Quantitative evaluation system for immune state of patient with thymoma complicated with myasthenia gravis

Abstract

The invention belongs to the technical field of evaluation of immune states of patients with thymoma, and particularly relates to a quantitative evaluation system for immune states of patients with thymoma and myasthenia gravis. According to the method, the immune state of the patient can be evaluated more accurately by comprehensively analyzing the quantitative score and the abnormal confidence coefficient of the immune index of the patient, compared with a traditional evaluation method, the quantitative score is considered, the parameter of the abnormal confidence coefficient is introduced, and therefore the accuracy and the reliability of the evaluation result are improved, and in addition, the evaluation accuracy is improved. By dynamically adjusting the evaluation threshold, the evaluation system can be more flexible and adapt to individual differences of different patients, and the accuracy and practicability of evaluation are further improved. Meanwhile, the immune state database is constructed, immune state data of the patient is tracked and analyzed for a long time, and more comprehensive immune state information of the patient is provided for doctors.

Description

Technical Field

[0001] The invention belongs to the technical field of immune status assessment of thymoma patients, and in particular relates to a quantitative immune status assessment system for thymoma patients combined with myasthenia gravis. Background Art

[0002] As the incidence of myasthenia gravis caused by thymoma increases year by year, it has a serious impact on the quality of life of patients. Although it has been proven that surgical resection of the thymus is the key to treating thymoma and myasthenia gravis, even with the latest and most minimally invasive "three-hole" anterior mediastinal tumor resection under the xiphoid costal margin, it still causes great trauma to patients and causes changes in the body's immunity. It is easy to induce myasthenic crisis after surgery and cause fatal danger. At present, there is a lack of accurate and stable quantitative indicators for the immune status of patients after thymectic surgery, and there are no reports on the immune molecular mechanism research of thymectic surgery for the treatment improvement of patients with combined myasthenia gravis. Therefore, it is particularly important to develop and study a system that can accurately and quantitatively evaluate the postoperative immune status of patients with thymoma and myasthenia gravis.

[0003] At present, evaluation algorithms are introduced for immune status assessment, and machine computing power is used to output evaluation results. However, there are many limitations in the current evaluation methods. For example, the evaluation results are generally reflected in the form of scores. However, since the immune status is related to various factors, even if the evaluation value is output after fusion of various data, it still has great drawbacks, especially for the evaluation value close to the threshold. Although it meets the evaluation requirements, it has great uncertainty. If it is regarded as a normal value and the patient is not treated, there is a certain risk. Based on this, the present invention proposes a computer-assisted quantitative evaluation system, which aims to solve the above problems. Summary of the invention

[0004] The purpose of the present invention is to provide a quantitative assessment system for the immune status of patients with thymoma combined with myasthenia gravis, which can accurately and comprehensively assess the immune status of patients, thereby providing doctors with more reliable diagnostic basis and treatment recommendations.

[0005] The technical solution adopted by the present invention is as follows: A quantitative evaluation system for the immune status of patients with thymoma and myasthenia gravis, comprising: A multimodal data collection module, used to collect immune index data of patients, wherein the immune index data includes T cell subset ratio, B cell number and autoantibody titer; The feature extraction module is used to extract key features from the collected immune index data, add feature codes simultaneously, and output them as base feature parameters; An evaluation module is used to quantitatively score the patient's immune status based on the baseline characteristic parameters and output abnormal confidence; The intervention module is used to trigger differentiated intervention strategies based on quantitative scores and abnormal confidence levels. The differentiated intervention strategies include trend prediction of normal states, output of intervention data based on prediction deviations, abnormal level classification of abnormal states, risk verification based on abnormal levels, and dynamic adjustment of the collection frequency of the multimodal data collection module.

[0006] In a preferred embodiment, the data acquisition module includes a preprocessing unit, and the preprocessing unit is used to perform data preprocessing on the immune index data, including: Acquire the immune index data, and perform denoising processing to eliminate abnormal values ​​and noise in the immune index data; Normalizing the denoised immune index data to unify the magnitude of the immune index data; The normalized immune index data are classified and summarized, and output as multiple immune index data subsets.

[0007] In a preferred embodiment, the feature extraction module includes a feature extraction unit and a feature encoding unit. The feature extraction unit is preset with a demand feature template. By comparing the demand feature template with the preprocessed immune index data, key features that are consistent with the demand feature template are extracted from the preprocessed immune index data. The feature encoding unit is used to encode the extracted key features, generate corresponding feature codes, and output them as reference feature parameters. Wherein, the feature code is numerical data.

[0008] In a preferred embodiment, the evaluation module includes an evaluation unit and a confidence calculation unit, wherein the evaluation unit is used to receive the basal feature parameters, and input them into a preset evaluation comparison table to perform matching analysis on the patient's immune status, and synchronously output the quantitative scores of each of the basal feature parameters, and the status determination unit determines the patient's immune status according to the quantitative scores, and synchronously outputs the corresponding evaluation report; Wherein, the immune status includes a normal status and an abnormal status, and the evaluation report includes a normal evaluation report corresponding to the normal status and an abnormal evaluation report corresponding to the abnormal status; The evaluation module also includes a confidence evaluation unit, which is used to perform confidence evaluation on the abnormal degree of the patient's immune status based on the quantitative score and output abnormal confidence, wherein the abnormal confidence is used to indicate the credibility of the abnormal immune status of the patient.

[0009] In a preferred embodiment, the evaluation module further comprises a state determination unit, which is used to collect all quantitative scores reflecting the patient's immune status within a preset sampling period; The quantitative score within the sampling period is weighted and integrated with the anomaly confidence level to output a comprehensive score. By comparing the preset dynamic assessment threshold with the comprehensive score, the patient's immune status type is output; Wherein, when the comprehensive score is greater than the dynamic assessment threshold, it indicates that the corresponding basal characteristic parameter is normal, and the immune status of the corresponding patient is recorded as normal; When the comprehensive score is less than or equal to the dynamic evaluation threshold, it indicates that the corresponding basal characteristic parameter is abnormal, and the immune status of the corresponding patient is recorded as an abnormal state.

[0010] In a preferred embodiment, the intervention module includes a normal intervention unit, which is used to perform trend prediction in a normal state; And, a summary unit and a statistical analysis unit, the summary unit is used to construct an immune status database, and summarize the base characteristic parameters under normal conditions into the immune status database, and add a timestamp identifier to each of the base characteristic parameters, the statistical analysis unit is used to perform statistical analysis on the base characteristic parameters after adding the timestamp identifier, and determine the range of the immune status parameters under normal conditions.

[0011] In a preferred embodiment, the normal intervention unit is used to record the baseline characteristic parameters with timestamp identifiers as baseline condition parameters, and input the baseline condition parameters into a preset trend measurement function, and record the output result of the trend measurement function as trend condition parameters, output a trend prediction curve based on the trend condition parameters, and then calculate the safe time that the patient is in a normal state based on the trend condition parameters. When the trend condition parameters deviate from the baseline, an early warning is triggered, and the collection frequency of the multimodal data acquisition module is increased, and the safety time is updated synchronously.

[0012] In a preferred embodiment, the intervention module also includes an abnormal intervention unit, which includes a difference calculation subunit and an abnormal level determination subunit. The difference calculation subunit is used to receive the basal characteristic parameters under the abnormal state, and calculate the difference between the basal characteristic parameters and the standard characteristic parameters under the abnormal state, and output it as an abnormal difference. The abnormal level determination subunit determines the abnormal level of the patient's immune status according to the size of the abnormal difference and with reference to a preset abnormal level standard.

[0013] In a preferred embodiment, the abnormal intervention unit further includes a risk verification subunit, which is used to compare and analyze the current immune status data with the historical immune status data, and output the similarity between the current abnormal state and the historical abnormal state; If the similarity between the current abnormal state and the historical abnormal state exceeds the preset similarity threshold, it is determined to be a known risk type, and the collection frequency of the multimodal data collection module is adjusted to the collection frequency under the historical abnormal state; If the similarity between the current abnormal state and the historical abnormal state does not exceed the preset similarity threshold, it is determined to be an unknown risk type, and the high-risk collection mode is started, increasing the collection frequency of the multimodal data collection module to the maximum.

[0014] And, an electronic device, the electronic device comprising: at least one processor; and a memory communicatively coupled to the at least one processor; The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the above-mentioned quantitative assessment system for the immune status of patients with thymoma and myasthenia gravis.

[0015] The technical effects achieved by the present invention are: The present invention can more accurately evaluate the patient's immune status by comprehensively analyzing the quantitative scoring and abnormal confidence of the patient's immune indicators. Compared with the traditional evaluation method, the present invention not only considers the quantitative scoring, but also introduces the parameter of abnormal confidence, thereby improving the accuracy and reliability of the evaluation results. In addition, by dynamically adjusting the evaluation threshold, the evaluation system can be made more flexible and adaptable to the individual differences of different patients, further improving the accuracy and practicality of the evaluation. At the same time, the present invention also provides doctors with more comprehensive patient immune status information by constructing an immune status database, tracking and analyzing the patient's immune status data for a long time, and helping doctors to formulate more accurate and personalized treatment plans. At the same time, the immune status evaluation under abnormal conditions and the determination of abnormal levels are helpful to timely discover the patient's immune abnormalities, take corresponding intervention measures, and improve the treatment effect. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

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

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

[0020] See also Figure 1 As shown, the present invention provides a quantitative evaluation system for the immune status of patients with thymoma combined with myasthenia gravis, comprising: A multimodal data collection module is used to collect the patient's immune index data, where the immune index data includes T cell subset ratio, B cell number and autoantibody titer; The feature extraction module is used to extract key features from the collected immune index data, add feature codes simultaneously, and output them as base feature parameters; An evaluation module is used to quantitatively score the patient's immune status based on the baseline characteristic parameters and output abnormal confidence; The intervention module is used to trigger differentiated intervention strategies based on quantitative scores and abnormal confidence levels. The differentiated intervention strategies include trend prediction of normal states, output of intervention data based on prediction deviations, abnormal level classification of abnormal states, risk verification based on abnormal levels, and dynamic adjustment of the collection frequency of the multimodal data collection module.

[0021] The present invention relates to a quantitative immune status evaluation system for patients with thymoma combined with myasthenia gravis. The system is composed of multiple modules, including a multimodal data acquisition module, a feature extraction module, an evaluation module and an intervention module. The main function of the multimodal data acquisition module is to collect immune index data of patients, and the immune index data covers key indicators such as T cell subset ratio, B cell number and autoantibody titer. The feature extraction module is responsible for extracting key features from the collected immune index data, and adding corresponding feature codes to the extracted key features, and finally outputting a group of basal feature parameters. The function of the evaluation module is to accurately evaluate the immune status of the patient based on the basal feature parameters, and then distinguish the immune status of the patient based on the evaluation results. In this embodiment, the immune status includes a normal state and an abnormal state. The purpose of the intervention module is to collect corresponding basal feature parameters when the patient is in a normal state, and perform corresponding statistical analysis, so as to establish a detailed immune status database under a normal state, and calculate abnormal differences according to the basal feature parameters under an abnormal state, and then determine the abnormal level of the patient's immune status based on the abnormal differences, thereby providing a scientific basis for the clinical treatment of the patient.

[0022] In a preferred embodiment, the data acquisition module includes a preprocessing unit, which is used to preprocess the immune index data, including: Obtain immune indicator data and perform denoising to eliminate outliers and noise in the immune indicator data; Normalize the denoised immune index data to unify the magnitude of the immune index data; The normalized immune index data are classified and summarized, and output as multiple immune index data subsets.

[0023] In this embodiment, a preprocessing unit is provided in the data acquisition module, and the preprocessing unit is responsible for performing preliminary processing on the immune index data. The main working process is as follows: first, the immune index data is obtained. The obtained immune index data often contains various noises and outliers, which may have an adverse effect on subsequent analysis. Therefore, the immune index data needs to be denoised to eliminate the outliers and noise in the data to ensure the accuracy and reliability of the data. After the denoising is completed, the cleaned immune index data needs to be normalized. The purpose of normalization is to unify the magnitudes of different immune index data so that they can be compared and analyzed under the same standard, thereby improving the efficiency and accuracy of data processing. The normalized immune index data will be classified and summarized. Through classification and summary, the data can be organized into multiple immune index data subsets, each of which contains data of a specific category, which is convenient for targeted analysis and research.

[0024] In a preferred embodiment, the feature extraction module includes a feature extraction unit and a feature encoding unit. The feature extraction unit is preset with a demand feature template. By comparing the demand feature template with the preprocessed immune index data, key features that are consistent with the demand feature template are extracted from the preprocessed immune index data. The feature encoding unit is used to encode the extracted key features, generate corresponding feature codes, and output them as reference feature parameters. Among them, the feature code is numerical data.

[0025] In this embodiment, the feature extraction module mainly includes a feature extraction unit and a feature encoding unit. In the feature extraction unit, a series of demand feature templates are pre-set. The demand feature templates are designed according to specific requirements so that the corresponding data features can be accurately identified and extracted. When the pre-processed immune index data is input into the feature extraction unit, the system will compare and analyze the immune index data with the demand feature template accordingly. Through this comparison, the system can identify and extract key features that match the demand feature template to reflect the core information of the immune index data. The extracted key features will then be passed to the feature encoding unit. The function of the feature encoding unit is to encode the extracted key features. The encoding process involves converting the key features into a standardized numerical data format, namely, feature encoding. The generation of feature encoding is to facilitate subsequent data processing and analysis, simplifying complex feature information into a set of quantifiable values ​​for easy reading and processing by the computer system, and finally serving as a benchmark feature parameter for subsequent analysis. In addition, it should be pointed out that in the entire feature extraction and encoding process, the generated feature code is numerical data, which means that the encoded features can be represented by numbers, which is convenient for mathematical operations and statistical analysis.

[0026] In a preferred embodiment, the evaluation module includes an evaluation unit and a confidence calculation unit, wherein the evaluation unit is used to receive the basal characteristic parameters, input them into a preset evaluation comparison table to perform matching analysis on the patient's immune status, and synchronously output the quantitative scores of each basal characteristic parameter; The confidence assessment unit is used to perform confidence assessment on the abnormality of the patient's immune status based on the quantitative score and output abnormality confidence, wherein the abnormality confidence is used to indicate the credibility of the abnormality of the patient's immune status.

[0027] Specifically, the evaluation module consists of an evaluation unit and a confidence measurement unit. The main responsibility of the evaluation unit is to receive basal feature parameters from different sources, and then the basal feature parameters are input into a preset evaluation comparison table. The comparison table is constructed based on a large amount of clinical data and medical research, and can accurately match and analyze the patient's immune status. Through this analysis, the evaluation unit can synchronously output the quantitative scores of each basal feature parameter. The quantitative score reflects the relative importance and influence of each parameter in evaluating the patient's immune status. The confidence evaluation unit is responsible for the task of performing a corresponding confidence evaluation on the abnormality of the patient's immune status based on the quantitative score output by the evaluation unit. First, the difference between the quantitative score and the preset evaluation threshold is collected and recorded as the deviation amplitude of the quantitative score, and then input into the preset confidence measurement function to calculate the corresponding abnormal confidence: ; In the formula, represents the abnormal confidence, Indicates the deviation of quantitative scoring ( , Quantitative rating, represents the preset evaluation threshold), Indicates the maximum allowable deviation (the value is 0.25~0.3), represents the coefficient of variation ( , where It represents the arithmetic mean of T cell subset ratio, B cell absolute count and antibody titer detection value. represents variance), Indicates the correlation between the current quantitative score and the historical quantitative score ( ), Indicates the historical normal range, It represents the mutation correction factor (penalty coefficient of antibody titer mutation). Based on this, a comprehensive abnormality confidence can be derived to indicate the credibility of the abnormal immune status of the patient. That is, the higher the abnormality confidence, the greater the possibility that the patient's immune status is abnormal, and vice versa.

[0028] In a preferred embodiment, the evaluation module further includes a status determination unit, which determines the immune status of the patient according to the quantitative score and synchronously outputs a corresponding evaluation report; The immune status includes a normal status and an abnormal status, and the assessment report includes a normal assessment report corresponding to the normal status and an abnormal assessment report corresponding to the abnormal status; When the status determination unit is executed, all quantitative scores reflecting the patient's immune status are collected within a preset sampling period; The quantitative score within the sampling period is weighted and integrated with the anomaly confidence level to output a comprehensive score. By comparing the preset dynamic assessment threshold with the comprehensive score, the patient's immune status type is output; Among them, when the comprehensive score is greater than the dynamic assessment threshold, it indicates that the corresponding basal characteristic parameters are normal, and the corresponding patient's immune status is recorded as normal; When the comprehensive score is less than or equal to the dynamic assessment threshold, it indicates that the corresponding basal characteristic parameter is abnormal, and the immune status of the corresponding patient is recorded as abnormal.

[0029] In this embodiment, the evaluation module also includes a key component, namely, a state determination unit, which is used to determine the patient's immune status based on a series of quantitative scores and abnormal confidence levels. In this embodiment, the immune status is divided into two categories, normal status and abnormal status. Correspondingly, the evaluation report is also divided into two types, one is a normal evaluation report for the normal status, which records various indicators of the patient's immune system functioning well, and the other is an abnormal evaluation report for the abnormal status, which records possible problems and abnormal indicators of the patient's immune system. When performing state determination, the state determination unit will collect all quantitative scores that can reflect the patient's immune status within a preset sampling period. The collected quantitative score data will be weighted and fused with a preset abnormal confidence level. The weighted fusion formula is: ; In the formula, Indicates the comprehensive rating. and The weight factors representing the quantitative score and the abnormality confidence, respectively, represent the mean of the quantitative score within the sampling period, in this way, the state determination unit can output a comprehensive score, thereby more comprehensively reflecting the immune status of the patient; Here, it should be made clear that and is a dynamic factor, which is automatically adjusted according to the fluctuation rate of the patient's historical data. , , where is the sensitivity coefficient (usually 1≤ ≤3), generally speaking, when When the value of is close to 0, it means that the closer the quantitative score is to the evaluation threshold, the higher its instability is, that is, the patient's immune status is more likely to be abnormal. At this time, The value of is close to 0, The value of is close to 1. When outputting the comprehensive score, more attention is paid to the abnormal confidence. In order to further determine the patient's immune status type, the status determination unit will set the preset dynamic evaluation threshold ( , where represents the dynamic evaluation threshold, represents the initial threshold, It represents the correction factor, usually ranging from 0.1 to 0.3. Indicates The improvement rate of immune indicators after intervention Through this comparison, it can be determined whether the patient's immune status is within the normal range or has deviated from normal, and the corresponding immune status type can be output. Specifically, if the comprehensive score is greater than this dynamic evaluation threshold, it means that the patient's basal characteristic parameters are within the normal range, and the patient's immune status can be recorded as normal. Such a result is a positive signal for medical personnel, meaning that the patient's current immune system is healthy. On the contrary, if the comprehensive score is less than or equal to the dynamic evaluation threshold, it means that the patient's basal characteristic parameters are abnormal, and the patient's immune status will be recorded as abnormal. In this case, medical personnel need to further analyze the evaluation report, find out the specific cause of the abnormal immune status, and take corresponding treatment measures.

[0030] In a preferred embodiment, the intervention module includes a normal intervention unit, which is used to perform trend prediction under normal conditions. The normal intervention unit is used to record the base feature parameters with timestamps as base condition parameters, input the base condition parameters into a preset trend measurement function, and record the output result of the trend measurement function as the trend condition parameter, output a trend prediction curve based on the trend condition parameter, and then calculate the safe time of the patient in a normal state based on the trend condition parameter. When the trend condition parameter deviates from the baseline, an early warning is triggered, and the collection frequency of the multimodal data collection module is increased, and the safe time is updated synchronously; Specifically, when the normal intervention unit is executed, the base characteristic parameters with timestamps are recorded and defined as base condition parameters. The base condition parameters are then input into a pre-set trend calculation function. Through the calculation and processing of the trend calculation function, an output result recorded as a trend condition parameter can be obtained. The trend condition parameter reflects the development trend of the patient's immune status based on the patient's current and historical health status. The expression of the trend calculation function is: , where represents the trend condition parameter, Indicates the length of time covered by the base condition parameters, represents the number of base condition parameters, and It represents the base condition parameter under adjacent timestamps. Then, according to the trend condition parameter, the safe time for the patient to be in a normal state can be further calculated. The calculation formula involved is: , where represents the standard characteristic parameters, represents the base characteristic parameters, It stands for safe time. The safe time refers to the length of time that the patient can maintain a normal state under the current health condition. Through the length of the safe time, doctors and care staff can better formulate corresponding treatment and care plans to ensure that patients receive appropriate intervention and attention within a safe time frame, thereby effectively preventing and controlling the deterioration of the disease.

[0031] and, a summary unit and a statistical analysis unit coordinated with the normal intervention unit, wherein the summary unit is used to construct an immune status database, and summarize the basal characteristic parameters under normal conditions into the immune status database, and add a timestamp to each basal characteristic parameter, and the statistical analysis unit is used to perform statistical analysis on the basal characteristic parameters after adding the timestamp to determine the range of immune status parameters under normal conditions; Specifically, the main responsibility of the summary unit is to build a comprehensive immune status database, which is responsible for collecting and summarizing the basal characteristic parameters under normal conditions into this immune status database to ensure the integrity and traceability of the immune indicator data. In addition, the summary unit will add a timestamp identifier for each basal characteristic parameter to record the specific collection time of each basal characteristic parameter for subsequent tracking and analysis. On the other hand, the statistical analysis unit is responsible for performing corresponding statistical analysis on the basal characteristic parameters with timestamp identifiers, so as to determine the range of immune status parameters under normal conditions.

[0032] In a preferred embodiment, the intervention module also includes an abnormal intervention unit, which includes a difference calculation subunit and an abnormal level determination subunit. The difference calculation subunit is used to receive the basal characteristic parameters under the abnormal state, and calculate the difference between the basal characteristic parameters and the standard characteristic parameters under the abnormal state, and output it as an abnormal difference. The abnormal level determination subunit determines the abnormal level of the patient's immune status based on the size of the abnormal difference and with reference to the preset abnormal level standard.

[0033] Specifically, the abnormal intervention unit is composed of a difference calculation subunit and an abnormal level determination subunit. The main responsibility of the difference calculation subunit is to receive the basal characteristic parameters under abnormal conditions. The difference calculation subunit will perform a difference calculation between the basal characteristic parameters and the standard characteristic parameters preset by the system, thereby obtaining the difference between the two and synchronously recording it as the abnormal difference. The abnormal level determination subunit is responsible for determining the abnormal level of the patient's immune status based on the abnormal difference output by the difference calculation subunit. The abnormal level determination subunit will refer to a set of preset abnormal level standards (the abnormal level standards are formulated based on medical research and clinical experience, and can help the system accurately assess the severity of the abnormality), and then compare the abnormal difference with the abnormal level standard to divide the abnormal level into different levels, such as low, medium, high or more detailed divisions.

[0034] In addition, the abnormal intervention unit also includes a risk verification subunit, which is used to compare and analyze the current immune status data with the historical immune status data, and output the similarity between the current abnormal state and the historical abnormal state; If the similarity between the current abnormal state and the historical abnormal state exceeds the preset similarity threshold, it is determined to be a known risk type, and the collection frequency of the multimodal data collection module is adjusted to the collection frequency under the historical abnormal state; If the similarity between the current abnormal state and the historical abnormal state does not exceed the preset similarity threshold, it is determined to be an unknown risk type, and the high-risk collection mode is started, increasing the collection frequency of the multimodal data collection module to the maximum; Specifically, the abnormal intervention unit also includes a risk verification subunit. The main responsibility of the risk verification subunit is to evaluate and output the similarity level between the current abnormal state and the historical abnormal state by comparing and analyzing the current immune status data with the historical immune status data (specifically, it can be achieved through cosine similarity or other suitable algorithms). When performing such comparative analysis, if it is found that the similarity level between the current abnormal state and the historical abnormal state exceeds a preset similarity threshold (not less than 95%), then the current abnormality will be considered to belong to a known risk type. At this time, the collection frequency of the multimodal data collection module will be adjusted accordingly, and it will be adjusted to the same collection frequency as the historical abnormal state to ensure that key data can be captured in time. On the contrary, if the similarity level between the current abnormal state and the historical abnormal state does not exceed the preset similarity threshold, then it will be determined that the current abnormality belongs to an unknown risk type. In this case, a high-risk collection mode will be activated to deal with potential unknown threats. In the high-risk collection mode, the collection frequency of the multimodal data collection module will be increased to the maximum value, thereby ensuring that as much data as possible can be collected to facilitate in-depth analysis and research to identify and understand this new risk type.

[0035] See also Figure 2 , an electronic device, the electronic device comprising: at least one processor; and a memory communicatively coupled to the at least one processor; The memory stores a computer program that can be executed by at least one processor, and the computer program is executed by at least one processor so that the at least one processor can implement the above-mentioned quantitative assessment system for the immune status of patients with thymoma and myasthenia gravis.

[0036] The processor of the above-mentioned electronic device may be a central processing unit (CPU), a graphics processing unit (GPU) or an application-specific integrated circuit (ASIC), and the memory may be a random access memory (RAM), a read-only memory (ROM) or other forms of non-volatile memory. The electronic device may also include an arithmetic unit, an input device and an output device. The arithmetic unit is used to execute instructions in a computer program, the input device is used to receive user operation instructions, and the output device is used to display processing results or provide a user interaction interface.

[0037] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0038] The above is only a preferred embodiment of the present invention. It should be noted that, for those skilled in the art, several improvements and modifications can be made without departing from the principles of the present invention, and these improvements and modifications should also be considered as the protection scope of the present invention. The structures, devices and operating methods not specifically described and explained in the present invention shall be implemented according to the conventional means in the art unless otherwise specified and limited.

Claims

1. A quantitative assessment system for the immune status of patients with thymoma and myasthenia gravis, characterized by: include: A multimodal data collection module, used to collect immune index data of patients, wherein the immune index data includes T cell subset ratio, B cell number and autoantibody titer; The feature extraction module is used to extract key features from the collected immune index data, add feature codes simultaneously, and output them as base feature parameters; An evaluation module is used to quantitatively score the patient's immune status based on the baseline characteristic parameters and output abnormal confidence; The intervention module is used to trigger differentiated intervention strategies based on quantitative scores and abnormal confidence levels. The differentiated intervention strategies include trend prediction of normal states, output of intervention data based on prediction deviations, abnormal level classification of abnormal states, risk verification based on abnormal levels, and dynamic adjustment of the collection frequency of the multimodal data collection module.

2. A quantitative evaluation system for the immune status of patients with thymoma and myasthenia gravis according to claim 1, characterized in that: The data acquisition module includes a preprocessing unit, which is used to preprocess the immune index data, including: Acquire the immune index data, and perform denoising processing to eliminate abnormal values ​​and noise in the immune index data; Normalizing the denoised immune index data to unify the magnitude of the immune index data; The normalized immune index data are classified and summarized, and output as multiple immune index data subsets.

3. A quantitative evaluation system for the immune status of patients with thymoma and myasthenia gravis according to claim 2, characterized in that: The feature extraction module includes a feature extraction unit and a feature encoding unit. The feature extraction unit is preset with a demand feature template. By comparing the demand feature template with the preprocessed immune index data, key features that are consistent with the demand feature template are extracted from the preprocessed immune index data. The feature encoding unit is used to encode the extracted key features, generate corresponding feature codes, and output them as reference feature parameters. Wherein, the feature code is numerical data.

4. The quantitative evaluation system for the immune status of patients with thymoma and myasthenia gravis according to claim 1, characterized in that: The evaluation module includes an evaluation unit and a confidence calculation unit, wherein the evaluation unit is used to receive the basal characteristic parameters, input them into a preset evaluation comparison table to perform matching analysis on the patient's immune status, and synchronously output the quantitative scores of each of the basal characteristic parameters; The confidence assessment unit is used to perform confidence assessment on the abnormality of the patient's immune status based on the quantitative score and output abnormality confidence, wherein the abnormality confidence is used to indicate the credibility of the abnormality of the patient's immune status.

5. The quantitative evaluation system for the immune status of patients with thymoma and myasthenia gravis according to claim 4, characterized in that: The evaluation module also includes a status determination unit, which determines the patient's immune status according to the quantitative score and synchronously outputs a corresponding evaluation report; Wherein, the immune status includes a normal status and an abnormal status, and the evaluation report includes a normal evaluation report corresponding to the normal status and an abnormal evaluation report corresponding to the abnormal status; When the state determination unit is executed, all quantitative scores reflecting the patient's immune status are collected within a preset sampling period; The quantitative score within the sampling period is weighted and integrated with the anomaly confidence level to output a comprehensive score. By comparing the preset dynamic assessment threshold with the comprehensive score, the patient's immune status type is output; Wherein, when the comprehensive score is greater than the dynamic assessment threshold, it indicates that the corresponding basal characteristic parameter is normal, and the immune status of the corresponding patient is recorded as normal; When the comprehensive score is less than or equal to the dynamic evaluation threshold, it indicates that the corresponding basal characteristic parameter is abnormal, and the immune status of the corresponding patient is recorded as an abnormal state.

6. The quantitative evaluation system for the immune status of patients with thymoma and myasthenia gravis according to claim 1, characterized in that: The intervention module includes a normal intervention unit, which is used to perform trend prediction in a normal state; And, a summary unit and a statistical analysis unit, the summary unit is used to construct an immune status database, and summarize the basal characteristic parameters under normal conditions into the immune status database, and add a timestamp identifier to each of the basal characteristic parameters, the statistical analysis unit is used to perform statistical analysis on the basal characteristic parameters after adding the timestamp identifier, and determine the range of the immune status parameters under normal conditions.

7. A quantitative evaluation system for the immune status of patients with thymoma and myasthenia gravis according to claim 6, characterized in that: The normal intervention unit is used to record the baseline characteristic parameters with timestamp identifiers as baseline condition parameters, input the baseline condition parameters into a preset trend measurement function, and record the output result of the trend measurement function as trend condition parameters, output a trend prediction curve based on the trend condition parameters, and then calculate the safe time for the patient to be in a normal state based on the trend condition parameters. When the trend condition parameters deviate from the baseline, an early warning is triggered, the acquisition frequency of the multimodal data acquisition module is increased, and the safety time is updated synchronously.

8. The quantitative evaluation system for immune status of patients with thymoma and myasthenia gravis according to claim 1, characterized in that: The intervention module also includes an abnormal intervention unit, which includes a difference calculation subunit and an abnormal level determination subunit. The difference calculation subunit is used to receive the basal characteristic parameters under the abnormal state, and calculate the difference between the basal characteristic parameters and the standard characteristic parameters under the abnormal state, and output it as an abnormal difference. The abnormal level determination subunit determines the abnormal level of the patient's immune status according to the size of the abnormal difference and with reference to a preset abnormal level standard.

9. The quantitative evaluation system for the immune status of patients with thymoma and myasthenia gravis according to claim 8, characterized in that: The abnormal intervention unit also includes a risk verification subunit, which is used to compare and analyze the current immune status data with the historical immune status data, and output the similarity between the current abnormal state and the historical abnormal state; If the similarity between the current abnormal state and the historical abnormal state exceeds the preset similarity threshold, it is determined to be a known risk type, and the collection frequency of the multimodal data collection module is adjusted to the collection frequency under the historical abnormal state; If the similarity between the current abnormal state and the historical abnormal state does not exceed the preset similarity threshold, it is determined to be an unknown risk type, and the high-risk collection mode is started, increasing the collection frequency of the multimodal data collection module to the maximum.

10. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively coupled to the at least one processor; Wherein, the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the quantitative assessment system for the immune status of patients with thymoma and myasthenia gravis according to any one of claims 1 to 9.

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