve corresponding to the target supplementary attention site.

[0016] By adopting the above technical solution, the target sites are classified by preset importance, and the direct and indirect effects of cancer types and pain sites are taken into account, making the classification more scientific and reasonable. The target sites are divided into the first focus site, the second focus site and the supplementary focus site, so as to achieve differentiated attention to different pain sites. Preset attention conditions are set for the supplementary focus sites, which may include the threshold of the pain index, the limit of the pain duration, the change of the nature of the pain, etc. The setting of these conditions helps medical staff to timely discover and pay attention to those pain changes that may have been ignored but are actually of great significance. When the supplementary focus site meets the preset attention conditions, the change trend and potential risks of the pain can be more accurately judged by analyzing the corresponding pain change curve. This data-based analysis method helps to reduce the error of subjective judgment and improve the accuracy and effectiveness of pain management. According to the analysis results of the pain change curve, the target supplementary focus site is dynamically updated. This means that if a site that was originally regarded as a supplementary focus becomes more important due to increased pain or changes in nature, it can be reclassified as a higher focus category, so as to be managed more timely and effectively.

[0017] Optionally, updating the target supplementary focus area according to the pain change curve corresponding to the target supplementary focus area includes:

[0018] A plurality of discrete points are obtained according to the time point of each acquisition of the target supplementary focus area and the acquired pain index, and a pain change curve of the pain index changing with time is obtained by connecting the plurality of discrete points through a curve fitting technology;

[0019] Determine the change trend of the pain change curve, and judge whether the change trend conforms to a preset change trend, wherein the change trend includes a sharp rise, a slow rise, a slow fall and a stable state;

[0020] When the change trend does not conform to the preset change trend, the target supplementary focus site is updated to the second key focus site.

[0021] By adopting the above technical solution, the pain data is visualized by collecting the pain index of the target supplementary focus area at different time points, and connecting these discrete points into a pain change curve using curve fitting technology. The pain change curve can sensitively capture any subtle changes in the pain index, including sudden fluctuations or long-term trend changes. By determining the change trend of the pain change curve (such as sharp rise, slow rise, slow decline and stability), the development of pain can be scientifically evaluated. This trend recognition ability helps to better understand the patient's pain condition and predict its future development trend. By comparing the change trend of the pain change curve with the preset change trend, it can be determined whether the patient's pain condition is within the normal range. This comparison process provides an objective and quantifiable evaluation standard and reduces the error of subjective judgment. When the change trend of the pain change curve does not conform to the preset change trend (such as a sharp rise), it means that the patient's pain condition may be abnormal or worse. At this time, updating the target supplementary focus area to the second focus area (that is, the area corresponding to the second type of parameters) can ensure that the area receives more timely and effective attention and management.

[0022] Optionally, determining the change trend of the pain change curve includes:

[0023] Calculate the average pain value according to the pain index collected regularly, calculate the variance according to the average pain value, and determine whether the variance is less than a threshold;

[0024] When the variance is less than a threshold, the slope of the pain change curve is calculated to determine the rising / falling speed of the pain index, and the change trend is determined according to the rising / falling speed.

[0025] By adopting the above technical solution, the stability of the pain index can be evaluated by calculating the mean and variance of the pain index collected regularly. The variance less than the threshold indicates that the pain index fluctuates less over a period of time and is relatively stable. This step helps to distinguish whether the pain is in a stable state or is about to change significantly. When the pain index is relatively stable (that is, the variance is less than the threshold), the slope of the pain change curve is further calculated to determine the rise / fall speed of the pain index. The size and direction of the slope directly reflect the change trend of the pain index, such as a sharp rise, a slow rise, a slow fall, or a stable state. This quantitative analysis improves the accuracy of trend judgment. Compared with intuitive observation or simple comparison alone, the method of determining the pain change trend by calculating the slope and variance is more objective and scientific. It reduces the possibility of misjudgment due to subjective judgment or data noise, and improves the accuracy of pain management. When it is determined through quantitative analysis that the pain change trend does not meet the preset standard (such as a sharp rise), the target supplementary focus area can be immediately updated to a higher focus level (such as the area corresponding to the second type of parameter).

[0026] Optionally, the step of integrating the first type of parameters, the second type of parameters and the third type of parameters to obtain a comprehensive pain index includes:

[0027] Assigning a first weight to the first type of parameter, assigning a second weight to the second type of parameter, assigning a third weight to the third type of parameter, multiplying the first weight and the first type of parameter to obtain a first score, the first weight is greater than the second weight, the second weight is greater than the third weight, and the sum of the first weight, the second weight and the third weight is one;

[0028] Assigning the second weight to each first sub-parameter in the two types of parameters to determine a first sub-weight of each first sub-parameter, assigning the third weight to each second sub-parameter in the three types of parameters to determine a second sub-weight of each second sub-parameter, performing a weighted sum of the first sub-parameter and the first sub-weight to obtain a second score, and performing a weighted sum of the second sub-parameter and the second sub-weight to obtain a third score;

[0029] The first score, the second score and the third score are added together to obtain a comprehensive pain index.

[0030] By adopting the above technical solution, different weights are assigned to different categories of pain parameters, and the sum of these weights is ensured to be one. This method ensures that the calculation of the comprehensive pain index is scientific and objective. The weight allocation is based on factors such as the importance of the pain parameter, the degree of association with cancer, and the impact on the patient's quality of life, so that the calculation results are more in line with the actual situation. For each sub-parameter in the second and third category parameters, sub-weights are further allocated and weighted summation is performed. This refinement makes the calculation of the comprehensive pain index more accurate and can more comprehensively reflect the patient's pain condition. By weighted summation, different categories of pain parameters are combined to obtain a comprehensive pain index. This index can comprehensively and objectively reflect the patient's overall pain condition. Since the weight allocation and weighted summation process are adjustable, the weights can be adjusted according to the patient's specific situation and the need for pain management to achieve a more accurate evaluation. This flexibility makes the comprehensive pain index more practical in practical applications.

[0031] Optionally, the monitoring method further includes:

[0032] Establishing a pain index prediction model based on the regularly collected pain index, and predicting the standard pain index at a preset time by using the pain index prediction model;

[0033] Determining whether the comprehensive pain index at the preset moment exceeds the standard pain index;

[0034] When the comprehensive pain index at the preset moment exceeds the standard pain index, an alarm is triggered.

[0035] By adopting the above technical solution, a pain index prediction model is established through regularly collected pain indexes, which can predict the standard pain index at a preset moment in the future. This predictive ability makes pain management no longer limited to the current state, but has forward-looking and predictive. The accuracy of the pain index prediction model directly affects the accuracy and timeliness of the alarm. By continuously optimizing the model algorithm and parameters, the accuracy of the prediction can be improved, thereby more accurately guiding the resource allocation in the pain management process. Through the alarm mechanism, it is possible to respond quickly to the patient's pain changes and take necessary intervention measures. This timely response helps to alleviate the patient's pain and discomfort. The pain index prediction model can not only predict the changing trend of pain conditions, but also be used to assess the risks of pain management. By comparing the differences between the predicted results and the actual situation, potential problems and risk points can be discovered in a timely manner, providing strong support for risk prevention and control.

[0036] Optionally, the monitoring method further includes:

[0037] comparing the comprehensive pain index with the previous comprehensive pain index;

[0038] When the comprehensive pain index is less than the previous comprehensive pain index, continuing to implement the current management plan;

[0039] When the comprehensive pain index is greater than or equal to the previous comprehensive pain index, the management plan is adjusted according to the difference between the comprehensive pain index and the previous comprehensive pain index.

[0040] By adopting the above technical solution, by comparing the current comprehensive pain index with the previous result, the change of pain condition can be understood in real time. When the pain index is found to have increased, a quick response can be made. According to the change of pain index, the management plan is adjusted dynamically. This flexibility ensures that pain management can always adapt to the actual needs of patients and improves the pertinence and effectiveness of management. When the comprehensive pain index is less than the previous time, it means that the current management plan is effective, so it continues to be implemented; when the pain index is greater than or equal to the previous time, the management plan is adjusted according to the difference. This differentiated processing method makes the management plan more accurate and can take corresponding measures for different degrees of pain changes. Comparing the comprehensive pain index and adjusting the management plan is a cyclical process. In this process, by constantly discovering problems, solving problems and optimizing management plans, the level of pain management can be continuously improved.

[0041] In a second aspect of the present application, a patient pain monitoring system for palliative care is provided, comprising a collection module, a classification module, a calculation module and an execution module, wherein:

[0042] A collection module configured to periodically collect pain indexes of multiple target parts of a patient to be monitored;

[0043] a classification module configured to classify the pain index into first-class parameters, second-class parameters and third-class parameters according to the cancer type of the patient to be monitored and the degree of influence of the pain site, wherein the first-class parameters are the pain index of the site directly affected by the tumor, the second-class parameters are the pain index of the site indirectly affected by the tumor, and the third-class parameters are the pain index of the site unrelated to the tumor;

[0044] a calculation module configured to combine the first type of parameters, the second type of parameters and the third type of parameters to obtain a comprehensive pain index;

[0045] An execution module is configured to generate a management plan according to the comprehensive pain index.

[0046] In the third aspect of the present application, an electronic device is provided, including a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are both used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes any one of the methods described above.

[0047] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores instructions, and when the instructions are executed, any of the methods described above is executed.

[0048] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0049] 1. By regularly collecting pain indexes of multiple target parts of the patient, the patient's pain condition can be fully covered, avoiding omissions that may be caused by monitoring a single part; the pain index is divided into first, second and third category parameters according to the type of cancer and the degree of influence of the pain part. This classification method helps to more accurately reflect the actual situation of the patient's pain;

[0050] 2. The pain indexes of different categories are processed comprehensively to obtain a comprehensive pain index, which can more comprehensively reflect the overall pain condition of the patient. The management plan generated based on the comprehensive pain index can be customized according to the specific situation of the patient, thus improving the pertinence and effectiveness of management.

[0051] 3. Regularly collecting pain index can timely understand the changes in the patient's pain; according to the changes in the pain index, the management plan can be dynamically adjusted to meet the actual needs of the patient; through continuous monitoring and evaluation, the management plan can be continuously optimized to improve the effectiveness and quality of pain management. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 is a flow chart of a method for monitoring pain in patients for palliative care disclosed in an embodiment of the present application;

[0053] Figure 2 is a schematic diagram of a module of a patient pain monitoring system for palliative care disclosed in an embodiment of the present application;

[0054] Figure 3 It is a structural schematic diagram of an electronic device disclosed in an embodiment of the present application.

[0055] Explanation of the accompanying drawings: 201, acquisition module; 202, classification module; 203, calculation module; 204, execution module; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0056] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0057] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0058] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0059] This embodiment discloses a method for monitoring patient pain in palliative care, which is applied to a pain monitoring platform. Figure 1 is a flow chart of a method for monitoring pain in patients for palliative care disclosed in an embodiment of the present application, such as Figure 1 As shown, the monitoring method comprises the following steps:

[0060] S110, regularly collecting pain indexes of multiple target parts of the patient to be monitored;

[0061] The embodiment of the present invention adopts pain assessment methods such as VAS visual analog scale and NRS digital score to determine the pain index. The pain index is determined by the patient according to his or her own physical condition in the numbers 0-10 representing different pain levels. VAS Visual Analog Scale is a commonly used pain assessment tool. It uses a 10cm long straight line with two ends representing "no pain" (0 points) and "the most severe pain" (10 points). The patient marks the pain level on the straight line according to his or her self-perception. NRS digital score (Numeric Rating Scale): Similar to VAS, but more directly allows patients to select a number from 0 to 10 to represent their pain level. This method is more concise and convenient for quick recording. Regular collection means that pain assessment is performed at certain time intervals (such as daily, weekly or monthly). The specific interval should be determined according to the patient's condition. A dedicated index collection table is designed for each patient, which lists all target parts that need to be monitored and reserves space to record the pain index of each assessment. The index collection table is shown in the following table:

[0062] ;

[0063] As shown in the table above, the index collection form should include information such as patient number, location, and pain index. The pain index should be accurately recorded for subsequent analysis and comparison. After each assessment, the index collection form should be updated in a timely manner to ensure the integrity and timeliness of the data. Regularly perform statistical analysis on the collected pain index to understand the patient's pain change trend and distribution characteristics.

[0064] S120, according to the cancer type of the patient to be monitored and the degree of influence of the pain site, the pain index is divided into a first-class parameter, a second-class parameter and a third-class parameter, wherein the first-class parameter is the pain index of the site directly affected by the tumor, the second-class parameter is the pain index of the site indirectly affected by the tumor, and the third-class parameter is the pain index of the site unrelated to the tumor;

[0065] Category I parameters refer to pain indices generated by sites that are directly invaded or affected by tumors (such as cancer cells). These pains are usually directly related to the progression of cancer and are one of the important indicators for assessing cancer. In the case of patient 001, the pain index of the abdomen was classified as a category I parameter because the abdomen is the primary site of gastric cancer and the pain is directly caused by the invasion of cancer cells. Category II parameters refer to pain indices that are indirectly affected by tumors (such as cancer cells) or due to complications caused by tumors (such as metastasis). Although these pains are not directly caused by the primary tumor, they are closely related to the progression of the tumor and may be the result of tumor metastasis, compression or other complications. In the case of patient 001, the pain indices of the chest, bones and skin were classified as category II parameters. Chest pain may be caused by lung metastasis, bone pain may be caused by bone metastasis, and skin ulcers may be a manifestation of subcutaneous metastasis. Although these pains are not directly caused by the primary tumor, they are closely related to the progression of the tumor. Category III parameters refer to pain indices that are not related to tumors (such as cancer cells) but are caused by other behaviors or reasons of the patient (such as falls, abrasions, etc.). These pains are not directly related to the progression of cancer and may be caused by the patient's daily life, unexpected events, or other non-cancer related diseases. In the case of patient 001, the pain index of the finger was classified as a Category III parameter because it was caused by an accidental scratch that occurred a few days ago and was not related to cancer.

[0066] Optionally, the pain index is divided into first-class parameter, second-class parameter and third-class parameter according to the cancer type and the degree of influence of the pain site of the patient to be monitored, including:

[0067] Classifying the plurality of target parts according to preset importance to obtain a first focused part, a second focused part and a supplementary focused part, wherein the first focused part corresponds to one type of parameter, the second focused part corresponds to two types of parameters, and the supplementary focused part corresponds to three types of parameters;

[0068] Determining whether a target supplementary focus part satisfies a preset focus condition, wherein the target supplementary focus part is any one of the supplementary focus parts;

[0069] When the target supplementary attention site meets a preset attention condition, the target supplementary attention site is updated according to the pain change curve corresponding to the target supplementary attention site.

[0070] The first focus area is directly invaded or affected by the tumor (such as cancer cells), which is the key to disease assessment and treatment decision-making. They correspond to the first type of parameters, namely the pain index of the area directly affected by the tumor. The second focus area may be indirectly affected by the tumor, such as pain caused by tumor metastasis, compression or complications. They correspond to the second type of parameters, namely the pain index of the area indirectly affected by the tumor. The additional focus area is not directly related to the tumor, and the pain may be caused by other reasons (such as falls, abrasions, etc.). They correspond to the third type of parameters, namely the pain index of the area not related to the tumor.

[0071] The pain assessment table corresponding to patient 001 is shown in the following table:

[0072] ;

[0073] After the additional focus areas are determined, it is necessary to further determine whether each of these areas (i.e., the target additional focus areas) meets the preset focus conditions. These conditions may include but are not limited to:

[0074] The pain index suddenly increases and exceeds a certain threshold; the pain lasts for more than a certain period of time; the nature of the pain changes, such as from dull pain to sharp pain; the pain affects the patient's daily quality of life or psychological state.

[0075] If the target additional attention area meets the preset attention conditions, its attention level or treatment strategy needs to be updated according to the pain change curve corresponding to the area. The update may include:

[0076] Raise the area to a higher level of concern, such as from a supplementary area of ​​concern to a second priority area or a first priority area of ​​concern; adjust the pain management plan and add pain relief measures for this area.

[0077] By subdividing the pain index into first-class parameters, second-class parameters, and third-class parameters according to the type of cancer and the degree of influence of the pain site, accurate and personalized monitoring of the patient's pain condition is achieved. The mechanism includes dynamic evaluation and adjustment of the target supplementary focus site. By judging whether the target supplementary focus site meets the preset focus conditions and updating its focus status accordingly, this dynamic adjustment mechanism ensures the flexibility and adaptability of the monitoring system. As the patient's condition changes, the system can adjust the monitoring focus in time to ensure the effectiveness and timeliness of pain management. Through accurate and dynamic pain monitoring and management, it helps to reduce patients' pain perception and improve their comfort and quality of life.

[0078] Optionally, updating the target supplementary focus area according to the pain change curve corresponding to the target supplementary focus area includes:

[0079] A plurality of discrete points are obtained according to the time point of each acquisition of the target supplementary focus area and the acquired pain index, and a pain change curve of the pain index changing with time is obtained by connecting the plurality of discrete points through a curve fitting technology;

[0080] Determine the change trend of the pain change curve, and judge whether the change trend conforms to a preset change trend, wherein the change trend includes a sharp rise, a slow rise, a slow fall and a stable state;

[0081] When the change trend does not conform to the preset change trend, the target supplementary focus site is updated to the second key focus site.

[0082] At specific time intervals, the pain index of the target supplementary focus area is collected. These time points can be fixed (such as every two hours, every day, etc.) or based on certain trigger conditions (such as when the patient actively reports an increase in pain). The pain index obtained each time and the corresponding time point constitute a discrete point. Over time, multiple such discrete points will accumulate. Using curve fitting techniques (such as linear interpolation, polynomial fitting, smooth curve, etc.), these discrete points are connected to form a smooth curve, namely the pain change curve. This curve reflects the trend of the pain index over time. Analyze the shape and slope of the pain change curve to determine its change trend. Common change trends include sharp rise (indicating rapid increase in pain), slow rise (indicating gradual increase in pain but at a slower rate), slow decline (indicating gradual relief of pain) and stability (indicating that pain remains at a certain level). Based on medical experience or the specific situation of the patient, a series of preset change trends are set as normal or acceptable ranges. These preset trends may vary depending on factors such as the condition and treatment stage. Compare the actual observed pain change trend with the preset change trend to determine whether it meets expectations. If the observed pain trend does not conform to the preset trend, especially when the pain rises sharply, this may indicate that there is an abnormality in the target supplementary focus area and requires closer attention. In this case, the system or medical staff will promote the target supplementary focus area to the second priority focus area (or higher priority) for more frequent monitoring, evaluation and treatment adjustment.

[0083] By recording the pain index collected at different time points of the target supplementary focus area and using curve fitting technology to generate a pain change curve, the change of pain over time can be intuitively displayed. This method improves the accuracy and continuity of pain monitoring, and helps doctors or patients understand the dynamic changes of pain more accurately. By determining the change trend of the pain change curve (such as sharp rise, slow rise, slow decline and stability), the development of pain can be predicted and evaluated. When the pain change trend does not meet the preset health or treatment expectations (such as a sharp rise may indicate a worsening of the condition), the system can detect and respond in time, providing the possibility for timely intervention. When the pain change trend does not meet the preset change trend, the target supplementary focus area is updated to the second focus area. This mechanism reflects the strategy of dynamic adjustment. It allows the system to flexibly adjust the focus according to the actual changes in pain.

[0084] Optionally, determining the change trend of the pain change curve includes:

[0085] Calculate the average pain value according to the pain index collected regularly, calculate the variance according to the average pain value, and determine whether the variance is less than a threshold;

[0086] When the variance is less than a threshold, the slope of the pain change curve is calculated to determine the rising / falling speed of the pain index, and the change trend is determined according to the rising / falling speed.

[0087] According to the pain index collected regularly (i.e., the pain data collected at multiple time points), the average value of these pain indexes is calculated. The average value reflects the overall level of pain over a period of time. Then, based on these pain indexes and the calculated average value, the variance is further calculated. Variance is a statistic that measures the degree of data dispersion, which reflects the fluctuation of the pain index around the average value. The calculated variance is compared with a preset threshold. This threshold is set according to the actual situation (such as the accuracy requirements of pain monitoring, individual differences of patients, etc.) to determine whether the change of the pain index is stable enough. If the variance is less than the threshold, it means that the pain index is relatively stable over a period of time without obvious fluctuations. At this time, the slope of the pain change curve can be further analyzed. When the variance is less than the threshold, the slope of the pain change curve is calculated to evaluate the speed of the increase or decrease of the pain index. The slope indicates how fast the pain index changes over time. A positive value indicates an increase and a negative value indicates a decrease. According to the absolute value of the slope, it can be further determined whether the change trend of the pain index is a sharp increase, a slow increase, a slow decrease or a stable one. A sharp increase: The absolute value of the slope is large and positive, indicating that the pain increases rapidly in a short period of time. Slowly rising: The slope is positive but the absolute value is small, indicating that the pain gradually increases over a long period of time. Slowly falling: The slope is negative but the absolute value is small, indicating that the pain gradually decreases over a long period of time. Steady: The slope is close to zero, indicating that the pain remains relatively stable over a period of time.

[0088] By calculating the mean and variance of the pain index collected regularly, the stability of the pain change can be evaluated. A variance less than the threshold indicates that the pain index fluctuates less and is relatively stable over a period of time. This step helps to distinguish the real change trend of pain from random fluctuations, providing a reliable basis for subsequent analysis. After confirming that the pain change is relatively stable, the rise / fall speed of the pain index is determined by calculating the slope of the pain change curve. Slope analysis is an effective means of identifying trend changes, which can accurately reflect the direction and speed of change of the pain index. This method can capture the subtle trends of pain changes better than simply observing discrete points, and improves the accuracy of trend identification. The embodiment of the present application is not only applicable to stable and changing pain conditions, but also has a certain dynamic adaptability. When the pain change is no longer stable (ie, the variance is greater than the threshold), the pain data can be re-evaluated and the analysis strategy can be adjusted. This flexibility ensures the effectiveness of the method under different pain change modes.

[0089] S130, combining the first type of parameters, the second type of parameters and the third type of parameters to obtain a comprehensive pain index;

[0090] The comprehensive pain index is a comprehensive quantitative indicator that reflects the severity and scope of the patient's overall pain condition. By weighting or otherwise combining the first-class parameters (pain directly affected by the tumor), the second-class parameters (pain indirectly affected by the tumor) and the third-class parameters (pain not related to the tumor), a single value or score is obtained to more intuitively understand the patient's pain condition. The pain index data of patients at different time points and different parts (i.e., the parts corresponding to the first, second and third-class parameters) are collected. These data can be obtained through patient self-assessment, doctor evaluation or pain monitoring equipment. In the synthesis process, different weights are usually assigned to different categories of pain parameters. Generally speaking, the weight of the first-class parameters (pain directly affected parts) will be relatively high because they are most directly related to the cancer condition and treatment effect; the weight of the second-class parameters is second because they reflect the scope and degree of indirect effects of the tumor; the weight of the third-class parameters is relatively low because they are not directly related to the cancer itself. The allocation of weights can be adjusted and optimized based on clinical experience and research data. According to the weight distribution results, various pain parameters can be comprehensively calculated by weighted summation, weighted average or other appropriate mathematical methods. The specific calculation method may vary depending on the application scenario and actual needs, but the goal is to obtain a comprehensive pain index that can fully reflect the patient's pain condition. After obtaining the comprehensive pain index, it needs to be reasonably explained and interpreted. Generally speaking, the higher the comprehensive pain index, the more severe the patient's overall pain condition; conversely, it means that the pain condition is relatively mild.

[0091] Optionally, the step of integrating the first type of parameters, the second type of parameters and the third type of parameters to obtain a comprehensive pain index includes:

[0092] Assigning a first weight to the first type of parameter, assigning a second weight to the second type of parameter, assigning a third weight to the third type of parameter, multiplying the first weight and the first type of parameter to obtain a first score, the first weight is greater than the second weight, the second weight is greater than the third weight, and the sum of the first weight, the second weight and the third weight is one;

[0093] Assigning the second weight to each first sub-parameter in the two types of parameters to determine a first sub-weight of each first sub-parameter, assigning the third weight to each second sub-parameter in the three types of parameters to determine a second sub-weight of each second sub-parameter, performing a weighted sum of the first sub-parameter and the first sub-weight to obtain a second score, and performing a weighted sum of the second sub-parameter and the second sub-weight to obtain a third score;

[0094] The first score, the second score and the third score are added together to obtain a comprehensive pain index.

[0095] Different weights are assigned to parameters of different categories. Here, the first category of parameters is assigned the first weight (denoted as W1), the second category of parameters is assigned the second weight (denoted as W2), and the third category of parameters is assigned the third weight (denoted as W3). The assignment of these weights is based on their respective importance in the comprehensive pain assessment. Generally, W1>W2>W3, indicating that the first category of parameters (pain that directly affects the site) is the most important, the second category of parameters is second, and the third category of parameters is the lightest. At the same time, the sum of these three weights must be 1, that is, W1+W2+W3=1, to ensure that the comprehensive pain index is within a reasonable range. For each sub-parameter in the second category of parameters and the third category of parameters, sub-weights need to be further assigned. For each first sub-parameter in the second category of parameters, the second weight W2 is assigned to them to determine the first sub-weight of each first sub-parameter (denoted as W2i, where i represents the i-th first sub-parameter). Similarly, for each second sub-parameter in the three categories of parameters, the third weight W3 is assigned to them to determine the second sub-weight of each second sub-parameter (denoted as W3j, where j represents the j-th second sub-parameter). The distribution of these sub-weights can be adjusted according to actual conditions, but their sum should be equal to the total weight of their corresponding categories, that is, the sum of all first sub-weights is equal to W2, and the sum of all second sub-weights is equal to W3. Multiply the first weight W1 by the value of the first category parameter (denoted as P1) to obtain the first score (denoted as S1). That is, S1=W1×P1. This score reflects the contribution of the first category parameter in the comprehensive pain assessment. For each first sub-parameter in the second category parameter, multiply its value by the corresponding first sub-weight, and then add all the products to obtain the second score (denoted as S2). That is, S2=∑(W2i×P2i), where P2i represents the value of the i-th first sub-parameter. This score reflects the total contribution of all sub-parameters in the second category parameter to the comprehensive pain assessment. For each second sub-parameter in the third category parameter, perform the same operation as the second category parameter to obtain the third score (denoted as S3). That is, S3=∑j(W3j×P3j), where P3j represents the value of the j-th second sub-parameter. This score reflects the total contribution of all sub-parameters in the three categories of parameters in the comprehensive pain assessment. The first score S1, the second score S2 and the third score S3 are added together to obtain the comprehensive pain index (denoted as CPI). That is, CPI=S1+S2+S3.

[0096] By assigning different weights to different categories of pain parameters, this method achieves a refined assessment of pain conditions. Class I parameters (pain in the direct affected area) are given higher weights due to their direct association with cancer conditions and treatment effects, which helps to more accurately reflect the main source of pain in patients. At the same time, Class II and Class III parameters are also assigned appropriate weights to ensure the comprehensiveness of the assessment. This method not only considers the overall weight of different categories of pain parameters, but also further subdivides and weights each subparameter in Class II and Class III parameters. This flexibility enables the assessment process to consider multiple factors of patient pain in more detail, such as the location, nature, and duration of pain, thereby improving the accuracy and pertinence of the assessment. By calculating the comprehensive pain index in a weighted summation manner, this method reduces the impact of subjective judgment on the assessment results. The weight allocation and calculation process can be optimized and adjusted based on clinical experience and research data to ensure the objectivity and reliability of the assessment results.

[0097] S140, generating a management plan according to the comprehensive pain index.

[0098] Interpret the comprehensive pain index. The comprehensive pain index is a quantitative indicator that reflects the severity of the patient's overall pain condition. Based on the interpretation of the comprehensive pain index, further analyze the patient's pain characteristics. This includes the location, nature, duration, influencing factors, etc. By deeply understanding the patient's pain condition, the cause and type of pain can be more accurately determined, providing an important basis for formulating a management plan. Set clear management goals based on the patient's pain characteristics and comprehensive pain index. These goals can be to reduce the degree of pain, shorten the duration of pain, and improve the patient's quality of life. When setting management goals, it is necessary to fully consider the patient's actual situation and needs to ensure that the goals are feasible and operational. In order to achieve management goals, a series of specific measures need to be formulated. These measures can include drug treatment, non-drug treatment, psychological intervention and other aspects. For example, if the patient needs to supplement a certain nutrient, then among all foods that provide the same nutrition, give priority to the variety that the patient likes most; if the patient needs to relax, then among activities that can reduce anxiety, give priority to activities that meet the patient's preferences. At the same time, psychological intervention is also an important part that cannot be ignored. By alleviating the patient's anxiety, depression and other emotional problems, it helps to improve their pain perception. After the management plan is formulated, it needs to be implemented according to the plan and the implementation effect needs to be monitored. During the implementation process, it is necessary to pay close attention to the changes in the patient's pain and adjust the management strategy in a timely manner. At the same time, it is also necessary to regularly evaluate the implementation effect of the management plan to ensure that patients can obtain the best pain management effect. According to the implementation effect and patient feedback, the management plan is adjusted and optimized as necessary. This includes adjusting drug dosages, changing treatment methods, strengthening psychological intervention and other aspects. Through continuous feedback and adjustment, it can be ensured that the management plan always meets the actual needs of patients and improves the effectiveness and satisfaction of pain management.

[0099] Optionally, the monitoring method further includes:

[0100] Establishing a pain index prediction model based on the regularly collected pain index, and predicting the standard pain index at a preset time by using the pain index prediction model;

[0101] Determining whether the comprehensive pain index at the preset moment exceeds the standard pain index;

[0102] When the comprehensive pain index at the preset moment exceeds the standard pain index, an alarm is triggered.

[0103] Collect the patient's pain index data regularly. This data can be obtained through self-assessment by the patient, evaluation by the doctor, or using pain assessment tools (such as pain rating scales). The collected data should include key information such as the location, intensity, and duration of pain to ensure that the patient's pain condition can be fully reflected. Based on the regularly collected pain index data, a pain index prediction model is established using methods such as statistics, machine learning, or deep learning. This model can learn the changing pattern of the pain index and predict the standard pain index at a preset time in the future (such as the next few hours, days, or weeks). The selection and construction of the model need to take into account the characteristics of the data and the accuracy requirements of the prediction. Use the established pain index prediction model to predict the standard pain index at a preset time. This predicted value represents the future pain condition inferred by the model based on historical data, which can provide important reference information for doctors or pain management teams. When the preset time arrives, re-evaluate the patient's comprehensive pain index and compare it with the predicted standard pain index. If the comprehensive pain index exceeds the predicted standard pain index, it means that the patient's pain condition may be more severe than expected and needs attention. When the comprehensive pain index at the preset time exceeds the standard pain index, the system automatically triggers an alarm mechanism. This can be done by notifying the doctor or pain management team through sound, light signals, text messages or emails. After receiving the alert, the doctor or team needs to quickly assess the patient's pain condition and adjust the management plan based on the actual situation. As new data is continuously collected and accumulated, the pain index prediction model needs to be continuously optimized. This includes updating model parameters, adjusting model structure or introducing new prediction algorithms. By continuously optimizing the prediction model, its prediction accuracy can be improved, providing patients with more accurate and personalized pain management services.

[0104] Through the pain index prediction model, the possible pain change trend of the patient can be predicted before the patient actually feels severe pain. When it is predicted that the comprehensive pain index at the preset moment will exceed the standard value, the system can automatically trigger an alarm to prompt medical staff to take intervention measures in advance. This early warning mechanism helps to avoid the adverse consequences of increased pain and improve the initiative and effectiveness of pain management. The pain index prediction model established based on the patient's historical pain data and individual characteristics can more accurately reflect the patient's pain change pattern. Pain management is a process that requires continuous attention and timely adjustment. Through the pain index prediction model, medical staff can more accurately predict the patient's pain changes, so as to reasonably arrange medical resources and nursing forces. This helps to avoid the waste and overuse of medical resources and improve the efficiency and quality of medical services.

[0105] Optionally, the monitoring method further includes:

[0106] comparing the comprehensive pain index with the previous comprehensive pain index;

[0107] When the comprehensive pain index is less than the previous comprehensive pain index, continuing to implement the current management plan;

[0108] When the comprehensive pain index is greater than or equal to the previous comprehensive pain index, the management plan is adjusted according to the difference between the comprehensive pain index and the previous comprehensive pain index.

[0109] In the process of pain management, the comprehensive pain index is used as an important indicator to measure the patient's pain condition. By regularly collecting and calculating the comprehensive pain index and comparing it with the previous result, the changing trend of the patient's pain condition can be intuitively understood. This process of continuous monitoring and comparison is the basis for formulating and adjusting the management plan. By comparing the current comprehensive pain index with the previous result, the effectiveness of the current management plan can be evaluated. If the current comprehensive pain index is less than the previous result, it means that the management plan is working and the patient's pain condition is improving. At this time, the current management plan can be continued to maintain and consolidate the treatment effect. However, if the current comprehensive pain index is greater than or equal to the previous result, this may mean that the management plan is not effective or that the patient's pain condition has changed. In this case, the management plan needs to be adjusted based on the difference between the comprehensive pain index and the previous result. The size of the difference can reflect the degree of deterioration of the pain condition, thereby guiding the direction and intensity of the adjustment. When adjusting the management plan, the specific situation and needs of the patient should be considered. For example, if the worsening of pain is related to the patient's psychological state or living habits, psychological intervention can be strengthened or living habits can be adjusted. The adjustment strategy should be personalized, comprehensive and targeted. By continuously monitoring and comparing the comprehensive pain index and adjusting the management plan based on the results, a continuous improvement and optimization cycle can be formed. This process not only helps to control the patient's pain condition and improve the quality of life, but also provides valuable experience and reference for future pain management.

[0110] By comparing the comprehensive pain index with real-time monitoring, changes in the patient's pain condition can be detected in time. When the comprehensive pain index rises, it indicates that the current pain management may not be effective enough and the management plan needs to be adjusted to better control the pain. This dynamic adjustment mechanism ensures that the management plan can respond flexibly as the patient's pain condition changes, improving the pertinence and effectiveness of pain management. By comparing the changes in the comprehensive pain index, medical staff can quickly judge the effect of the management plan. If the comprehensive pain index decreases, it means that the current management plan is effective and can continue to be implemented; if the comprehensive pain index increases or remains unchanged, the management plan needs to be adjusted in time. This data-driven decision-making method reduces unnecessary trial and error processes and improves management efficiency. According to the difference between the comprehensive pain index and the previous result, the degree and trend of pain changes can be judged more accurately. This helps medical staff to develop more personalized adjustment strategies. Personalized adjustment strategies can better meet the actual needs of patients and improve the effectiveness of pain management.

[0111] This embodiment also discloses a patient pain monitoring system for palliative care. Figure 2 is a schematic diagram of a module of a patient pain monitoring system for palliative care disclosed in an embodiment of the present application, such as Figure 2 As shown, the system includes a collection module 201, a classification module 202, a calculation module 203 and an execution module 204, wherein:

[0112] A collection module 201 is configured to periodically collect pain indexes of multiple target parts of a patient to be monitored;

[0113] The classification module 202 is configured to classify the pain index into a first-class parameter, a second-class parameter and a third-class parameter according to the cancer type of the patient to be monitored and the degree of influence of the pain site, wherein the first-class parameter is the pain index of the site directly affected by the tumor, the second-class parameter is the pain index of the site indirectly affected by the tumor, and the third-class parameter is the pain index of the site unrelated to the tumor;

[0114] A calculation module 203, configured to combine the first type of parameters, the second type of parameters and the third type of parameters to obtain a comprehensive pain index;

[0115] The execution module 204 is configured to generate a management plan according to the comprehensive pain index.

[0116] Optionally, the classification module 202 is configured to:

[0117] Classifying the plurality of target parts according to preset importance to obtain a first focused part, a second focused part and a supplementary focused part, wherein the first focused part corresponds to one type of parameter, the second focused part corresponds to two types of parameters, and the supplementary focused part corresponds to three types of parameters;

[0118] Determining whether a target supplementary focus part satisfies a preset focus condition, wherein the target supplementary focus part is any one of the supplementary focus parts;

[0119] When the target supplementary attention site meets a preset attention condition, the target supplementary attention site is updated according to the pain change curve corresponding to the target supplementary attention site.

[0120] Optionally, the classification module 202 is configured to:

[0121] A plurality of discrete points are obtained according to the time point of each acquisition of the target supplementary focus area and the acquired pain index, and a pain change curve of the pain index changing with time is obtained by connecting the plurality of discrete points through a curve fitting technology;

[0122] Determine the change trend of the pain change curve, and judge whether the change trend conforms to a preset change trend, wherein the change trend includes a sharp rise, a slow rise, a slow fall and a stable state;

[0123] When the change trend does not conform to the preset change trend, the target supplementary focus site is updated to the second key focus site.

[0124] Optionally, the classification module 202 is configured to:

[0125] Calculate the average pain value according to the pain index collected regularly, calculate the variance according to the average pain value, and determine whether the variance is less than a threshold;

[0126] When the variance is less than a threshold, the slope of the pain change curve is calculated to determine the rising / falling speed of the pain index, and the change trend is determined according to the rising / falling speed.

[0127] Optionally, the calculation module 203 is configured to:

[0128] Assigning a first weight to the first type of parameter, assigning a second weight to the second type of parameter, assigning a third weight to the third type of parameter, multiplying the first weight and the first type of parameter to obtain a first score, the first weight is greater than the second weight, the second weight is greater than the third weight, and the sum of the first weight, the second weight and the third weight is one;

[0129] Assigning the second weight to each first sub-parameter in the two types of parameters to determine a first sub-weight of each first sub-parameter, assigning the third weight to each second sub-parameter in the three types of parameters to determine a second sub-weight of each second sub-parameter, performing a weighted sum of the first sub-parameter and the first sub-weight to obtain a second score, and performing a weighted sum of the second sub-parameter and the second sub-weight to obtain a third score;

[0130] The first score, the second score and the third score are added together to obtain a comprehensive pain index.

[0131] Optionally, the monitoring system further includes an alarm module, wherein the alarm module is configured to:

[0132] Establishing a pain index prediction model based on the regularly collected pain index, and predicting the standard pain index at a preset time by using the pain index prediction model;

[0133] Determining whether the comprehensive pain index at the preset moment exceeds the standard pain index;

[0134] When the comprehensive pain index at the preset moment exceeds the standard pain index, an alarm is triggered.

[0135] Optionally, the monitoring system further comprises a comparison module, wherein the comparison module is configured to:

[0136] comparing the comprehensive pain index with the previous comprehensive pain index;

[0137] When the comprehensive pain index is less than the previous comprehensive pain index, continuing to implement the current management plan;

[0138] When the comprehensive pain index is greater than or equal to the previous comprehensive pain index, the management plan is adjusted according to the difference between the comprehensive pain index and the previous comprehensive pain index.

[0139] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0140] This embodiment also discloses an electronic device, referring to Figure 3The electronic device may include: at least one processor 301 , at least one communication bus 302 , a user interface 303 , a network interface 304 , and at least one memory 305 .

[0141] The communication bus 302 is used to realize the connection and communication between these components.

[0142] The user interface 303 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0143] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0144] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the processor 301 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 301 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 301, and it can be implemented separately through a chip.

[0145] Among them, the memory 305 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may also be optionally at least one storage device located away from the aforementioned processor 301. As Figure 3 As shown, the memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an application program for a method for monitoring pain in patients undergoing palliative care.

[0146] exist Figure 3 In the electronic device shown, the user interface 303 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 301 can be used to call the application program of the patient pain monitoring method applied to palliative care stored in the memory 305. When executed by one or more processors 301, the electronic device executes one or more methods such as those in the above-mentioned embodiments.

[0147] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the order of the actions described, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0148] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0149] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0150] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0151] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0152] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory 305 and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory 305 includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk.

[0153] The above is only an exemplary embodiment of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification, those skilled in the art will easily think of other embodiments of the present disclosure. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field that are not recorded in the present disclosure. The description and examples are only regarded as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A method for monitoring pain in patients undergoing palliative care, characterized in that: Applied to a pain monitoring platform, the monitoring method comprises: Regularly collect pain indexes of multiple target sites of the patient to be monitored; According to the cancer type of the patient to be monitored and the degree of influence of the pain site, the pain index is divided into a first-class parameter, a second-class parameter and a third-class parameter. The first-class parameter is the pain index of the site directly affected by the tumor, the second-class parameter is the pain index of the site indirectly affected by the tumor, and the third-class parameter is the pain index of the site unrelated to the tumor. The first type of parameters, the second type of parameters and the third type of parameters are integrated to obtain a comprehensive pain index; Generate a management plan based on the comprehensive pain index, According to the cancer type of the patient to be monitored and the degree of influence of the pain site, the pain index is divided into first-class parameters, second-class parameters and third-class parameters, including: Classifying the plurality of target parts according to preset importance to obtain a first focused part, a second focused part and a supplementary focused part, wherein the first focused part corresponds to one type of parameter, the second focused part corresponds to two types of parameters, and the supplementary focused part corresponds to three types of parameters; Determining whether a target supplementary focus part satisfies a preset focus condition, wherein the target supplementary focus part is any one of the supplementary focus parts; When the target supplementary attention site meets a preset attention condition, the target supplementary attention site is updated according to the pain change curve corresponding to the target supplementary attention site.

2. The method for monitoring pain in patients for palliative care according to claim 1, characterized in that: The updating of the target supplementary focus area according to the pain change curve corresponding to the target supplementary focus area comprises: A plurality of discrete points are obtained according to the time point of each acquisition of the target supplementary focus area and the acquired pain index, and a pain change curve of the pain index changing with time is obtained by connecting the plurality of discrete points through a curve fitting technology; Determine the change trend of the pain change curve, and judge whether the change trend conforms to a preset change trend, wherein the change trend includes a sharp rise, a slow rise, a slow fall and a stable state; When the change trend does not conform to the preset change trend, the target supplementary focus site is updated to the second key focus site.

3. The method for monitoring pain in patients for palliative care according to claim 2, characterized in that: Determining the change trend of the pain change curve includes: Calculate the average pain value according to the pain index collected regularly, calculate the variance according to the average pain value, and determine whether the variance is less than a threshold; When the variance is less than a threshold, the slope of the pain change curve is calculated to determine the rising / falling speed of the pain index, and the change trend is determined according to the rising / falling speed.

4. The method for monitoring pain in patients for palliative care according to claim 1, characterized in that: The step of synthesizing the first type of parameters, the second type of parameters and the third type of parameters to obtain a comprehensive pain index comprises: Assigning a first weight to the first type of parameter, assigning a second weight to the second type of parameter, assigning a third weight to the third type of parameter, multiplying the first weight and the first type of parameter to obtain a first score, the first weight is greater than the second weight, the second weight is greater than the third weight, and the sum of the first weight, the second weight and the third weight is one; Assigning the second weight to each first sub-parameter in the two types of parameters to determine a first sub-weight of each first sub-parameter, assigning the third weight to each second sub-parameter in the three types of parameters to determine a second sub-weight of each second sub-parameter, performing a weighted sum of the first sub-parameter and the first sub-weight to obtain a second score, and performing a weighted sum of the second sub-parameter and the second sub-weight to obtain a third score; The first score, the second score and the third score are added together to obtain a comprehensive pain index.

5. The method for monitoring pain in patients for palliative care according to claim 1, characterized in that: The monitoring method further comprises: Establishing a pain index prediction model based on the regularly collected pain index, and predicting the standard pain index at a preset time by using the pain index prediction model; Determining whether the comprehensive pain index at the preset moment exceeds the standard pain index; When the comprehensive pain index at the preset moment exceeds the standard pain index, an alarm is triggered.

6. The method for monitoring pain in patients for palliative care according to claim 1, characterized in that: The monitoring method further comprises: comparing the comprehensive pain index with the previous comprehensive pain index; When the comprehensive pain index is less than the previous comprehensive pain index, continuing to implement the current management plan; When the comprehensive pain index is greater than or equal to the previous comprehensive pain index, the management plan is adjusted according to the difference between the comprehensive pain index and the previous comprehensive pain index.

7. A patient pain monitoring system for palliative care, characterized in that: It includes a collection module, a classification module, a calculation module and an execution module, among which: A collection module configured to periodically collect pain indexes of multiple target parts of a patient to be monitored; a classification module configured to classify the pain index into first-class parameters, second-class parameters and third-class parameters according to the cancer type of the patient to be monitored and the degree of influence of the pain site, wherein the first-class parameters are the pain index of the site directly affected by the tumor, the second-class parameters are the pain index of the site indirectly affected by the tumor, and the third-class parameters are the pain index of the site unrelated to the tumor; a calculation module configured to combine the first type of parameters, the second type of parameters and the third type of parameters to obtain a comprehensive pain index; an execution module configured to generate a management plan according to the comprehensive pain index, According to the cancer type of the patient to be monitored and the degree of influence of the pain site, the pain index is divided into first-class parameters, second-class parameters and third-class parameters, including: Classifying the plurality of target parts according to preset importance to obtain a first focused part, a second focused part and a supplementary focused part, wherein the first focused part corresponds to one type of parameter, the second focused part corresponds to two types of parameters, and the supplementary focused part corresponds to three types of parameters; Determining whether a target supplementary focus part satisfies a preset focus condition, wherein the target supplementary focus part is any one of the supplementary focus parts; When the target supplementary attention site meets a preset attention condition, the target supplementary attention site is updated according to the pain change curve corresponding to the target supplementary attention site.

8. An electronic device, characterized in that: It includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are both used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 6 is performed.

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