Intelligent diagnosis auxiliary system based on informatization planning

Abstract

The invention relates to the technical field of medical systems, in particular to an intelligent diagnosis auxiliary system based on informatization planning, which comprehensively collects inspection operation parameters and patient physique information and covers data in multiple aspects from inspection process details to patient individual physique characteristics. The data are mapped to a test method-interference factor relation network through a knowledge graph, the influence of test operation interference and patient physique interference on diagnosis can be comprehensively analyzed, the influence condition of interference parameters is quantified by adopting a natural constant power formula in test process anomaly analysis and physique-to-diagnosis disease anomaly influence analysis, and the diagnosis accuracy is improved. The non-linear quantification mode better accords with the actual relation between interference and influence in a medical examination operation scene, the influence degree of the examination process and the patient physique on diagnosis can be more accurately evaluated, and then a more reliable basis is provided for diagnosis.

Description

Technical Field

[0001] This invention relates to the field of medical system technology, and in particular to an intelligent diagnostic assistance system based on information-based planning. Background Technology

[0002] In modern medical diagnosis, accurate disease diagnosis is crucial for patient treatment and recovery. However, current diagnostic technologies face numerous challenges, one prominent issue being the difficulty in identifying abnormalities in the patient's body caused by the testing process, leading to errors in the initial diagnosis. Currently, disease diagnosis typically relies on various testing methods, such as blood tests, imaging examinations, and endoscopy. During these tests, due to the complexity of the procedures, individual patient differences, and limitations of the equipment, various interfering factors can arise, affecting the accuracy of the results and leading to erroneous initial diagnoses. For example, after a gastroscopy biopsy, inadequate hemostasis may result in occult bleeding, causing a sustained decrease in hemoglobin levels. In such cases, doctors might misdiagnose it as a progression of a gastrointestinal tumor, overlooking the abnormality caused by the testing process itself. Similarly, a transient increase in CRP after surgery is a normal physiological characteristic, but this change could lead to a misdiagnosis as an infection. These initial diagnostic errors not only impose unnecessary psychological and financial burdens on patients but may also result in incorrect treatment plans and delays in the patient's recovery. Furthermore, existing diagnostic methods often lack a comprehensive consideration of confounding factors during the testing process and patient physical factors, making it difficult to accurately assess the reliability of test results and thus affecting diagnostic accuracy and efficiency. Currently, although some intelligent diagnostic assistance systems are applied in the medical field, most of these systems focus on simple analysis of disease symptoms and test data, lacking in-depth exploration and comprehensive analysis of confounding factors during the testing process and patient physical factors. At the same time, existing systems fail to fully utilize advanced technologies such as knowledge graphs, making it difficult to establish an effective correlation between testing methods and confounding factors, and thus failing to provide doctors with accurate and comprehensive diagnostic assistance information. Therefore, there is an urgent need for an intelligent assistance system that can comprehensively consider confounding factors during the testing process and patient physical factors, and utilize knowledge graph technology to achieve accurate diagnosis, in order to improve the accuracy and reliability of disease diagnosis and reduce the occurrence of misdiagnosis and missed diagnosis.

[0003] To address the technical problem of existing technologies that "difficulty in identifying abnormalities in the patient's body during the disease testing process leads to errors in the initial diagnosis of subsequent disease examinations (e.g., inadequate hemostasis after gastroscopy biopsy → postoperative occult bleeding → continuous decline in hemoglobin monitoring values ​​→ misdiagnosis as gastrointestinal tumor progression)," this application designs an intelligent diagnostic assistance system based on information planning. Summary of the Invention

[0004] To overcome the technical problem of existing technologies that "difficulty in identifying abnormalities in the patient's body during the disease testing process leads to errors in the initial diagnosis of subsequent disease examinations (e.g., inadequate hemostasis after gastroscopy biopsy → postoperative occult bleeding → continuous decline in hemoglobin monitoring values ​​→ misdiagnosis as gastrointestinal tumor progression)," this invention provides an intelligent diagnostic assistance system based on information planning.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides an intelligent diagnostic assistance system based on information planning, comprising a test process recording module, a diagnostic disease atlas analysis module, a test process analysis module, a test influence analysis module, a test interference analysis module, and a misjudgment assistance analysis module. The test process recording module is used to collect test operation parameters and patient physical condition information in real time. The diagnostic disease atlas analysis module maps the test process records to a test method-interference factor relationship network in a knowledge graph to obtain test operation interference parameters and patient physical condition interference data corresponding to the diagnosed disease. The test process analysis module performs test process anomaly analysis on the test operation interference parameters collected during the test. The test influence analysis module performs test process influence anomaly analysis by combining the test process anomaly analysis results and patient physical condition interference data. The test interference analysis module performs interference analysis based on the test process influence anomaly analysis results and the diagnosed disease status. The misjudgment assistance analysis module performs misjudgment assistance analysis based on the interference analysis results.

[0006] In one implementation of the present invention, the diagnostic disease atlas analysis module includes a disease diagnosis method acquisition unit, a diagnosis method error cause acquisition unit, a knowledge graph analysis unit, and an interference parameter acquisition unit. The disease diagnosis method acquisition unit is used to acquire the preliminary diagnosis method of the disease, i.e., the preliminary diagnosis method for judging the disease. The diagnosis method error cause acquisition unit is used to acquire the types of errors that have occurred in the preliminary diagnosis method of the disease in the past and the probability of the causes. The knowledge graph analysis unit is used to construct a relational network of the types of errors that have occurred in the past and the probability of the causes of the causes of different preliminary diagnosis methods. The interference parameter acquisition unit is used to import the test operation parameters and patient physical condition information into the constructed relational network and output the test operation interference parameters and patient physical condition interference data contained in the test operation parameters and patient physical condition information, as well as the probability of the causes of various data. The test operation interference parameters and patient physical condition interference data are both in the test operation parameters and patient physical condition information and in the relational network of the types of errors that have occurred in the past and the probability of the causes of the causes, i.e., the intersection of the two types of data.

[0007] In one implementation of the present invention, the step of performing anomaly analysis on the inspection operation interference parameters collected during the inspection process includes the following specific steps: S10. Obtain the interference parameters of the corresponding diagnostic disease and the probability of the occurrence of the interference parameter types. At the same time, obtain the average value of the difference between the historical interference parameters of the diagnostic disease and the standard safety parameters, and set it as the total difference. At the same time, obtain the difference between the interference parameters of the current diagnostic disease and the standard safety parameters, and set it as the difference of the current operation. S11. Obtain all differences for the corresponding interference parameter type and the difference for this operation, as well as the probability of the cause of the interference parameter type. Use the natural constant power formula to obtain the influence of the corresponding interference parameter type. The natural constant power formula is: Here, Ls represents the probability of the occurrence of the interference parameter type, exp() is the power of the natural constant e, cs is the difference in the current operation for the corresponding interference parameter type, and ys is the total difference for the corresponding interference parameter type. The reason for setting exp() is that, in reality, the influence of the interference parameter type is often not a simple linear relationship with the operation difference. A linear relationship means that the influence increases uniformly with the increase of the operation difference. However, in many medical laboratory operation scenarios, when the operation difference is small, its influence may be relatively small and its growth is relatively slow; while when the operation difference increases to a certain extent, its influence may increase rapidly. The natural constant power function exp(x) has non-linear characteristics. When x is small, its growth is relatively slow, but as x increases, the growth rate becomes faster and faster. It can well simulate this characteristic of the change in influence under different operation differences. For example, in the case of hemostasis volume after gastroscopy biopsy, when the difference between the hemostasis volume and the standard safety parameter is small, it may only have a slight impact on the patient's recovery; but when the difference is large, the impact may become increasingly significant. S12. Obtain the impact of all corresponding interference parameter types and sum them to obtain the anomaly analysis results of the inspection process.

[0008] In one implementation of the present invention, the step of performing anomaly analysis of the testing process by combining the results of the anomaly analysis of the testing process and the patient's physical condition interference data includes the following specific steps: S20. Obtain patient constitution interference data and the probability of occurrence of various constitution interference parameters for the corresponding diagnosed disease. Based on the patient constitution interference data and the probability of occurrence of various constitution interference parameters for the corresponding diagnosed disease, analyze the abnormal impact of constitution on the diagnosed disease. The advantage of this setup is that patients with different constitutions may have different clinical manifestations for the same disease. By analyzing the constitution interference data and the probability of occurrence of the causes, a more comprehensive understanding of the patient's condition can be obtained, avoiding misdiagnosis or missed diagnosis due to constitution factors. S21. The results of the abnormal impact analysis of the test process are obtained by weighted summing of the results of the analysis of the abnormal impact of physical constitution on the diagnosis of diseases and the results of the analysis of abnormality in the test process. Physical constitution factors can affect the manifestation and development of diseases. Patients with different physical constitutions may have different symptoms and test results for the same disease, and the test process itself may also have various abnormalities.

[0009] In one implementation of the present invention, the analysis of the abnormal influence of constitution on the diagnosed disease based on the patient's constitution interference data and the probability of the occurrence of the types of constitution interference parameters includes the following specific contents: S200: Obtain patient constitution interference data for the corresponding diagnosed disease and the probability of occurrence of the types of constitution interference parameters. At the same time, obtain the average value of the difference between the historical corresponding constitution interference data and the standard constitution safety parameters, and set it as the overall constitution difference. Also, obtain the difference between the current corresponding constitution interference data and the standard constitution safety parameters, and set it as the current constitution difference. S201. Obtain the overall difference in physical condition and the current difference in physical condition for the corresponding patient's physical condition interference data. Simultaneously, obtain the probability of the occurrence of each type of physical condition interference parameter. Calculate the influence of the corresponding physical condition interference parameter using the natural constant power formula. The formula for calculating the influence of the corresponding physical condition interference parameter is as follows: Where Pz is the probability of the occurrence of the corresponding type of physical condition interference parameter, rz is the current difference of physical condition in the corresponding patient's physical condition interference data, and tz is the overall difference of physical condition in the corresponding patient's physical condition interference data. S202. Obtain the influence of all physical constitution interference parameters, and sum them to obtain the analysis results of the abnormal influence of physical constitution on the diagnosis of diseases.

[0010] In one implementation of the present invention, the interference analysis based on the impact of the testing process on the abnormal analysis results and the diagnosis of the disease includes the following specific contents: The obtained abnormal analysis results of the test process influence are compared with the set test process influence threshold. If the abnormal analysis results of the test process influence are greater than or equal to the set test process influence threshold, it indicates that the probability of misdiagnosis is high, and a re-examination instruction is generated and sent to medical staff. If the abnormal analysis results of the test process influence are less than the set test process influence threshold, it indicates that the probability of misdiagnosis is low, and a low misdiagnosis instruction is generated and sent to medical staff.

[0011] In one implementation of the present invention, the misjudgment assistance analysis based on interference analysis results includes the following specific contents: The medical staff received the corresponding re-examination instruction and conducted a re-examination of the patient.

[0012] Secondly, the present invention also provides an intelligent diagnostic assistance method based on information-based planning, comprising the following specific steps: Used for real-time collection of test operation parameters and patient physical information; The test process records are mapped to the test method-interference factor relationship network in the knowledge graph to obtain the test operation interference parameters and patient physical interference data for the corresponding diagnostic diseases. Anomaly analysis of the inspection process is performed on the inspection operation interference parameters collected during the inspection process; An analysis of the impact of abnormalities on the testing process was conducted by combining the results of the abnormality analysis of the testing process with data on interference from the patient's physical condition. Interference analysis was conducted based on the impact of the testing process on anomaly analysis results and the diagnosis of diseases. Misjudgment assistance analysis based on interference analysis results.

[0013] Thirdly, the present invention provides an electronic device comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor, and the processor executes an intelligent diagnostic assistance method based on information planning by calling the computer program stored in the memory.

[0014] Fourthly, the present invention provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform an intelligent diagnostic assistance method based on information planning.

[0015] Compared with the prior art, the present invention has the following advantages and beneficial effects: Taking into account multiple factors: The system comprehensively collects test operation parameters and patient physical information, covering multiple aspects of data from test process details to individual patient physical characteristics. Through knowledge graph, this data is mapped to the relationship network of test methods and interference factors, which can comprehensively analyze the impact of test operation interference and patient physical interference on diagnosis.

[0016] Quantifying the impact of interference: In the analysis of abnormalities in the testing process and the analysis of the impact of physical condition on the diagnosis of diseases, the influence of interference parameters is quantified by the power formula of the natural constant. This non-linear quantification method is more in line with the actual relationship between interference and influence in medical testing operation scenarios. It can more accurately assess the degree of influence of the testing process and the patient's physical condition on the diagnosis, thereby providing a more reliable basis for diagnosis.

[0017] Reduce the probability of misdiagnosis: By comparing the results of abnormal analysis of the test process with the set threshold through interference analysis, the probability of misdiagnosis of disease conditions can be judged. When the probability of misdiagnosis is high, a re-examination instruction is generated in a timely manner, allowing medical staff to re-examine the patient, which effectively reduces the possibility of misdiagnosis and improves the accuracy of diagnosis. Attached Figure Description

[0018] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a schematic diagram of the overall process of an embodiment of the method of the present invention; Figure 2 This is a schematic diagram of the anomaly analysis process in the inspection procedure of an embodiment of the method of the present invention; Figure 3 This is a schematic diagram of the structure of an embodiment of the system of the present invention; Figure 4 This is a schematic diagram of the structure of the diagnostic disease atlas analysis module in an embodiment of the system of the present invention. Detailed Implementation

[0019] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0020] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0021] Secondly, the term "an 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 one embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it a single embodiment or an embodiment selectively excluded from other embodiments.

[0022] Example 1. This example specifically illustrates the technical problem of existing technologies: "It is difficult to identify abnormalities in the patient's body caused by the disease testing process, which leads to errors in the preliminary diagnosis of subsequent disease examinations (e.g., inadequate hemostasis after gastroscopy biopsy → postoperative occult bleeding → continuous decrease in hemoglobin monitoring value → misjudgment as progression of gastrointestinal tumor)." like Figure 1 and Figure 2 As shown, this embodiment provides an intelligent diagnostic assistance method based on information-based planning, which specifically includes the following steps: Used for real-time collection of test operation parameters and patient physical information; In this embodiment, the test process recording module is used to collect test operation parameters and patient physical information in real time. The test operation parameters include data such as time, operation process, blood sampling depth and equipment operation status, and patient physical data such as patient position, patient temperature and respiratory rate. The test process records are mapped to the test method-interference factor relationship network in the knowledge graph to obtain the test operation interference parameters and patient physical interference data for the corresponding diagnostic diseases. In this embodiment, the specific content is as follows: Obtaining the preliminary diagnostic methods for the disease, i.e., the methods for initially judging the disease, such as a continuous decrease in hemoglobin monitoring values ​​leading to a misdiagnosis of gastrointestinal tumor progression; similarly, a temporary increase in postoperative CRP leading to a misdiagnosis of infection; and obtaining the types of errors in the historical occurrence of these preliminary diagnostic methods and their probabilities. For example, misdiagnosis of gastrointestinal tumors may be due to respiratory motion artifacts and inadequate hemostasis after endoscopic biopsy, with probabilities of 13.12% and 12.3%, respectively. Constructing a relationship network between the types of errors in the historical occurrence of different preliminary diagnostic methods and their probabilities, and... The relationship network constructed by importing test operation parameters and patient physical condition information outputs test operation interference parameters and patient physical condition interference data contained in the test operation parameters and patient physical condition information, as well as the probability of the causes of various data. Specifically, test operation interference parameters and patient physical condition interference data are present in both the test operation parameters and patient physical condition information, and also in the relationship network of historical error causes and their probabilities—that is, the intersection of the two types of data. For example, if a gastrointestinal tumor is diagnosed due to respiratory motion artifacts and inadequate hemostasis after endoscopic biopsy, the corresponding test operation interference parameters and patient physical condition interference data show abnormal respiratory motion and adequate hemostasis after endoscopic biopsy. Therefore, the output is the patient's abnormal respiratory motion parameters and the probability of their occurrence. Anomaly analysis of the inspection process is performed on the inspection operation interference parameters collected during the inspection process; In this embodiment, the following specific steps are included: S10. Obtain the interference parameters of the corresponding diagnostic disease and the probability of the occurrence of the interference parameter types. At the same time, obtain the average value of the differences between the historical interference parameters and the standard safety parameters, and set it as the total difference. Also, obtain the difference between the interference parameters of the current diagnostic disease and the standard safety parameters, and set it as the difference of this operation. For example, if hemostasis is not sufficient after gastroscopy biopsy, the standard safety parameter is 100% hemostasis. The historical average hemostasis rate of interference parameters is 92%, so the total difference is 8%. However, the hemostasis rate of the interference parameters for the current diagnostic disease is 85%, so the difference of this operation is 15%. S11. Obtain all differences for the corresponding interference parameter type and the difference for this operation, as well as the probability of the cause of the interference parameter type. Use the natural constant power formula to obtain the influence of the corresponding interference parameter type. The natural constant power formula is: Here, Ls represents the probability of the occurrence of the interference parameter type, exp() is the power of the natural constant e, cs is the difference in the current operation for the corresponding interference parameter type, and ys is the total difference for the corresponding interference parameter type. The reason for setting exp() is that, in reality, the influence of the interference parameter type is often not a simple linear relationship with the operation difference. A linear relationship means that the influence increases uniformly with the increase of the operation difference. However, in many medical laboratory operation scenarios, when the operation difference is small, its influence may be relatively small and its growth is relatively slow; while when the operation difference increases to a certain extent, its influence may increase rapidly. The natural constant power function exp(x) has non-linear characteristics. When x is small, its growth is relatively slow, but as x increases, the growth rate becomes faster and faster. It can well simulate this characteristic of the change in influence under different operation differences. For example, in the case of hemostasis volume after gastroscopy biopsy, when the difference between the hemostasis volume and the standard safety parameter is small, it may only have a slight impact on the patient's recovery; but when the difference is large, the impact may become increasingly significant. S12. Obtain the impact of all corresponding interference parameter types and sum them to obtain the anomaly analysis results of the inspection process; An analysis of the impact of abnormalities on the testing process was conducted by combining the results of the abnormality analysis of the testing process with data on interference from the patient's physical condition. In this embodiment, the following specific steps are included: S20. Obtain patient constitution interference data and the probability of occurrence of various constitution interference parameters for the corresponding diagnosed disease. Based on this data, analyze the abnormal impact of constitution on the diagnosed disease. The advantage of this approach is that patients with different constitutions may have different clinical manifestations of the same disease. By analyzing constitution interference data and the probability of their occurrence, a more comprehensive understanding of the patient's condition can be achieved, avoiding misdiagnosis or missed diagnosis due to constitution factors. For example, a person with a Yang deficiency constitution may have atypical symptoms when infected with a cold, with mild fever but prominent symptoms such as chills and fatigue. Without considering constitution factors, this could lead to misdiagnosis as other diseases. For diseases with similar symptoms, constitution factors can serve as an important basis for differential diagnosis. For example, with cough, a patient with a Yin deficiency constitution may have a dry cough with little phlegm, while a patient with a phlegm-dampness constitution may have more phlegm. By analyzing constitution interference data, the type of disease the patient suffers from can be determined more accurately. In this embodiment, the abnormal impact of physical constitution on the diagnosis of diseases is analyzed based on the patient's physical constitution interference data for the corresponding diagnosed diseases and the probability of the occurrence of various physical constitution interference parameters. This analysis includes the following specific aspects: S200: Obtain patient constitution interference data for the corresponding diagnosed disease and the probability of occurrence of the types of constitution interference parameters. At the same time, obtain the average value of the difference between the historical corresponding constitution interference data and the standard constitution safety parameters, and set it as the overall constitution difference. Also, obtain the difference between the current corresponding constitution interference data and the standard constitution safety parameters, and set it as the current constitution difference. S201. Obtain the overall difference in physical condition and the current difference in physical condition for the corresponding patient's physical condition interference data. Simultaneously, obtain the probability of the occurrence of each type of physical condition interference parameter. Calculate the influence of the corresponding physical condition interference parameter using the natural constant power formula. The formula for calculating the influence of the corresponding physical condition interference parameter is as follows: Where Pz is the probability of the occurrence of the corresponding type of physical condition interference parameter, rz is the current difference of physical condition in the corresponding patient's physical condition interference data, and tz is the overall difference of physical condition in the corresponding patient's physical condition interference data. S202. Obtain the influence of all physical constitution interference parameters, and sum them to obtain the analysis results of the abnormal influence of physical constitution on the diagnosis of diseases. S21. The analysis results of abnormal impact of physical constitution on disease diagnosis and abnormal analysis of the testing process are obtained by weighted summation. Physical constitution factors can affect the manifestation and development of diseases. Patients with different physical constitutions may have different symptoms and test results for the same disease. Furthermore, the testing process itself may contain various abnormalities, such as improper sample collection or instrument errors. Weighted summation of these two analyses comprehensively considers both internal physical constitution factors and external testing process factors, avoiding the limitations of single-factor analysis and more accurately determining whether abnormal test results truly reflect the disease condition. For example, a person with a certain constitution... Patients with a weak constitution may have naturally low test results, and improper sample preservation during testing can also lead to abnormal results. Comprehensive analysis allows for more accurate diagnosis. It can more clearly identify whether the abnormal test results are due to the patient's constitution, problems in the testing process, or a combination of both, thereby reducing misdiagnosis and missed diagnosis. For example, some people with special constitutions may have test values ​​outside the normal range but are not actually ill. Without considering constitution analysis, misdiagnosis may occur. Similarly, if errors in the testing process are not detected, they can also affect the diagnosis. Comprehensive analysis can effectively avoid such problems. Interference analysis was conducted based on the impact of the testing process on anomaly analysis results and the diagnosis of diseases. This embodiment includes the following specific contents: The obtained abnormal analysis results of the test process influence are compared with the set test process influence threshold. If the abnormal analysis results of the test process influence are greater than or equal to the set test process influence threshold, it indicates that the probability of misdiagnosis is high and a re-examination instruction is generated and sent to medical staff. If the abnormal analysis results of the test process influence are less than the set test process influence threshold, it indicates that the probability of misdiagnosis is low and a misdiagnosis probability instruction is generated and sent to medical staff. Misjudgment auxiliary analysis based on interference analysis results; This embodiment includes the following specific contents: The medical staff received the corresponding re-examination instruction and conducted a re-examination of the patient.

[0023] It should be noted in this embodiment that this embodiment has the following advantages and benefits, and compared with the prior art, the present invention has the following advantages and beneficial effects: Taking into account multiple factors: The system comprehensively collects test operation parameters and patient physical information, covering multiple aspects of data from test process details to individual patient physical characteristics. Through knowledge graph, this data is mapped to the relationship network of test methods and interference factors, which can comprehensively analyze the impact of test operation interference and patient physical interference on diagnosis.

[0024] Quantifying the impact of interference: In the analysis of abnormalities in the testing process and the analysis of the impact of physical condition on the diagnosis of diseases, the influence of interference parameters is quantified by the power formula of the natural constant. This non-linear quantification method is more in line with the actual relationship between interference and influence in medical testing operation scenarios. It can more accurately assess the degree of influence of the testing process and the patient's physical condition on the diagnosis, thereby providing a more reliable basis for diagnosis.

[0025] Reduce the probability of misdiagnosis: By comparing the results of abnormal analysis of the test process with the set threshold through interference analysis, the probability of misdiagnosis of disease conditions can be judged. When the probability of misdiagnosis is high, a re-examination instruction is generated in a timely manner, allowing medical staff to re-examine the patient, which effectively reduces the possibility of misdiagnosis and improves the accuracy of diagnosis.

[0026] It should also be noted that the values ​​of the set parameters in this embodiment (such as weighted weights or thresholds) are obtained by those skilled in the art through historical data experiments. Based on historical test operation parameters and patient physical information, the abnormal analysis results of the test process are calculated in each step of this embodiment. Then, the judgment result of whether a misjudgment has actually occurred is obtained. The judgment result and the abnormal analysis result are imported into the fitting software (preferably MATLAB) for fitting, and the set parameter values ​​that meet the maximum judgment accuracy are output.

[0027] Example 2. (As shown) Figure 3 and Figure 4 As shown, this embodiment provides an intelligent diagnostic assistance system based on information planning, including: a test process recording module, a diagnostic disease atlas analysis module, a test process analysis module, a test impact analysis module, a test interference analysis module, and a misjudgment assistance analysis module. The test process recording module is used to collect test operation parameters and patient physical information in real time. The test operation parameters include data such as time, operation process (e.g., blood sampling depth, and equipment operation status), and patient physical data (e.g., patient position, body temperature, and respiratory rate). The diagnostic disease atlas analysis module maps the test process records to the test method-interference factor relationship network in the knowledge graph to obtain the test operation interference parameters and patient physical interference data corresponding to the diagnosed disease. The test process analysis module is used to perform test process anomaly analysis on the test operation interference parameters collected during the test. The test impact analysis module is used to perform test process impact anomaly analysis by combining the test process anomaly analysis results and patient physical interference data. The test interference analysis module performs interference analysis based on the test process impact anomaly analysis results and the diagnosed disease status. The misjudgment assistance analysis module performs misjudgment assistance analysis based on the interference analysis results. The diagnostic disease atlas analysis module includes a disease diagnosis method acquisition unit, a diagnosis method error cause acquisition unit, a knowledge graph analysis unit, and an interference parameter acquisition unit. The disease diagnosis method acquisition unit acquires the preliminary diagnosis method for a disease, i.e., the initial diagnostic method for judging the disease. For example, a continuous decrease in hemoglobin monitoring values ​​might be misdiagnosed as a progression of gastrointestinal tumors; in this case, the preliminary diagnosis method is a continuous decrease in hemoglobin monitoring values. Similarly, a transient increase in postoperative CRP might lead to a misdiagnosis of infection; in this case, the preliminary diagnosis method is a postoperative CRP test. The diagnosis method error cause acquisition unit acquires the types of errors that have occurred in the history of preliminary diagnosis methods and their probabilities. For example, misdiagnosis of gastrointestinal tumors might be due to respiratory motion artifacts or inadequate hemostasis after endoscopic biopsy, with probabilities of 13.12% and 12.3%, respectively. The knowledge graph analysis unit constructs the historical causes of errors in different preliminary diagnosis methods. The interference parameter acquisition unit is used to import the test operation parameters and patient physical condition information into the constructed relationship network and output the test operation interference parameters and patient physical condition interference data contained in the test operation parameters and patient physical condition information, as well as the probability of the occurrence of various data. Among them, the test operation interference parameters and patient physical condition interference data are in the test operation parameters and patient physical condition information, and also in the relationship network of the types of errors in the history and the probability of the occurrence of the causes, that is, the intersection of the two types of data. For example, if the cause of the gastrointestinal tumor is determined to be the patient's respiratory motion artifact interference and insufficient hemostasis after gastroscopy biopsy, the corresponding test operation interference parameters and patient physical condition interference data show that the patient's respiratory motion is abnormal and the hemostasis after gastroscopy biopsy is sufficient. Therefore, the output is the patient's respiratory motion abnormality parameters and the probability of the occurrence of the cause. The specific content of each step in this embodiment has been specifically described in the above system embodiment.

[0028] Example 3. An electronic device according to an embodiment of the present invention includes: a processor and a memory, wherein the memory stores a computer program that can be called by the processor, and the processor executes an intelligent diagnostic assistance method based on information planning by calling the computer program stored in the memory. It should be noted that all computer programs of the intelligent diagnostic assistance method based on information planning are implemented using C language.

[0029] Example 4. This example proposes a computer-readable storage medium on which an erasable and rewritable computer program is stored; When the computer program runs on the computer device, it enables the computer device to perform the aforementioned intelligent diagnostic assistance method based on information planning.

[0030] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the flow or function according to the embodiments of the present invention is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired network and / or wireless network. A computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives (SSDs).

[0031] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different systems to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0032] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing system embodiments, and will not be repeated here.

[0033] In the several embodiments provided by this invention, it should be understood that the disclosed systems, devices, and mechanisms can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of units is only one method, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interface; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0034] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0035] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0036] In the description of this specification, references to terms such as "an embodiment," "example," and "specific example" indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0037] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent diagnosis assistance system based on informatization planning, characterized in that, The test process record module is used for real-time collection of test operation parameters and patient physical information, the diagnostic disease graph analysis module maps the test process record to a test method-interference factor relationship network in a knowledge graph, obtains test operation interference parameters and patient physical interference data corresponding to a diagnostic disease, the test process analysis module is used for test process abnormality analysis of the test operation interference parameters collected in the test process, the test influence analysis module is used for test process influence abnormality analysis in combination with the test process abnormality analysis result and the patient physical interference data, the test interference analysis module performs interference analysis based on the test process influence abnormality analysis result and the diagnostic disease condition, and the misjudgment auxiliary analysis module performs misjudgment auxiliary analysis based on the interference analysis result. 2.The intelligent diagnosis assistant system based on informatization planning according to claim 1, characterized in that, The diagnostic disease graph analysis module includes a disease diagnosis method acquisition unit, a diagnosis method error reason acquisition unit, a knowledge graph analysis unit and an interference parameter acquisition unit, the disease diagnosis method acquisition unit is used for acquiring a preliminary diagnosis method of a disease, the diagnosis method error reason acquisition unit is used for acquiring a reason type situation and a probability of occurrence of a historical error of the preliminary diagnosis method of the disease, the knowledge graph analysis unit is used for constructing a relationship network of the reason type situation and the probability of occurrence of the historical error of the different preliminary diagnosis methods, and the interference parameter acquisition unit is used for importing the test operation parameters and the patient physical information into the constructed relationship network to output test operation interference parameters and patient physical interference data contained in the test operation parameters and the patient physical information, and probabilities of occurrence of various data, wherein the test operation interference parameters and the patient physical interference data are in the test operation parameters and the patient physical information, and are also in the relationship network of the reason type situation and the probability of occurrence of the historical error, that is, the intersection of the two types of data. 3.The intelligent diagnosis assistant system based on informatization planning according to claim 2, characterized in that, The test process abnormality analysis of the test operation interference parameters collected in the test process includes the following specific steps: S10, acquiring the test operation interference parameters corresponding to the diagnostic disease and the probabilities of occurrence of the interference parameter types, and simultaneously acquiring the average value of the difference between the historical test operation interference parameters and the standard safety parameters, which is set as all the differences, and acquiring the difference between the test operation interference parameters of the diagnostic disease and the standard safety parameters, which is set as the operation difference value; S11, acquiring all the differences and the operation difference value corresponding to the interference parameter types, and the probabilities of occurrence of the interference parameter types, and obtaining the influence situation corresponding to the interference parameter types through a natural constant power formula; S12, acquiring all the influence situations corresponding to the interference parameter types, and summing to obtain the test process abnormality analysis result.

4. The intelligent diagnosis assistant system based on informatization planning according to claim 3, characterized in that, The test process influence abnormality analysis in combination with the test process abnormality analysis result and the patient physical interference data includes the following specific steps: S20, obtaining patient constitution interference data corresponding to the diagnosed disease and the probability of the cause of the type of constitution interference parameter, and performing constitution abnormal influence analysis on the diagnosed disease based on the patient constitution interference data corresponding to the diagnosed disease and the probability of the cause of the type of constitution interference parameter; S21, obtaining the test process influence abnormal analysis result by weighted summation of the constitution abnormal influence analysis result on the diagnosed disease and the test process abnormal analysis result. 5.The intelligent diagnosis assistant system based on informatization planning according to claim 4, characterized in that, The constitution abnormal influence analysis on the diagnosed disease based on the patient constitution interference data corresponding to the diagnosed disease and the probability of the cause of the type of constitution interference parameter includes the following specific contents: S200, obtaining patient constitution interference data corresponding to the diagnosed disease and the probability of the cause of the type of constitution interference parameter, and obtaining the average value of the difference between the historical corresponding constitution interference data and the standard constitution safety parameter, which is set as the constitution overall difference, and obtaining the difference between the current corresponding constitution interference data and the standard constitution safety parameter, which is set as the constitution current difference; S201, obtaining the constitution overall difference and the constitution current difference corresponding to the patient constitution interference data, and obtaining the probability of the cause of the type of constitution interference parameter, and obtaining the influence of the corresponding constitution interference parameter by a natural constant power formula; S202, obtaining the influence of all constitution interference parameters, and summing to obtain the constitution abnormal influence analysis result on the diagnosed disease. 6.The information planning based intelligent diagnostic assistance system of claim 1, wherein, The interference analysis based on the test process influence abnormal analysis result and the diagnosed disease situation includes the following specific contents: The obtained test process influence abnormal analysis result is compared with the set test process influence threshold value, if the test process influence abnormal analysis result is greater than or equal to the set test process influence threshold value, it means that the misjudgment probability of the diagnosed disease situation is large, a re-examination instruction is generated and sent to the medical staff, if the test process influence abnormal analysis result is less than the set test process influence threshold value, it means that the misjudgment probability of the diagnosed disease situation is small, a small misjudgment probability instruction is generated and sent to the medical staff. 7.The information planning based intelligent diagnostic assistance system of claim 1, wherein, The misjudgment auxiliary analysis based on the interference analysis result includes the following specific contents: The medical staff receives the corresponding re-examination instruction and re-examines the patient.

8. The intelligent diagnosis assistance method based on information planning, which is implemented based on the intelligent diagnosis assistance system based on information planning according to any one of claims 1-7, characterized in that, The specific steps include: Real-time acquisition of test operation parameters and patient constitution information; Mapping the test process record to the test method-interference factor relationship network in the knowledge graph to obtain test operation interference parameters and patient constitution interference data corresponding to the diagnosed disease; Performing test process abnormal analysis on the test operation interference parameters collected in the test process; Performing test process influence abnormal analysis in combination with the test process abnormal analysis result and the patient constitution interference data; Performing interference analysis based on the test process influence abnormal analysis result and the diagnosed disease situation; Performing misjudgment auxiliary analysis based on the interference analysis result.

9. An electronic device comprising: A processor and a memory, wherein the memory stores a computer program which can be invoked by the processor; characterized in that the processor, by invoking the computer program stored in the memory, executes the intelligent diagnosis assistance method based on informatization planning according to any one of claims 8.

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