Lung cancer whole course state information management method and system based on big data

Abstract

The invention discloses a lung cancer whole course state information management method and system based on big data, and relates to the technical field of state information management. The lung cancer whole course state information management method based on big data comprises the following steps: performing bilateral inspection; performing synchronous optimization; and evaluating the information management efficiency. Whether synchronous optimization is carried out or not is judged through the obtained bilateral inspection efficiency score, if synchronous optimization is carried out, the synchronous optimization index is obtained, whether the management prompt list is generated or not is judged based on the obtained synchronous optimization index, and if the management prompt list is generated, the management prompt list is not generated. If yes, acquiring an information management efficiency score and judging whether intelligent information management is performed again or not based on the acquired information management efficiency score, so that the effect of improving the synchronism of the state information data of the lung cancer patient in the whole disease course management process is achieved. The problem that in the prior art, the reliability of synchronous state evaluation of the state information data of the lung cancer patient in the whole disease course management process is not high is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of status information management, and in particular to a method and system for managing status information of the entire course of lung cancer based on big data. Background Art

[0002] With the continuous advancement of medical technology and the rapid development of big data technology, the treatment and management of lung cancer are gradually moving towards precision and personalization. As one of the malignant tumors with the highest morbidity and mortality rates in the world, the effective management of the entire course of lung cancer is of great significance for improving the survival rate of patients and improving their quality of life. Traditionally, the management of lung cancer mainly relies on the clinical experience of doctors and self-reporting by patients, but this method often has the problem of incomplete and untimely information. With the introduction of big data technology, it is possible to collect, integrate and analyze massive amounts of lung cancer patient data to achieve accurate monitoring and management of the entire course of lung cancer.

[0003] In the existing technology, the electronic health record system is used to integrate data from different channels to form a comprehensive health portrait of the individual patient. The big data is then analyzed through machine learning algorithms to identify treatment response patterns in the course of lung cancer. Finally, the patient's physiological indicators and disease course changes are monitored in real time. At the same time, the monitored data is transmitted to the receiving end of the medical team in real time, realizing real-time tracking of the patient's entire disease status.

[0004] For example, the invention patent with announcement number: CN118299063B announces a cardiovascular and cerebrovascular disease information management system for rehabilitation assistance, including: a management center communication connection with a cardiovascular and cerebrovascular disease information collection module, a network construction module and a treatment analysis module; the cardiovascular and cerebrovascular disease information collection module is used to collect historical cardiovascular and cerebrovascular disease information; the network construction module is used to construct a symptom countermeasure network based on the collected historical cardiovascular and cerebrovascular disease information; the treatment analysis module is used to obtain a cardiovascular and cerebrovascular disease treatment plan for the patient based on the constructed symptom countermeasure network.

[0005] For example, the invention patent with announcement number: CN115798715B announces a chronic respiratory disease prevention and treatment information management system based on data analysis, including: collecting basic information of patients, screening symptoms, and automatically generating screening results; evaluating patients' symptoms and conducting initial screening, and after the diagnosis and evaluation of the patients are completed, automatically generating the patients' annual follow-up plan based on the diagnosis and evaluation results; after obtaining the patient's authorization, continuously obtaining the patient's real-time GPS information, and the health room and health center where the real-time GPS information is located will send notification reminder information, the address information of the health room and health center, and the communication method to the patient's WeChat applet.

[0006] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems:

[0007] In the existing technology, due to the differences in the format, standard and accuracy of data from different channels in big data, the integration process of data from different channels in the electronic health record system may be different, which reduces the quality of data integration. Secondly, the generalization ability of machine learning algorithms in identifying treatment response patterns in the course of lung cancer is affected by the quality of data integration, which in turn leads to the inability of real-time monitored data to timely reflect the patient's latest status. There is a problem of low reliability of synchronous status assessment of lung cancer patients' status information data during the entire disease management process. Summary of the invention

[0008] The embodiments of the present application solve the problem of low reliability of synchronous status evaluation of status information data of lung cancer patients in the whole course of disease management in the prior art by providing a method and system for managing status information of lung cancer throughout the whole course of disease management based on big data, and achieve improved synchronization of status information data of lung cancer patients in the whole course of disease management.

[0009] The embodiment of the present application provides a method for managing status information of lung cancer throughout the entire course based on big data, comprising the following steps: step 1, respectively obtaining status information data of patients in the experimental group and patients in the control group and performing a bilateral test to obtain a bilateral test efficiency score, and judging whether to perform synchronous optimization based on the obtained bilateral test efficiency score, the bilateral test efficiency score is used to quantify the detection efficiency of the degree of difference between the experimental group and the control group in terms of status information; step 2, if synchronous optimization is performed, real-time monitoring of changes in status information data during the synchronous optimization process to obtain synchronous optimization indicators, and judging whether to generate a management prompt list based on the obtained synchronous optimization indicators, the synchronous optimization indicators are used to quantify changes in the degree of difference between the experimental group and the control group in terms of status information; step 3, if a management prompt list is generated, evaluating the effect of intelligent information management according to the obtained management prompt list to obtain an information management efficiency score, and judging whether to re-perform intelligent information management based on the obtained information management efficiency score, the information management efficiency score is used to quantify the information management efficiency of the status information data of patients in the experimental group and the control group.

[0010] Furthermore, the bilateral test efficiency score is obtained by the following method: E1, classify and number the patients in the experimental group and the control group respectively, monitor the chi-square value of the status information data in the chi-square verification process in real time, and search the corresponding probability value in the database according to the obtained chi-square value and the corresponding statistical distribution; E2, determine whether the obtained probability value is less than the preset significance level, if so, execute E3, otherwise re-acquire the first status information data and record the bilateral test efficiency score as 0; E3, obtain the effect size score and the synchronous efficiency score, and combine the obtained probability value and the bilateral test efficiency weight factor in the database to obtain the bilateral test efficiency score.

[0011] Furthermore, the probability value is used to measure the significance of the difference between the treatment course state and the natural course state; the effect size score represents the ratio of the difference between the mean effect size of the experimental group and the mean effect size of the control group to the mean effect size of the control group; the difference between the mean effect size of the experimental group and the mean effect size of the control group is greater than the reference mean deviation in the database; the synchronous efficiency score represents the ratio of the two-sided test response deviation to the maximum allowable two-sided test response deviation in the database; the two-sided test response deviation is used to reflect the response time deviation between the first state information data and the second state information data in the two-sided test process; the two-sided test efficiency weight factor includes the effect size score weight factor and the synchronous efficiency score weight factor.

[0012] Furthermore, the synchronization optimization index is obtained by the following method: U1, when the obtained bilateral inspection efficiency score is less than the bilateral inspection efficiency score preset in the database, the initial synchronization optimization parameters before the synchronization optimization are obtained, and the actual synchronization optimization parameters at the end of the preset synchronization optimization period are obtained; U2, the probability value change at the end of the preset synchronization optimization period is obtained, and it is determined whether the obtained probability value change is greater than the probability value change preset in the database. If so, execute U3, otherwise re-perform synchronization optimization; U3, obtain the throughput coefficient and the parallel processing coefficient, and combine the obtained probability value change, the synchronization efficiency score and the synchronization optimization parameters in the database. The weight factor obtains the synchronization optimization index; the initial synchronization optimization parameters include the initial throughput data volume and the initial parallel processing duration; the actual synchronization optimization parameters include the actual throughput data volume and the actual parallel processing duration; the probability value change represents the difference between the acquired probability value and the actual probability value at the end of the preset synchronization optimization period; the throughput coefficient is used to reflect the access frequency of the first state information data within the preset synchronization optimization period; the parallel processing coefficient is used to reflect the parallel processing efficiency of the number of accesses of the first state information data within the preset synchronization optimization period; the synchronization optimization parameter weight factor includes the throughput coefficient weight factor and the parallel processing coefficient weight factor.

[0013] Furthermore, the information management efficiency score is obtained by the following method: V1, when the obtained synchronization optimization index is greater than the synchronization optimization index preset in the database, the comparative monitoring response time of the status information data group in the intelligent information management process is obtained, and at the same time, it is determined whether the obtained comparative monitoring response time is not greater than the reference comparative monitoring response time in the database. If so, V2 is executed, otherwise V3 is executed; V2, the first response time coefficient is obtained, and the information management efficiency score is obtained by combining the obtained bilateral inspection efficiency score, the synchronization optimization index and the first information management efficiency weight factor; V3, the second response time coefficient is obtained, and the information management efficiency score is obtained by combining the obtained bilateral inspection efficiency score, the synchronization optimization index and the second information management efficiency weight factor; the comparative monitoring response time represents the synchronous response time of the status information data group in the monitoring process; the first information management efficiency weight factor includes the first response time coefficient weight factor, the bilateral inspection efficiency score weight factor and the synchronization optimization index weight factor; the second information management efficiency weight factor includes the second response time coefficient weight factor, the bilateral inspection efficiency score weight factor and the synchronization optimization index weight factor.

[0014] The embodiment of the present application provides a lung cancer full-course state information management system based on big data, including: a bilateral inspection module, a synchronous optimization module and an information management effect evaluation module; wherein the bilateral inspection module is used to respectively obtain the state information data of the experimental group patients and the control group patients and perform bilateral inspection to obtain the bilateral inspection efficiency score, and at the same time determine whether to perform synchronous optimization based on the obtained bilateral inspection efficiency score, and the bilateral inspection efficiency score is used to quantify the detection efficiency of the degree of difference between the experimental group and the control group in terms of state information; the synchronous optimization module is used to monitor the change of the state information data in the synchronous optimization process in real time to obtain the synchronous optimization index if synchronous optimization is performed, and at the same time determine whether to generate a management prompt list based on the obtained synchronous optimization index, and the synchronous optimization index is used to quantify the change of the degree of difference between the experimental group and the control group in terms of state information; the information management effect evaluation module is used to evaluate the effect of intelligent information management according to the obtained management prompt list to obtain the information management efficiency score if a management prompt list is generated, and at the same time determine whether to re-perform intelligent information management based on the obtained information management efficiency score, and the information management efficiency score is used to quantify the information management efficiency of the state information data of the experimental group and the control group patients.

[0015] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. Determine whether to perform synchronous optimization through the obtained bilateral inspection efficiency score. If synchronous optimization is performed, obtain the synchronous optimization index and determine whether to generate a management prompt list based on the obtained synchronous optimization index. If a management prompt list is generated, obtain the information management efficiency score and determine whether to re-perform intelligent information management based on the obtained information management efficiency score, thereby improving the accuracy of bilateral inspection and synchronous optimization, and further improving the synchronization of the status information data of lung cancer patients in the whole course of disease management, effectively solving the problem of low reliability of synchronous status evaluation of the status information data of lung cancer patients in the whole course of disease management in the prior art.

[0016] 2. By real-time monitoring of the chi-square values ​​of the experimental group patients and the control group patients during the chi-square verification process after classification and numbering, the corresponding probability values ​​are searched in the database according to the obtained chi-square values ​​and the corresponding statistical distribution. When the obtained probability value is less than the preset significance level, the effect size score and the synchronous efficiency score are obtained. At the same time, the obtained probability value and the bilateral test efficiency weight factor in the database are combined to obtain the bilateral test efficiency score, thereby improving the accuracy of obtaining the bilateral test efficiency score and achieving a more accurate assessment of the degree of difference in state information.

[0017] 3. By obtaining the change in probability value at the end of the preset synchronization optimization period, when the obtained change in probability value is greater than the preset change in probability value in the database, the throughput coefficient and the parallel processing coefficient are obtained, and the synchronization optimization index is obtained by combining the obtained change in probability value, the synchronization efficiency score and the synchronization optimization parameter weight factor in the database, thereby achieving an improvement in the accuracy of obtaining the synchronization optimization index, and then achieving a more accurate assessment of the change in the degree of difference in status information. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A flowchart of a method for managing lung cancer full-course status information based on big data provided in an embodiment of the present application; Figure 2 A flowchart of intelligent management of status information data provided by an embodiment of the present application; Figure 3 A schematic diagram of the structure of a lung cancer full-course status information management system based on big data provided in an embodiment of the present application. DETAILED DESCRIPTION

[0019] The embodiment of the present application solves the problem of low reliability of synchronous status evaluation of status information data of lung cancer patients during the whole course of disease management in the prior art by providing a method and system for managing status information of lung cancer throughout the disease course based on big data. The method obtains status information data of patients in the experimental group and patients in the control group respectively and performs a bilateral test to obtain a bilateral test efficiency score. At the same time, it is judged whether to perform synchronous optimization based on the obtained bilateral test efficiency score. If synchronous optimization is performed, the changes of status information data during the synchronous optimization process are monitored in real time to obtain synchronous optimization indicators. Then, based on the obtained synchronous optimization indicators, it is judged whether to generate a management prompt list. If a management prompt list is generated, the effect of intelligent information management is evaluated according to the obtained management prompt list to obtain the information management efficiency score. Finally, based on the obtained information management efficiency score, it is judged whether to perform intelligent information management again, thereby improving the synchronization of status information data of lung cancer patients during the whole course of disease management.

[0020] The technical solution in the embodiment of the present application is to solve the problem that the reliability of the synchronous status evaluation of the status information data of the lung cancer patient in the whole course of disease management is not high. The overall idea is as follows: The obtained two-sided test efficiency score is used to determine whether to perform synchronous optimization. If synchronous optimization is performed, the synchronous optimization index is obtained and based on the obtained synchronous optimization index, it is determined whether to generate a management prompt list. If a management prompt list is generated, the information management efficiency score is obtained and based on the obtained information management efficiency score, it is determined whether to re-perform intelligent information management, thereby achieving the effect of improving the synchronization of the status information data of lung cancer patients throughout the entire disease management process.

[0021] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0022] like Figure 1As shown, it is a flow chart of a lung cancer full-course state information management method based on big data provided by an embodiment of the present application. The lung cancer full-course state information management method based on big data provided by an embodiment of the present application includes the following steps: Step 1, respectively obtaining state information data of patients in the experimental group and patients in the control group and performing a bilateral test to obtain a bilateral test efficiency score, and judging whether to perform synchronous optimization based on the obtained bilateral test efficiency score, and the bilateral test efficiency score is used to quantify the detection efficiency of the degree of difference between the experimental group and the control group in state information; Step 2, if synchronous optimization is performed, real-time monitoring of the change of state information data during the synchronous optimization process to obtain a synchronous optimization index, and judging whether to generate a management prompt list based on the obtained synchronous optimization index, and the synchronous optimization index is used to quantify the change of the degree of difference between the experimental group and the control group in state information; Step 3, if a management prompt list is generated, the effect of intelligent information management is evaluated according to the obtained management prompt list to obtain an information management efficiency score, and judging whether to re-perform intelligent information management based on the obtained information management efficiency score, and the information management efficiency score is used to quantify the information management efficiency of the state information data of patients in the experimental group and the control group.

[0023] Among them, the patients in the experimental group represent the patient group who received intervention treatment; the patients in the control group represent the patient group who did not receive intervention treatment; the state information data include first state information data and second state information data; the first state information data is used to reflect the treatment course status of the patients in the experimental group after receiving intervention treatment; the second state information data is used to reflect the natural course status of the patients in the control group without receiving intervention treatment; the two-sided test is used to identify the degree of difference between the treatment course status and the natural course status; the two-sided test includes t-test and chi-square test.

[0024] In this embodiment, the status information data of the experimental group patients and the control group patients are obtained through the electronic data capture system in the big data. The number of patients in the experimental group patients and the control group patients is the same, which is set by the preset user personnel and has a one-to-one correspondence, that is, the patient numbered 1 in the experimental group patients is compared with the patient numbered 1' in the control group patients; Figure 2As shown, it is a flow chart of the intelligent management of status information data provided in the embodiment of the present application. Through the bilateral test efficiency score and the synchronous optimization index, the difference degree and change of the status information of the experimental group and the control group in the whole course of lung cancer can be accurately quantified. This quantitative analysis provides doctors with objective and accurate data support, which helps to understand the changing trend of the patient's status more accurately. Secondly, through the calculation of the information management efficiency score, the effect of intelligent information management can be dynamically evaluated, and it can be judged whether intelligent information management needs to be re-performed according to the evaluation results. This dynamic evaluation mechanism ensures the continuous optimization and improvement of the information management method, thereby realizing the improvement of the synchronization of the status information data of lung cancer patients in the whole course of disease management.

[0025] Furthermore, the bilateral test efficiency score is obtained by the following method: E1, classify and number the patients in the experimental group and the control group respectively, monitor the chi-square value of the status information data in the chi-square verification process in real time, and search the corresponding probability value in the database according to the obtained chi-square value and the corresponding statistical distribution; E2, determine whether the obtained probability value is less than the preset significance level. If so, it is considered that there is a significant difference between the treatment course state and the natural course state and execute E3. Otherwise, it is considered that the degree of difference between the treatment course state and the natural course state does not meet the requirements of synchronous optimization. At this time, the first state information data is re-obtained and the bilateral test efficiency score is recorded as 0; E3, obtain the effect size score and the synchronous efficiency score, and combine the obtained probability value and the bilateral test efficiency weight factor in the database to obtain the bilateral test efficiency score.

[0026] Among them, the probability value is used to measure the significance of the difference between the treatment course state and the natural course state; the effect size score represents the ratio of the difference between the mean effect size of the experimental group and the mean effect size of the control group to the mean effect size of the control group; the difference between the mean effect size of the experimental group and the mean effect size of the control group is greater than the reference mean deviation in the database; the synchronous efficiency score represents the ratio of the two-sided test response deviation to the maximum allowable two-sided test response deviation in the database; the two-sided test response deviation is used to reflect the response time deviation between the first state information data and the second state information data in the two-sided test process; the two-sided test response deviation is not less than the reference two-sided test response deviation in the database; the two-sided test efficiency weight factor includes the effect size score weight factor and the synchronous efficiency score weight factor.

[0027] The specific restricted expression of the two-sided test efficiency score is: ; ; ; In the formula, e is a natural constant, It represents the two-sided test efficiency score of the state information data during the two-sided test. It represents the probability value of the status information data of the experimental group and the control group during the two-sided test. represents the preset significance level, represents the effect size score weight factor, It represents the effect size score of the status information data of the experimental group and the control group during the two-sided test. It represents the mean effect size of the experimental group in the process of two-sided test of the first state information data. It represents the mean effect size of the control group in the two-sided test of the second state information data. represents the synchronization efficiency score weight factor, It represents the synchronization efficiency score of the status information data of the experimental group and the control group during the two-sided test. It represents the two-sided test response deviation of the status information data of the experimental group and the control group during the two-sided test. represents the maximum allowable two-sided test response deviation.

[0028] In this embodiment, the preset significance level is usually set to 0.05, and the mean effect size of the control group is not equal to 0. In statistics, the abbreviation of probability value is usually P value. In medical research and clinical trials, the mean effect size is a key indicator for evaluating treatment effects and determining whether the treatment is effective.

[0029] The reference mean deviation is represented by the sum and average of the historical experimental group effect size means and the historical control group effect size means in the database. The reference two-sided test response deviation is represented by the sum and average of the historical two-sided verification deviations of the historical status information data in the database during the two-sided test process. The maximum allowable two-sided test response deviation represents the maximum value of the historical two-sided verification deviations of the historical status information data in the database during the two-sided test process.

[0030] The database stores preset weight factors that are closely related to the bilateral test efficiency scores. A predefined mapping relationship is established between these weight factors and the corresponding bilateral test efficiency scores. It is worth noting that this mapping is not set arbitrarily. It can be one-to-one or many-to-one to adapt to the complex impact of different bilateral test scenarios on the evaluation results. In practical applications, the effect size score and synchronous efficiency score obtained in real time can be directly input into this preset mapping relationship, which can quickly and accurately extract the effect size score weight factor and synchronous efficiency score weight factor that match the current bilateral test efficiency score. In order to ensure the consistency and comparability of the evaluation, the value range of the effect size score weight factor and the synchronous efficiency score weight factor in this example are limited to between 0 and 1, and the sum of the effect size score weight factor and the synchronous efficiency score weight factor is 1.

[0031] The aforementioned database is a database for storing various types of set data established before the design of the big data-based lung cancer full-course status information management method. The database includes but is not limited to preset bilateral test efficiency scores, preset probability value changes, preset synchronization optimization indicators, preset information management efficiency scores, and preset synchronization optimization time periods. Various numerical values ​​therein are directly set by technical personnel. Among them, the setting basis of the preset bilateral test efficiency score can be determined according to the actual bilateral test scenario of the status information data. For example, the preset bilateral test efficiency score is represented by the result of summing and averaging the historical bilateral test efficiency scores of the historical status information data in the database during the bilateral test process. In addition, various numerical values ​​in the database can be set and fine-tuned by technical personnel according to actual debugging.

[0032] Specifically, when When , the statistical table of changes in the two-sided test efficiency score is shown in Table 1: Table 1 Statistics of changes in two-sided test efficiency scores

[0033] It should be understood that, from the first, second and third groups of data in Table 1, it can be seen that the two-sided test efficiency score decreases with the increase of the probability value and the synchronous efficiency score. From the fourth and fifth groups of data in Table 1, it can be seen that the two-sided test efficiency score increases with the increase of the effect size score. Among them, the effect size score increases with the effect size deviation (i.e. ) increases, and the synchronous efficiency score increases with the increase of the two-sided test response deviation.

[0034] It should be noted that when When the probability value decreases, the change in probability value indirectly affects the value of the bilateral test efficiency score by affecting the value of the effect size score and the synchronous efficiency score. When the probability value decreases, that is, the difference between the treatment course state and the natural course state is more significant, it usually means that the difference between the experimental group and the control group increases, which may lead to an increase in the effect size score.

[0035] Although the probability value and effect size score are both key indicators that reflect the degree of difference between the treatment course status and the natural course status, there is no direct linear relationship between them, because the probability value is also affected by the sample size of the status information data and the distribution of the effect size.

[0036] The effect size score also indirectly affects the value of the synchronization efficiency score. When the probability value increases, that is, the difference between the treatment course state and the natural course state decreases, the corresponding effect size score also decreases accordingly. At this time, it indicates that the quality of obtaining state information data is improved, that is, the collection of state information data is more accurate, which increases the synchronization efficiency score.

[0037] By considering the above-mentioned indirect influence mechanism, we can have a deeper understanding of the relationship between the probability value, effect size score and synchronization efficiency score, which is helpful to more accurately evaluate the status information of lung cancer patients, thereby achieving the improvement of the synchronization of the status information data of lung cancer patients in the whole disease management process, and effectively solving the problem of low reliability of the synchronization status evaluation of the status information data of lung cancer patients in the whole disease management process in the prior art.

[0038] Furthermore, the specific process for determining whether to perform synchronous optimization based on the obtained bilateral inspection efficiency score is as follows: determine whether the obtained bilateral inspection efficiency score is less than the bilateral inspection efficiency score preset in the database: L1, if the obtained bilateral inspection efficiency score is less than the bilateral inspection efficiency score preset in the database, then send a synchronous optimization instruction and perform synchronous optimization, otherwise execute L2; L2, determine whether the obtained bilateral inspection efficiency score is equal to the bilateral inspection efficiency score preset in the database, then send a warning notification instruction, otherwise send a continue monitoring instruction; the warning notification instruction is used to prompt the preset personnel that the bilateral inspection efficiency has reached the critical value of the expected bilateral inspection efficiency.

[0039] In this embodiment, by comparing the numerical relationship between the bilateral inspection efficiency score and the preset bilateral inspection efficiency score, it is helpful to achieve personalized management of the status information data synchronization efficiency. By sending early warning notification instructions, preset personnel can take measures in advance to avoid efficiency decline, thereby reducing the risk of medical decision-making caused by data synchronization problems and improving the quality and reliability of status information data.

[0040] Furthermore, the synchronization optimization index is obtained by the following method: U1, when the obtained bilateral inspection efficiency score is less than the bilateral inspection efficiency score preset in the database, the initial synchronization optimization parameters before the synchronization optimization are obtained, and the actual synchronization optimization parameters at the end of the preset synchronization optimization period are obtained; U2, the probability value change at the end of the preset synchronization optimization period is obtained, and it is determined whether the obtained probability value change is greater than the probability value change preset in the database. If so, U3 is executed, otherwise the synchronization optimization is performed again; U3, the throughput coefficient and the parallel processing coefficient are obtained, and the synchronization optimization index is obtained by combining the obtained probability value change, the synchronization efficiency score and the synchronization optimization parameter weight factor in the database; the initial synchronization optimization parameters include the initial throughput data volume and the initial parallel processing duration; the actual synchronization optimization parameters include the actual throughput data volume and the actual parallel processing duration; the probability value change represents the difference between the obtained probability value and the actual probability value at the end of the preset synchronization optimization period; the throughput coefficient is used to reflect the access frequency of the first state information data within the preset synchronization optimization period; the parallel processing coefficient is used to reflect the parallel processing efficiency of the number of accesses of the first state information data within the preset synchronization optimization period; the synchronization optimization parameter weight factor includes the throughput coefficient weight factor and the parallel processing coefficient weight factor.

[0041] Among them, the specific restriction expression of the synchronous optimization index is: ; ; ; Where r is the batch number of the preset synchronization optimization period, , R is the total number of preset synchronization optimization periods, e is a natural constant, represents the synchronization optimization index of the first state information data within the preset synchronization optimization period, It represents the two-sided test efficiency score of the status information data of the experimental group and the control group during the two-sided test. represents the preset two-sided test efficiency score, It represents the synchronization efficiency score of the status information data of the experimental group and the control group during the two-sided test. represents the probability value change of the first state information data at the end of the rth preset synchronization optimization period, Indicates the preset probability value change, represents the throughput coefficient weight factor, represents the throughput coefficient of the first state information data in the rth preset synchronization optimization period, represents the actual throughput data volume of the first state information data at the end of the rth preset synchronization optimization period, represents the initial throughput data volume of the first state information data before synchronization optimization, represents the parallel processing coefficient weight factor, represents the parallel processing coefficient of the first state information data in the rth preset synchronization optimization period, represents the actual parallel processing duration of the first state information data at the end of the rth preset synchronization optimization period, Indicates the initial parallel processing duration of the first state information data before synchronous optimization.

[0042] In this embodiment, the preset probability value change is usually set to 0.015, the initial throughput data volume and the actual throughput data volume are obtained through a performance monitoring tool (such as JMeter), and the initial parallel processing time and the actual parallel processing time are obtained through a parallel performance analyzer.

[0043] The throughput coefficient weight factor and the parallel processing coefficient weight factor are the influence of the throughput coefficient and the parallel processing coefficient preset in the database on the synchronous optimization index acquisition process. Specifically, the database stores preset weight factors corresponding to the synchronous optimization index. There is a pre-set mapping relationship between these weight factors and the synchronous optimization index. This mapping relationship can be one-to-one or many-to-one. In practical applications, the real-time throughput coefficient and parallel processing coefficient can be input into this mapping relationship to quickly obtain the corresponding weight factor, which provides an important quantitative indicator for evaluating the difference degree change of the state information, and then more accurately calculates the synchronous optimization index.

[0044] In this example, the value ranges of the throughput coefficient weight factor and the parallel processing coefficient weight factor are both limited to between 0 and 1, and the sum of the two is 1.

[0045] It should be understood that the synchronization optimization index decreases with the increase of the change in the synchronization efficiency score and the probability value, and increases with the increase of the throughput coefficient and the parallel processing coefficient. Among them, the throughput coefficient increases with the increase of the actual throughput data volume, and the parallel processing coefficient decreases with the increase of the actual parallel processing time.

[0046] It should be noted that the synchronization efficiency score also indirectly affects the value of the probability value change. The improvement of the synchronization efficiency score is usually accompanied by the improvement of data quality, which means that the error in the experimental data is reduced. In statistical analysis, the reduction of errors usually leads to a decrease in the change of probability values, because when the data is more accurate, the fluctuation of the difference between the observed data and the original hypothesis (that is, the test statistic) will decrease, which will also reduce the change of the probability value.

[0047] The throughput coefficient also indirectly affects the value of the parallel processing coefficient. An increase in the throughput coefficient usually means an increase in the amount of data processed in the same synchronization optimization period. At this time, the parallel processing tasks may require more sophisticated allocation and scheduling to ensure the smooth completion of the tasks. When multiple parallel processing tasks run at the same time, they may compete for limited system resources (such as CPU, memory, and network bandwidth). This resource competition will intensify, resulting in a decrease in the execution speed of parallel processing tasks, thereby reducing the parallel processing coefficient.

[0048] By considering the above-mentioned indirect influence mechanism, it is helpful to have a deeper understanding of the relationship between the synchronization efficiency score, the change in probability value, the throughput coefficient and the parallel processing coefficient. These relationships are crucial for optimizing the synchronous state evaluation of the status information data of lung cancer patients in the whole process of disease management. By optimizing data synchronization, improving data quality and reasonably allocating parallel processing tasks, the synchronization of the status information data of lung cancer patients in the whole process of disease management is improved, which effectively solves the problem of low reliability of the synchronous state evaluation of the status information data of lung cancer patients in the whole process of disease management in the prior art.

[0049] Furthermore, the specific process of determining whether to generate a management prompt list based on the acquired synchronization optimization index is as follows: determining whether the acquired synchronization optimization index is greater than the synchronization optimization index preset in the database: if the acquired synchronization optimization index is greater than the synchronization optimization index preset in the database, the synchronization optimization is completed and a management prompt list is generated; if the acquired synchronization optimization index is not greater than the synchronization optimization index preset in the database, a synchronization optimization maintenance instruction is sent; the management prompt list is used to visualize the self-management suggestions corresponding to the current intervention treatment effect of the patients in the experimental group.

[0050] In this embodiment, the synchronous optimization maintenance instruction is used to prompt the preset personnel to check the synchronous optimization process, and the preset synchronous optimization index is represented by the result of summing and averaging the historical synchronous optimization index of the first state information data in the database in each historical synchronous optimization period; this example automatically inputs the relevant data into the statistical table in the database by comparing the numerical relationship between the synchronous optimization index and the preset synchronous optimization index, and generates a management prompt list, wherein the relevant data includes but is not limited to the task name, the dosage of medicines used by lung cancer patients, and the effect of intervention treatment on lung cancer patients, and can provide timely feedback on the current intervention treatment effect to the patients in the experimental group or their guardians, which helps to improve the efficiency and quality of medical services.

[0051] Furthermore, the information management efficiency score is obtained by the following method: V1, when the obtained synchronization optimization index is greater than the synchronization optimization index preset in the database, the comparative monitoring response time of the status information data group in the intelligent information management process is obtained, and at the same time, it is determined whether the obtained comparative monitoring response time is not greater than the reference comparative monitoring response time in the database. If so, V2 is executed, otherwise V3 is executed; V2, the first response time coefficient is obtained, and the information management efficiency score is obtained by combining the obtained bilateral inspection efficiency score, the synchronization optimization index and the first information management efficiency weight factor; V3, the second response time coefficient is obtained, and the information management efficiency score is obtained by combining the obtained bilateral inspection efficiency score, the synchronization optimization index and the second information management efficiency weight factor; Among them, the number of status information data groups represents half of the sum of the total number of patients in the experimental group and the total number of patients in the control group; the comparative monitoring response time represents the synchronous response time of the status information data group during the monitoring process; the first information management efficiency weight factor includes the first response time coefficient weight factor, the two-sided inspection efficiency score weight factor and the synchronous optimization index weight factor; the second information management efficiency weight factor includes the second response time coefficient weight factor, the two-sided inspection efficiency score weight factor and the synchronous optimization index weight factor; the first response time coefficient and the second response time coefficient respectively represent the ratio of the comparative monitoring response time to the maximum allowed comparative monitoring response time in the database.

[0052] The specific restricted expression of the information management efficiency score is: ; Where j is the batch number of the status information data group, , X is the total number of status information data groups, Indicates the information management efficiency score of the status information data group in the intelligent information management process, represents the weight factor of the synchronous optimization indicator, represents the synchronization optimization index of the first state information data within the preset synchronization optimization period, Indicates the preset synchronization optimization indicator. represents the two-sided test efficiency score weight factor, It represents the two-sided test efficiency score of the status information data of the experimental group and the control group during the two-sided test. represents the first response time coefficient weight factor, represents the first response time coefficient of the jth status information data group in the intelligent information management process, represents the comparative monitoring response time of the jth status information data group in the intelligent information management process, Indicates the reference comparison monitoring response time. represents the weight factor of the second response duration coefficient, It represents the second response time coefficient of the j-th status information data group in the intelligent information management process.

[0053] In this embodiment, intelligent information management includes bilateral inspection management and synchronous optimization management. The comparative monitoring response time is obtained through a timer. The reference comparative monitoring response time is represented by the sum and average of the historical comparative monitoring response times of each state information data in the database in the historical intelligent information management process. The maximum allowable comparative monitoring response time represents the maximum value of the historical comparative monitoring response time of each state information data in the database in the historical intelligent information management process.

[0054] The first response time coefficient weight factor and the second response time coefficient weight factor are the influence of the first response time coefficient and the second response time coefficient respectively preset in the database on the process of obtaining the information management efficiency score. Specifically, the database stores preset weight factors corresponding to the information management efficiency score. There is a pre-set mapping relationship between these weight factors and the information management efficiency score. This mapping relationship can be one-to-one or many-to-one. In practical applications, the real-time first response time coefficient and the second response time coefficient can be input into this mapping relationship to quickly obtain the corresponding weight factors, which provides an important quantitative indicator for evaluating the response rate of the status information data group in the intelligent information management process, and then more accurately calculates the information management efficiency score.

[0055] The weight factor of the bilateral inspection efficiency score and the weight factor of the synchronous optimization index are the influence of the bilateral inspection efficiency score and the synchronous optimization index preset in the database on the process of obtaining the information management efficiency score. Specifically, the database stores preset weight factors corresponding to the information management efficiency score. There is a pre-set mapping relationship between these weight factors and the information management efficiency score. This mapping relationship can be one-to-one or many-to-one. In practical applications, the real-time bilateral inspection efficiency score and synchronous optimization index can be input into this mapping relationship to quickly obtain the corresponding weight factor, which provides an important quantitative indicator for evaluating the intelligent information management rate, and then more accurately calculates the information management efficiency score.

[0056] It should be understood that the value ranges of the first response time coefficient weight factor, the second response time coefficient weight factor, the two-sided test efficiency score weight factor, and the synchronous optimization index weight factor are all limited to between 0 and 1. When , the sum of the weight factor of the first response time coefficient, the weight factor of the two-sided inspection efficiency score and the weight factor of the synchronous optimization index is 1, and the information management efficiency score increases with the increase of the first response time coefficient, the two-sided inspection efficiency score and the synchronous optimization index; when When , the sum of the weight factor of the second response time coefficient, the weight factor of the two-sided inspection efficiency score and the weight factor of the synchronous optimization index is 1, the information management efficiency score decreases with the increase of the second response time coefficient, and increases with the increase of the two-sided inspection efficiency score and the synchronous optimization index.

[0057] It should be noted that the bilateral inspection efficiency score and the synchronous optimization index also indirectly affect the values ​​of the first response time coefficient and the second response time coefficient. When the bilateral inspection efficiency score increases, the system's ability to identify abnormalities or key information will also be enhanced, which may cause the system to respond faster when a problem is detected, thereby shortening the first response time coefficient; when the synchronous optimization index increases, it means that when a request or problem is received, relevant resources can be mobilized faster for processing, which reduces the first response time coefficient. Similarly, when the bilateral inspection efficiency score increases and the synchronous optimization index increases, the second response time coefficient will also decrease accordingly.

[0058] By considering the above-mentioned indirect influence mechanism, we can have a more comprehensive understanding of the relationship between the information management efficiency score and each variable, which helps to improve the efficiency and accuracy in processing the status information data of lung cancer patients, thereby achieving the improvement of the synchronization of the status information data of lung cancer patients in the whole disease management process, and effectively solving the problem of low reliability of the synchronous status evaluation of the status information data of lung cancer patients in the whole disease management process in the prior art.

[0059] Furthermore, the specific process for determining whether to re-perform intelligent information management based on the acquired information management efficiency score is as follows: determine whether the acquired information management efficiency score is greater than the information management efficiency score preset in the database: if the acquired information management efficiency score is greater than the information management efficiency score preset in the database, the intelligent information management is completed; if the acquired information management efficiency score is not greater than the information management efficiency score preset in the database, a secondary intelligent information management instruction is sent.

[0060] In this embodiment, the secondary intelligent information management instruction is used to prompt the preset personnel to re-perform intelligent information management. The preset information management efficiency score is represented by the sum and average of the historical information management efficiency scores of each status information data in the database during the historical intelligent information management process; the process can be dynamically adjusted according to the real-time information management efficiency score, avoiding the inefficiency or waste of resources that may be caused by the fixed management strategy in the traditional method. Through intelligent judgment and decision-making, problems in the information management system can be discovered and solved in a timely manner, thereby improving the overall intelligent information management efficiency.

[0061] like Figure 3 As shown, it is a structural schematic diagram of a lung cancer full-course state information management system based on big data provided by an embodiment of the present application. The lung cancer full-course state information management system based on big data provided by an embodiment of the present application includes: a bilateral inspection module, a synchronous optimization module and an information management efficiency evaluation module; wherein the bilateral inspection module is used to respectively obtain the state information data of the experimental group patients and the control group patients and perform bilateral inspection to obtain a bilateral inspection efficiency score, and at the same time, determine whether to perform synchronous optimization based on the obtained bilateral inspection efficiency score, and the bilateral inspection efficiency score is used to quantify the detection efficiency of the degree of difference between the experimental group and the control group in state information; the synchronous optimization module is used to monitor the change of the state information data in the synchronous optimization process in real time to obtain a synchronous optimization index if synchronous optimization is performed, and at the same time, determine whether to generate a management prompt list based on the obtained synchronous optimization index, and the synchronous optimization index is used to quantify the change of the degree of difference between the experimental group and the control group in state information; the information management efficiency evaluation module is used to evaluate the effect of intelligent information management according to the obtained management prompt list to obtain an information management efficiency score if a management prompt list is generated, and at the same time, determine whether to re-perform intelligent information management based on the obtained information management efficiency score, and the information management efficiency score is used to quantify the information management efficiency of the state information data of the experimental group and the control group.

[0062] In this embodiment, the bilateral inspection module, the synchronous optimization module and the information management efficiency evaluation module together constitute a closed-loop information management system that can accurately quantify differences, monitor and adjust treatment plans in real time, automatically generate management prompts, evaluate information management effects, and continuously optimize and improve. This not only helps to provide doctors with specific optimization suggestions, such as increasing or decreasing the dosage of a certain drug, but also promotes the rational use of medical resources and the improvement of medical quality.

[0063] To summarize, the embodiment of the present application determines whether to perform synchronous optimization through the obtained bilateral inspection efficiency score. If synchronous optimization is performed, the synchronous optimization index is obtained and based on the obtained synchronous optimization index, it is determined whether to generate a management prompt list. If a management prompt list is generated, the information management efficiency score is obtained and based on the obtained information management efficiency score, it is determined whether to re-perform intelligent information management, thereby achieving improved accuracy of bilateral inspection and synchronous optimization, and further achieving improved synchronization of the status information data of lung cancer patients in the entire disease management process, effectively solving the problem of low reliability of synchronous status evaluation of the status information data of lung cancer patients in the prior art during the entire disease management process.

[0064] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0065] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0066] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0067] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0068] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0069] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for managing information on the entire course of lung cancer based on big data, characterized in that: The following steps are involved: Step 1, respectively obtaining the status information data of the patients in the experimental group and the patients in the control group and performing a bilateral test to obtain a bilateral test efficiency score, and judging whether to perform synchronous optimization based on the obtained bilateral test efficiency score, wherein the bilateral test efficiency score is used to quantify the detection efficiency of the difference degree between the experimental group and the control group in terms of status information; Step 2: If synchronous optimization is performed, the changes in the state information data during the synchronous optimization process are monitored in real time to obtain synchronous optimization indicators, and at the same time, based on the obtained synchronous optimization indicators, it is determined whether to generate a management prompt list, and the synchronous optimization indicators are used to quantify the changes in the degree of difference in state information between the experimental group and the control group; Step three: If a management prompt list is generated, the effect of intelligent information management is evaluated based on the obtained management prompt list to obtain an information management efficiency score. At the same time, based on the obtained information management efficiency score, it is determined whether to re-perform intelligent information management. The information management efficiency score is used to quantify the information management efficiency of the status information data of patients in the experimental group and the control group.

2. The method for managing lung cancer disease status information based on big data as claimed in claim 1, characterized in that: The state information data includes first state information data and second state information data; The first status information data is used to reflect the treatment course status of the patients in the experimental group after receiving the intervention treatment; The second state information data is used to reflect the natural course of the patients in the control group without receiving intervention treatment; The two-sided test is used to identify the degree of difference between the treatment course status and the natural course status; The two-sided tests include t-test and chi-square test.

3. The method for managing lung cancer whole course status information based on big data as claimed in claim 1, characterized in that: The two-sided test efficiency score is obtained by the following method: E1, classify and number the patients in the experimental group and the control group respectively, monitor the chi-square value of the status information data in the chi-square verification process in real time, and search the corresponding probability value in the database according to the obtained chi-square value and the corresponding statistical distribution; E2, determine whether the obtained probability value is less than the preset significance level, if so, execute E3, otherwise re-obtain the first state information data and record the two-sided test efficiency score as 0; E3, obtain the effect size score and the simultaneous efficiency score, and combine the obtained probability value and the two-sided test efficiency weight factor in the database to obtain the two-sided test efficiency score.

4. The method for managing lung cancer whole course status information based on big data as claimed in claim 3, characterized in that: The probability value is used to measure the significant difference between the treatment course state and the natural course state; The effect size score represents the ratio of the difference between the mean effect size of the experimental group and the mean effect size of the control group to the mean effect size of the control group; The difference between the mean effect size of the experimental group and the mean effect size of the control group is greater than the reference mean deviation in the database; The synchronization efficiency score represents the ratio of the two-sided test response deviation to the maximum allowed two-sided test response deviation in the database; The two-sided inspection response deviation is used to reflect the response time deviation between the first state information data and the second state information data during the two-sided inspection process; The two-sided test efficiency weight factor includes an effect size score weight factor and a simultaneous efficiency score weight factor.

5. The method for managing lung cancer whole course status information based on big data as claimed in claim 1, characterized in that: The specific process of determining whether to perform synchronous optimization based on the obtained two-sided test efficiency score is as follows: L1: If the obtained two-sided inspection efficiency score is less than the two-sided inspection efficiency score preset in the database, a synchronous optimization instruction is sent and synchronous optimization is performed, otherwise L2 is executed; L2, determine whether the obtained two-sided inspection efficiency score is equal to the two-sided inspection efficiency score preset in the database, then send an early warning notification instruction, otherwise send a continue monitoring instruction.

6. The method for managing lung cancer whole course status information based on big data as claimed in claim 1, characterized in that: The synchronization optimization index is obtained by the following method: U1, when the obtained two-sided inspection efficiency score is less than the two-sided inspection efficiency score preset in the database, the initial synchronization optimization parameters before the synchronization optimization are obtained, and the actual synchronization optimization parameters at the end of the preset synchronization optimization period are obtained; U2, obtain the probability value change at the end of the preset synchronization optimization period, and determine whether the obtained probability value change is greater than the probability value change preset in the database. If so, execute U3, otherwise re-perform synchronization optimization; U3, obtains the throughput coefficient and the parallel processing coefficient, and combines the obtained probability value change, the synchronization efficiency score and the synchronization optimization parameter weight factor in the database to obtain the synchronization optimization index; The initial synchronization optimization parameters include initial throughput data volume and initial parallel processing duration; The actual synchronization optimization parameters include actual throughput data volume and actual parallel processing duration; The probability value variation represents the difference between the acquired probability value and the actual probability value at the end of the preset synchronization optimization period; The throughput coefficient is used to reflect the access frequency of the first state information data within a preset synchronization optimization period; The parallel processing coefficient is used to reflect the parallel processing efficiency of the number of accesses to the first state information data within a preset synchronization optimization period; The synchronization optimization parameter weight factors include a throughput coefficient weight factor and a parallel processing coefficient weight factor.

7. The method for managing lung cancer whole course status information based on big data as claimed in claim 1, characterized in that: The specific process of determining whether to generate a management prompt list based on the acquired synchronization optimization index is as follows: Determine whether the obtained synchronization optimization index is greater than the synchronization optimization index preset in the database: If the obtained synchronization optimization index is greater than the synchronization optimization index preset in the database, the synchronization optimization is completed and a management prompt list is generated; If the acquired synchronization optimization index is not greater than the synchronization optimization index preset in the database, a synchronization optimization maintenance instruction is sent; The management prompt list is used to visualize the self-management suggestions corresponding to the current intervention treatment effects of the patients in the experimental group.

8. The method for managing lung cancer whole course status information based on big data as claimed in claim 1, characterized in that: The information management efficiency score is obtained by the following method: V1, when the obtained synchronization optimization index is greater than the synchronization optimization index preset in the database, obtain the comparative monitoring response time of the status information data group in the intelligent information management process, and at the same time determine whether the obtained comparative monitoring response time is not greater than the reference comparative monitoring response time in the database. If so, execute V2, otherwise execute V3; V2, obtain the first response time coefficient, and combine the obtained two-sided test efficiency score, synchronous optimization index and the first information management efficiency weight factor to obtain the information management efficiency score; V3, obtain the second response time coefficient, and combine the obtained two-sided test efficiency score, synchronous optimization index and the second information management efficiency weight factor to obtain the information management efficiency score; The comparative monitoring response duration represents the synchronous response duration of the status information data group during the monitoring process; The first information management efficiency weight factor includes a first response time coefficient weight factor, a two-sided inspection efficiency score weight factor and a synchronous optimization index weight factor; The second information management efficiency weight factor includes a second response time coefficient weight factor, a two-sided inspection efficiency score weight factor and a synchronous optimization index weight factor.

9. The method for managing lung cancer whole course status information based on big data as claimed in claim 1, characterized in that: The specific process of judging whether to re-perform intelligent information management based on the obtained information management efficiency score is as follows: Determine whether the obtained information management efficiency score is greater than the information management efficiency score preset in the database: If the information management efficiency score obtained is greater than the information management efficiency score preset in the database, intelligent information management is completed; If the acquired information management efficiency score is not greater than the preset information management efficiency score in the database, a secondary intelligent information management instruction is sent.

10. A lung cancer full course status information management system based on big data, characterized in that: include: Two-sided inspection module, synchronous optimization module and information management efficiency evaluation module; The bilateral test module is used to obtain the status information data of the patients in the experimental group and the patients in the control group respectively and perform bilateral tests to obtain bilateral test efficiency scores, and determine whether to perform synchronous optimization based on the obtained bilateral test efficiency scores. The bilateral test efficiency scores are used to quantify the detection efficiency of the degree of difference between the experimental group and the control group in terms of status information; The synchronous optimization module is used to monitor the changes in the state information data during the synchronous optimization process in real time to obtain the synchronous optimization index, and to determine whether to generate a management prompt list based on the obtained synchronous optimization index, and the synchronous optimization index is used to quantify the changes in the degree of difference in state information between the experimental group and the control group; The information management efficiency evaluation module is used to evaluate the effect of intelligent information management according to the obtained management prompt list to obtain an information management efficiency score if a management prompt list is generated, and at the same time, determine whether to re-perform intelligent information management based on the obtained information management efficiency score. The information management efficiency score is used to quantify the information management efficiency of the status information data of patients in the experimental group and the control group.

Images