Auxiliary diagnosis information providing device and system

Abstract

The embodiment of the invention provides an auxiliary diagnosis information providing device and system. The method comprises the following steps: firstly, preprocessing test data based on a data processing module to obtain to-be-diagnosed information; the to-be-diagnosed information and the target knowledge graph are matched based on an auxiliary diagnosis analysis module, and a matching result is obtained; and finally, an auxiliary diagnosis report output module outputs an auxiliary diagnosis report containing the auxiliary diagnosis radar map according to a matching result. In the application, by outputting diversified auxiliary diagnosis reports such as the auxiliary diagnosis radar map and the like, the sample inspection result can be displayed more intuitively, so that the inspection conclusion is more interpretable, and medical personnel are helped to make more reliable and more accurate diagnosis decisions. Besides, the information to be diagnosed is matched based on the knowledge graph, so that key information can be quickly extracted for deep analysis and accurate judgment, and the efficiency and the accuracy can be guaranteed.

Description

Technical Field

[0001] Embodiments of the present application relate to the field of medical technology. More specifically, embodiments of the present application relate to an auxiliary diagnosis information providing device and system. Background Art

[0002] With the development of laboratory medicine, the test items in clinical medicine are becoming more and more diverse, and higher requirements are put forward for the accuracy and efficiency of test results.

[0003] Currently, when conducting sample tests, first, a test instrument tests the sample to output test data, and then medical staff issue a diagnosis report based on the test data. However, the content presented in the diagnosis report issued manually is limited, and it is difficult to make accurate and reliable diagnostic decisions based on the diagnosis report. At the same time, this process depends to a large extent on the knowledge scope and clinical experience of medical staff, resulting in the difficulty of guaranteeing the accuracy of the diagnosis report.

[0004] Therefore, how to improve the visualization degree of the diagnosis report and guarantee the accuracy of the diagnosis report are problems that need to be solved urgently at present. Summary of the Invention

[0005] In this context, embodiments of the present application provide an auxiliary diagnosis information providing device and system for improving the visualization degree of the diagnosis report and guaranteeing the accuracy of the diagnosis report at the same time.

[0006] In a first aspect, an embodiment of the present application provides an auxiliary diagnosis information providing device, including: a data processing module, configured to obtain test data of a sample to be tested and preprocess the test data to obtain information to be diagnosed, where the information to be diagnosed includes at least one of the following: items to be tested, numerical information corresponding to the items to be tested, and basic information corresponding to the sample to be tested;

[0007] an auxiliary diagnosis analysis module, configured to obtain a target knowledge graph and match the information to be diagnosed with the target knowledge graph to obtain a matching result;

[0008] an auxiliary diagnosis report output module, configured to output an auxiliary diagnosis report according to the matching result, where the auxiliary diagnosis report includes at least one of the following: abnormal information corresponding to the sample to be tested, an auxiliary diagnosis radar chart, an abnormal population distribution chart, and historical abnormal information of the sample provider, and the auxiliary diagnosis radar chart is used to indicate abnormal items corresponding to the sample provider and probabilities corresponding to each abnormal item.

[0009] In an alternative embodiment, the auxiliary diagnostic radar chart is further configured to indicate the number of directed edges corresponding to each abnormal item of the sample to be tested, and the number of directed edges is used to indicate the number of clinical findings corresponding to each abnormal item in the target knowledge graph; the target knowledge graph includes clinical finding nodes and abnormal information nodes, and the abnormal information nodes include abnormal items; the clinical finding nodes and the abnormal information nodes are connected by directed edges, and the direction of the directed edge is used to indicate the hint between the clinical finding and the abnormal information; specifically, the auxiliary diagnostic analysis module is configured to: match the information to be diagnosed with the clinical finding nodes in the target knowledge graph, and determine the directed edges corresponding to the clinical findings that match the information to be diagnosed, wherein each directed edge points to an abnormal item in an abnormal information node.

[0010] In an alternative embodiment, the auxiliary diagnostic radar chart is further configured to indicate the first occurrence frequency corresponding to the abnormal item; the first occurrence frequency is used to indicate the number of times the correspondence between the clinical finding and the abnormal information of the abnormal item appears in the literature corresponding to the target knowledge graph;

[0011] Specifically, the auxiliary diagnostic analysis module is configured to: search for the abnormal item corresponding to the information to be diagnosed in the target knowledge graph according to the test item and / or the numerical information corresponding to the test item indicated in the information to be diagnosed, and obtain the first occurrence frequency of at least one abnormal item corresponding to the sample to be tested according to the search result.

[0012] In an alternative embodiment, the auxiliary diagnostic analysis module is further configured to: determine the probability corresponding to the first abnormal item whose first occurrence frequency meets the first preset requirement among the abnormal items; according to the probability, determine the second abnormal item whose probability meets the second preset requirement among the first abnormal items; the auxiliary diagnostic report output module is further configured to: obtain the auxiliary diagnostic radar chart according to the probability corresponding to the second abnormal item.

[0013] In an alternative embodiment, the auxiliary diagnostic analysis module is further configured to: when the first occurrence frequencies of at least two abnormal items corresponding to the sample to be tested are the same, obtain the second occurrence frequency of the abnormal items with the same frequency in the preset literature, and determine the first abnormal item and the probability corresponding to each first abnormal item according to the first occurrence frequency and the second occurrence frequency.

[0014] In an alternative embodiment, the auxiliary diagnostic radar chart includes sector regions corresponding to each abnormal item respectively; the sector regions display the first occurrence frequency, the probability level, and the number of directed edges of the abnormal items through at least one of the radius length, the central angle, and the region color.

[0015] In an alternative embodiment, the auxiliary diagnosis information providing device further includes: a display module, configured to: in response to a selection indication for a sector area of the auxiliary diagnosis radar chart, display a diagnosis description of an abnormal item corresponding to the sector area; the selection indication includes at least one of moving a cursor to the sector area, a click operation on the sector area, and the cursor staying in the sector area for a time exceeding a predetermined time; the diagnosis description includes at least one of the following: a diagnosis basis, a test item, or a description of the abnormal item.

[0016] In an alternative embodiment, the abnormal population distribution map includes the population distribution information of the abnormal population and the position of the sample provider in the abnormal population; the auxiliary diagnosis report output module is specifically configured to: output the population distribution information according to historical test results, and mark the position of the sample provider in the population distribution information according to the diagnosis result corresponding to the sample to be tested, so as to obtain an abnormal population distribution map; the historical abnormal information is used to display the historical test records of the sample provider; the auxiliary diagnosis report output module is further configured to: obtain the historical test records of the sample provider, and output the historical abnormal information according to the time axis corresponding to the historical test records.

[0017] In a second aspect, an embodiment of the present application further provides an auxiliary diagnosis information providing device, including: a data processing module, configured to obtain test data of a sample to be tested, and preprocess the test data to obtain information to be diagnosed, the information to be diagnosed including at least one of the following: a test item, numerical information corresponding to the test item, and basic information corresponding to the sample to be tested;

[0018] an auxiliary diagnosis analysis module, configured to obtain a target knowledge graph, and match the information to be diagnosed with the target knowledge graph to obtain a matching result;

[0019] an auxiliary diagnosis report output module, configured to output an auxiliary diagnosis report according to the matching result, the auxiliary diagnosis report including at least one of the following: abnormal information corresponding to the sample to be tested, an auxiliary diagnosis radar chart, an abnormal population distribution map, and historical abnormal information of the sample provider, the auxiliary diagnosis radar chart being used to indicate at least one of abnormal items corresponding to the sample provider, probabilities corresponding to each abnormal item, a first occurrence frequency, and the number of directed edges;

[0020] wherein, the first occurrence frequency is used to indicate the number of times the relationship between the clinical findings of the abnormal item and the abnormal information appears in the literature corresponding to the target knowledge graph, and the number of directed edges is used to indicate the number of clinical findings corresponding to each abnormal item in the target knowledge graph.

[0021] In a third aspect, an embodiment of the present application further provides an auxiliary diagnosis information providing system, including:

[0022] a sampling component, configured to collect a sample to be tested;

[0023] A reaction component for processing a sample to be tested to form a liquid to be tested;

[0024] A driving component for driving the liquid path between the sampling component and the reaction component;

[0025] An inspection component for inspecting the liquid to be tested to obtain inspection data;

[0026] And an auxiliary diagnosis information providing device according to any one of the first aspect and / or the second aspect. The auxiliary diagnosis information providing device is used to output an auxiliary diagnosis report based on the inspection data.

[0027] The auxiliary diagnosis information providing device provided by the embodiment of the present application first preprocesses the inspection data of the sample to be tested based on the data processing module to obtain the information to be diagnosed; then matches the information to be diagnosed with the target knowledge graph based on the auxiliary diagnosis analysis module to obtain a matching result; finally, the auxiliary diagnosis report output module outputs an auxiliary diagnosis report according to the matching result, where the auxiliary diagnosis report includes at least one of the following: abnormal information corresponding to the sample to be tested, an auxiliary diagnosis radar chart, abnormal population distribution information, and historical abnormal information of the sample provider. The diverse auxiliary diagnosis reports output by this solution, including auxiliary diagnosis radar charts, abnormal population distribution information, and historical abnormal information of the sample provider, have a high degree of visualization, can more intuitively display the sample analysis results of the sample provider, make the diagnosis conclusion more interpretable, and thus can help medical staff make more reliable and accurate diagnosis decisions. In addition, in this solution, a knowledge graph is used to match the information to be diagnosed, and then an auxiliary diagnosis report is obtained based on the matching result. Compared with the manual analysis by medical staff, since the knowledge scope covered by the knowledge graph in this method is relatively wide, it can avoid the influence of the subjective factors of medical staff, can not only quickly extract key information, but also conduct in-depth analysis and accurate judgment, ensuring the efficiency and accuracy of obtaining the auxiliary diagnosis report.

[0028] The auxiliary diagnosis information providing system provided by the above embodiment has the same technical concept as the corresponding embodiment of each auxiliary diagnosis information providing device, and thus has the same technical effect, which will not be elaborated here. Description of the Drawings

[0029] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0030] Figure 1Schematic diagram of the application scenario provided by the embodiment of the present application Figure 1 ;

[0031] Figure 2 Schematic diagram of the application scenario provided by the embodiment of the present application Figure 2 ;

[0032] Figure 3 Schematic diagram of the structure of the auxiliary diagnosis information providing device provided by the embodiment of the present application Figure 1 ;

[0033] Figure 4 Schematic diagram of the target knowledge graph provided by the embodiment of the present application;

[0034] Figure 5 Schematic diagram of the target knowledge graph corresponding to the purulent inflammatory disease as the abnormal item provided by the embodiment of the present application;

[0035] Figure 6 Schematic diagram of the structuring of the information to be diagnosed provided by the embodiment of the present application;

[0036] Figure 7 Exemplary auxiliary diagnosis radar chart provided by the embodiment of the present application;

[0037] Figure 8 Schematic diagram of the abnormal population distribution information provided by the embodiment of the present application;

[0038] Figure 9 Schematic diagram of the historical abnormal information of the sample provider provided by the embodiment of the present application;

[0039] Figure 10 Schematic diagram of the structure of the auxiliary diagnosis information providing device provided by the embodiment of the present application Figure 2 ;

[0040] Figure 11 Schematic diagram of the structure of the auxiliary diagnosis information providing system provided by the embodiment of the present application.

[0041] In the drawings, the same or corresponding reference numerals indicate the same or corresponding parts. Detailed implementation manners

[0042] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0043] In the description, claims, and above-mentioned drawings of the embodiments of the present application, terms such as "first" and "second" are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present application described here can be implemented in an order different from those illustrated or described here. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0044] It should be understood that the term "and / or" used herein is only a relationship describing associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. " / " represents the relationship of "or".

[0045] With the development of laboratory medicine, the test items in clinical medicine are becoming more and more diverse, and higher requirements are put forward for the accuracy and efficiency of test results.

[0046] In the related art, when performing sample testing, first, the test instrument tests the sample to output test data, and then medical staff issue a diagnosis report based on the test data. However, the inventor found that the test report involves complex relationships among different parameters, biomarkers, and diseases. Manually analyzing these relationships is very challenging. It is difficult for medical staff to quickly extract key information from multiple parameters, let alone make full and effective use of this information. Moreover, the content presented in the diagnosis report issued manually is limited and the visualization level is low, making it difficult for medical staff to make accurate and reliable diagnostic decisions based on the diagnosis report.

[0047] At the same time, during the testing process, medical staff often lack in-depth analysis and accurate judgment of the diagnosis results. When issuing a diagnosis report, they rely heavily on the knowledge scope and clinical experience of medical staff. However, the knowledge scope and clinical experience of medical staff may be limited, and it is very likely that they cannot deeply understand the clinical significance of each test item, nor can they comprehensively consider all possible factors and connections related to disease diagnosis. Moreover, due to the differences in the educational levels of medical staff, the same test results may lead to different interpretations and conclusions. This subjectivity and difference will affect the accuracy of the diagnosis, and thus it is difficult to guarantee the accuracy of the diagnosis report.

[0048] In addition, from the perspective of scalability, medical staff undertake a large amount of work every day and are not sensitive to the update of medical knowledge. Therefore, when it comes to processing a large amount of data, different data sources, and constantly updated medical knowledge, manual inspection has great limitations.

[0049] In view of this, the embodiments of the present application provide an auxiliary diagnosis information providing device and system to solve at least one of the above problems.

[0050] Next, the application scenarios of the embodiments of the present application will be described with reference to the accompanying drawings.

[0051] Figure 1 This is a schematic diagram of the application scenario provided by the embodiments of the present application. As Figure 1 shown, the application scenario provided by the embodiments of the present application includes: a sample analyzer 101 and an auxiliary diagnosis information providing device 102.

[0052] Among them, the auxiliary diagnosis information providing device 102 may refer to a computer program product that realizes the auxiliary diagnosis function based on a computer program process, such as various application programs; it may also refer to an auxiliary diagnosis device loaded with the corresponding computer program product to realize the auxiliary diagnosis function, such as various intelligent devices with storage and computing capabilities.

[0053] In practical applications, when the auxiliary diagnosis information providing device 102 refers to an auxiliary diagnosis device loaded with the corresponding computer program product, it may be a physically independent intelligent device or integrated with a certain known intelligent device. Specifically, as Figure 1 shown, the auxiliary diagnosis information providing device 102 is a physically separated intelligent device from the sample analyzer 101, such as a smart phone, a personal computer, a medical diagnostic instrument, a cloud server, etc. Optionally, please refer to Figure 2 , the auxiliary diagnosis information providing device 102 and the sample analyzer 101 may also be integrated into one, such as a sample analyzer loaded with the corresponding computer program product.

[0054] In some embodiments, the auxiliary diagnosis information providing device 102 may be set to communicate with the output interface of the application system that performs test analysis on samples in the sample analyzer 101, and is used to obtain the test data obtained after performing test analysis on the sample to be tested from the sample analyzer 101. Among them, the sample to be tested is a sample taken from the body of the sample provider and contains various biological cells or other biological information. The sample to be tested is, for example, a blood sample, a urine sample, or a sample of other body fluids (pleural effusion, cerebrospinal fluid, serous cavity effusion, synovial fluid), etc. Correspondingly, the above biological cells may be at least one of biological cells such as neutrophils, lymphocytes, monocytes, eosinophils, basophils, red blood cells, and platelets; or, the above biological cells may also be immature granulocytes, tumor cells, lymphoblasts, plasma cells, atypical lymphocytes, proerythroblasts, basophilic erythrocytes, and polychromatic erythrocytes, orthochromatic erythrocytes, pre-macrogametes, basophilic megagametes, polychromatic megacells, and nucleated erythrocytes selected from orthochromatic megalocytes and megalospheres.

[0055] Specifically, the auxiliary diagnosis information providing device 102 first obtains the test data obtained by the sample analyzer 101 for performing test analysis on the sample to be tested, and preprocesses the test data to obtain the information to be diagnosed. Among them, the information to be diagnosed includes at least one of the following: the item to be tested, the numerical information corresponding to the item to be tested, the basic information corresponding to the sample to be tested, etc.

[0056] Furthermore, the auxiliary diagnosis information providing device 102 then matches the information to be diagnosed based on the target knowledge graph corresponding to the current test item, so as to obtain a matching result; finally, an auxiliary diagnosis report is output according to the matching result. Among them, the auxiliary diagnosis report includes at least one of the following: the abnormal information corresponding to the sample to be tested, the auxiliary diagnosis radar chart, the abnormal population distribution information, and the historical abnormal information of the sample provider corresponding to the sample to be tested, etc.

[0057] Thus, the auxiliary diagnosis information providing device 102 in the embodiments of the present application uses a knowledge graph to match the information to be diagnosed, so as to obtain a matching result, and then outputs the above-mentioned auxiliary diagnosis report based on the matching result. Compared with manual analysis, the diverse auxiliary diagnosis reports output by the auxiliary diagnosis information providing device 102 have a higher degree of visualization, which is convenient for users to more intuitively understand the current possible existing or potential abnormal conditions of the sample provider to which the sample to be tested belongs through viewing the auxiliary diagnosis report. And in the case where an abnormality is determined to exist, the decision-making basis can be known through the diagnosis basis provided in the auxiliary diagnosis report, and an accurate auxiliary diagnosis result can be obtained without relying on the personal experience level of the inspection medical staff. On the one hand, the inspection efficiency can be improved and the inspection result is more accurate; on the other hand, based on diverse auxiliary diagnosis reports such as the auxiliary diagnosis radar chart, abnormal population distribution information, and historical abnormal information of the sample provider obtained by the sample analyzer, the diagnosis conclusion can be more interpretable, so as to assist the inspection medical staff to reduce the workload of screening inspection data, and at the same time, the results in the auxiliary diagnosis report can be better used to match the hierarchical diagnosis and treatment policy.

[0058] In addition, since the knowledge range covered by the knowledge graph involved in this device is relatively wide, it can avoid the influence of the subjective factors of medical staff, can quickly extract key information, and can also conduct in-depth analysis and accurate judgment, ensuring both the output efficiency and accuracy of the auxiliary diagnosis report.

[0059] Optionally, when determining the auxiliary diagnosis information, the auxiliary diagnosis information providing device 102 can comprehensively consider various aspects of data of the sample provider, such as clinical information data that can reflect the individual differences of the sample provider, generally including gender, age, medical history, etc. The utilization of the clinical information data by the auxiliary diagnosis information providing device 102 can be reflected in multiple stages: one stage is in the process of the sample analyzer 101 performing inspection and analysis on the sample to be tested to obtain inspection data, and the sample analyzer 101 calibrates the obtained sample inspection data by considering the clinical information data of the sample provider; another stage is that the auxiliary diagnosis information providing device 102 directly obtains the clinical information data of the sample provider, and further comprehensively considers the clinical information data of the sample provider to calibrate the abnormal information corresponding to the sample to be tested in the process of matching the information to be diagnosed with the target knowledge graph to obtain a matching result.

[0060] In some embodiments, when the utilization of the clinical information data of the sample provider by the auxiliary diagnosis information providing device 102 includes the above-mentioned second stage, the auxiliary diagnosis information providing device 102 is communicatively connected to a laboratory information system (LIS). The laboratory information system generally includes application terminals located at different positions such as the hospital's guidance desk and laboratory department, and can be used to receive test data, input and save the test information of the sample provider, and assist the hospital in information management, etc. The auxiliary diagnosis information providing device 102 can directly obtain the clinical information data of the specified category of the sample provider from the laboratory information system.

[0061] Furthermore, after outputting the above-mentioned auxiliary diagnosis report, the auxiliary diagnosis information providing device 102 can also send the auxiliary diagnosis report to the laboratory information system, thereby facilitating the viewing by users at each end.

[0062] To facilitate the understanding of the technical implementation of the auxiliary diagnosis information providing device 102 provided in the embodiments of the present application, in the description of the present application, the specific examples are mainly described in detail by taking the test sample as a blood sample. Correspondingly, the sample analyzer 101 is shown as a hematology analyzer, but it should not be limited in actual applications. The sample test data obtained by the auxiliary diagnosis information providing device 102 may include one or more of hematological analysis data, biochemical analysis data, and immunoassay data.

[0063] The following uses specific embodiments to elaborate in detail on the technical solutions of the embodiments of the present application and how the technical solutions of the present application solve the above-mentioned technical problems. These several specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments.

[0064] Figure 3 Structural schematic of the auxiliary diagnosis information providing device provided in the embodiments of the present application Figure 1 As Figure 3 shown, the auxiliary diagnosis information providing device 300 provided in the embodiments of the present application includes: a data processing module 301, an auxiliary diagnosis analysis module 302, and an auxiliary diagnosis report output module 303.

[0065] Specifically, the data processing module 301 is used to obtain the test data of the test sample to be tested and preprocess the test data to obtain the information to be diagnosed.

[0066] It should be noted that the sample to be tested is a sample taken from the body of the sample provider and containing various biological cell information or other biological information; correspondingly, the test data refers to the corresponding analysis data obtained by performing a counting test on various biological samples containing biological cell information or other biological information. For example, the test data can be blood routine test information, such as the abnormal cell types, cell quantity characteristics, cell size characteristics, cell composition ratio characteristics, cell content characteristics, nucleic acid content characteristics, etc. contained in the blood.

[0067] Among them, the information to be diagnosed includes at least one of the following: the item to be tested, the numerical information corresponding to the item to be tested, and the basic information corresponding to the sample to be tested.

[0068] Exemplarily, taking the cell quantity characteristic as the test data as an example, the items to be tested can be: the number of neutrophils, the percentage of neutrophils, the number of eosinophils, the percentage of eosinophils, the number of monocytes, the percentage of monocytes, the number of white blood cells, the percentage of red blood cells, etc., and the basic information corresponding to the sample to be tested is the age, species, gender, etc. of the sample provider to which the sample to be tested belongs.

[0069] Among them, obtaining the test data of the sample to be tested can refer to importing, through communication connection with the sample analyzer, the test data obtained by the sample analyzer through testing and analyzing the sample to be tested; or, obtaining the test data of the sample to be tested can also refer to obtaining when the tester performs a test on the sample to be tested based on the sample analyzer and manually enters the test data into the auxiliary diagnosis information providing device.

[0070] In some optional implementation manners, the numerical information corresponding to the item to be tested can be converted into the rise and fall of its index according to the normal range of the corresponding item of the corresponding species. Among them, the rise of the index means that the numerical information corresponding to the item to be tested exceeds the normal range of the corresponding item of the species, and the fall of the index means that the numerical information corresponding to the item to be tested is lower than the normal range of the corresponding item of the species.

[0071] In the embodiment of the present application, the data processing module 301 converts the structured test data into the information to be diagnosed and uses it as the input of the auxiliary diagnosis analysis module 302. Specifically, after the data processing module 301 converts all the test items, the program will output all the knowledge as a temporary intermediate file. Among them, each line in the temporary intermediate file is the result of a single converted test item, and the content of the test item result includes the sample number, species name, test package name, Chinese name of the test item, English name of the test item, floating situation of the test index, floating percentage, test item value, etc.

[0072] It should be noted that the names of test packages, Chinese names of test items, and English names of test items obtained by different instruments and hospitals may vary, and the embodiments of the present application are not limited thereto.

[0073] The auxiliary diagnosis and analysis module 302 is configured to obtain the information to be diagnosed obtained by the data processing module 301, and obtain the target knowledge graph, so as to match the information to be diagnosed with the target knowledge graph to obtain a matching result.

[0074] Specifically, the auxiliary diagnosis and analysis module 302 will match the information to be diagnosed from the data processing module 301 with the target knowledge graph to count the frequencies of occurrence of each abnormal item, so as to determine a candidate list of abnormal items according to the frequencies of occurrence of each abnormal item, and determine the next test and treatment information based on the candidate list of abnormal items.

[0075] Furthermore, the auxiliary diagnosis report output module 303 obtains the matching result from the auxiliary diagnosis and analysis module 302, and outputs an auxiliary diagnosis report according to the matching result.

[0076] In some embodiments, the auxiliary diagnosis report includes at least one of the following: abnormal information corresponding to the test sample to be tested, auxiliary diagnosis radar chart, abnormal population distribution chart, and historical abnormal information of the sample provider, etc. Among them, the abnormal information is used to prompt data such as the number of abnormal indicators of the test item and the corresponding abnormal indicator percentage. For example, the abnormal information is to prompt the number of abnormal indicators and the corresponding indicator abnormal percentage of the white blood cell series, red blood cell series, and platelet series; the auxiliary diagnosis radar chart is used to display the abnormal items corresponding to the sample provider and the probabilities corresponding to each abnormal item; the abnormal population distribution chart is used to display the population distribution of the sample provider in the big data; the historical abnormal information of the sample provider is used to display the test records of the sample provider during the historical test process.

[0077] It should be noted that the abnormal items corresponding to the above sample provider can be understood as the diseases that the sample provider may suffer from. Correspondingly, the probabilities corresponding to the abnormal items can be the prevalence rates corresponding to each disease.

[0078] In the embodiments of the present application, by outputting diversified auxiliary diagnosis reports such as auxiliary diagnosis radar charts, abnormal population distribution information, and historical abnormal information of the sample provider, the sample test results of the sample provider can be more intuitively displayed, with a high degree of visualization, making the sample test results more interpretable, thereby helping medical staff make more reliable and accurate diagnostic decisions. Specifically, in the embodiments of the present application, by displaying the auxiliary diagnosis radar chart, the abnormal items suffered by the sample provider and the probabilities of suffering from various abnormal items can be clearly and intuitively displayed, helping medical staff quickly identify the abnormal items suffered by the sample provider, and then making targeted decisions. At the same time, by displaying the abnormal population distribution chart, it can help medical staff and sample providers quickly understand the position of the abnormality suffered by the sample provider in the abnormal population, facilitating understanding of the stage and abnormal conditions of the sample provider in the population. Furthermore, by displaying the historical abnormal information of the sample provider, all historical test records of the sample provider can be intuitively viewed, and at the same time, their health conditions can be understood according to the time axis, facilitating tracking of the changes in the condition of the sample provider, and then making more accurate and reasonable diagnostic decisions.

[0079] In an alternative embodiment, the target knowledge graph may be a knowledge graph composed of information such as subsequent tests and treatment plans for the test items and abnormal items of the sample provider, and different test categories may correspond to different knowledge graphs.

[0080] In an alternative embodiment, after obtaining the to-be-diagnosed information from the to-be-data-processed module 301, the auxiliary diagnosis analysis module 302 will obtain the target knowledge graph corresponding to the test item according to the test category corresponding to the to-be-diagnosed information. Exemplarily, taking the current test item as the diagnosis of animal blood routine as an example, the sources of the target knowledge graph include various medical guide literatures for disease differential diagnosis, such as "Differential Diagnosis of Small Animal Medicine" and "Color Atlas of Laboratory Tests and Diagnosis of Pet Diseases with Case Analysis", etc.

[0081] In the embodiments of the present application, the auxiliary diagnosis radar chart in the auxiliary diagnosis report is also used to indicate the number of directed edges of each abnormal item corresponding to the to-be-tested sample, where the number of directed edges is used to indicate the number of clinical findings corresponding to each abnormal item in the target knowledge graph. For ease of understanding, next, this embodiment will be described in detail with reference to Figure 4 the schematic diagram of the target knowledge graph shown.

[0082] Figure 4 This is the schematic diagram of the target knowledge graph provided by the embodiments of the present application. As Figure 4 shown, the target knowledge graph includes nodes and directed edges connecting the nodes.

[0083] Among them, there are two types of nodes. One type represents the abnormal information nodes obtained from various medical guidelines. The abnormal information nodes contain abnormal items, as well as information such as subsequent examinations and treatment plans for each abnormal item. The other type of node represents the clinical findings nodes, and the clinical findings nodes can be the detected symptoms, such as the increase / decrease of various indicators, such as the increase of white blood cells.

[0084] In practical applications, the clinical findings nodes and the abnormal information nodes are connected by directed edges. The direction of the directed edge is used to indicate the relationship between the clinical findings and the abnormal information, that is, the direction of the directed edge is used to indicate the hint of the clinical findings to the abnormal information.

[0085] It should be noted that the names of the nodes in the target knowledge graph are unique. The name of the edge is result hint, which represents the relationship between different types of nodes. The direction of the edge represents the sequence between the two connected nodes (i.e., the clinical findings node and the abnormal information node). The edge contains the number of occurrences of the current clinical findings and abnormal information in how many documents and the connection weight of the nodes (for example, the initial connection weight can be set to 1).

[0086] Exemplarily, if the clinical findings corresponding to the abnormal item A in the knowledge graph include: symptom 1, symptom 2, symptom 3, symptom 4, symptom 5; the clinical findings corresponding to the abnormal item B include: symptom 6, symptom 7, symptom 8, symptom 9, symptom 10. During the matching process, if any one or more of the clinical findings such as symptom 1, symptom 2, symptom 3, symptom 4, symptom 5 appear in the information to be diagnosed, the abnormal item it points to is "abnormal item A"; if any one or more of the clinical findings such as symptom 6, symptom 7, symptom 8, symptom 9, symptom 10 appear in the information to be diagnosed, the abnormal item it points to is "abnormal item B".

[0087] Taking the abnormal item as suppurative inflammatory disease as an example, Figure 5 is a schematic diagram of the target knowledge graph corresponding to the suppurative inflammatory disease provided by the embodiment of the present application. As Figure 5 shown, the clinical findings corresponding to the suppurative inflammatory disease include: the increase in the number of neutrophils, the increase in the percentage of neutrophils, the increase in the number of eosinophils, the increase in the percentage of eosinophils, the increase in the number of monocytes, the increase in the percentage of monocytes, the increase in the number of white blood cells, and the increase in the percentage of red blood cells, etc.

[0088] During the matching process, when any one or more of the above clinical findings appear in the information to be diagnosed, the abnormal item it points to is "suppurative inflammation".

[0089] In summary, during the matching process, there may be multiple directed edges of clinical findings in the target knowledge graph that point to the same abnormal item for the information to be diagnosed. That is to say, the more directed edges corresponding to an abnormal item, the more clinical findings in the information to be diagnosed point to that abnormal item, and the greater the possibility that the sample provider has that abnormal item. Therefore, in the embodiments of the present application, by displaying the number of directed edges of each abnormal item corresponding to the sample to be tested in the auxiliary diagnostic radar chart, it is possible to intuitively see the conditions of each abnormal item that the sample provider has through the auxiliary diagnostic radar chart, thereby helping medical staff make targeted decisions.

[0090] In addition, the auxiliary diagnostic information providing device in the embodiments of the present application uses a knowledge graph to match the information to be diagnosed to obtain a matching result, and then outputs an auxiliary diagnosis and report based on the matching result. Compared with manual analysis, the knowledge graph involved in this device covers a wider range of knowledge. Therefore, it can avoid the influence of the subjective factors of medical staff, can quickly extract key information, and can also conduct in-depth analysis and accurate judgment, ensuring both efficiency and accuracy.

[0091] Furthermore, it should be noted that this knowledge graph can be dynamically expanded according to actual needs to update nodes and relationships. For example, when there is new knowledge about blood routine for suppurative inflammation, new knowledge can be added to the knowledge graph or the original knowledge can be modified, so that medical staff can obtain new blood routine knowledge in a timely manner during clinical work and provide standardized examination and treatment to patients with suppurative inflammation according to the latest diagnosis and treatment plan according to the actual situation.

[0092] In some other embodiments, the auxiliary diagnostic radar chart is further used to indicate the first occurrence frequency corresponding to the abnormal item, where the first occurrence frequency is used to indicate the number of occurrences of the correspondence between the clinical findings of the abnormal item and the abnormal information in the literature corresponding to the target knowledge graph. For the convenience of understanding, the following further describes this embodiment in combination with the matching process:

[0093] Specifically, after obtaining the information to be diagnosed, the auxiliary diagnostic analysis module 302 can perform matching in the target knowledge graph according to methods such as the similarity between texts and the Euclidean distance between text vectors, so as to obtain the relationship between the clinical findings and the abnormal information in the knowledge graph.

[0094] In a specific implementation, the following formula can be used to find all abnormal items corresponding to this knowledge in the target knowledge graph:

[0095]

[0096] Where K is the edge between the abnormal information and the test item, S = {a 1 ,a2 ,...a n}, which is a set of test items.

[0097] Specifically, the auxiliary diagnosis analysis module 302 obtains the information to be diagnosed based on the temporary intermediate file of the data processing module 301, and matches the abnormal information corresponding to the information to be diagnosed from the test items to be tested and / or the numerical information corresponding to the test items indicated in the information to be diagnosed in the target knowledge graph. Then, it determines the abnormal items indicated by the abnormal information based on the matched abnormal information, and obtains the first occurrence frequency of at least one abnormal item corresponding to the test sample to be tested according to the matching result, where the first occurrence frequency is used to indicate the number of times the corresponding relationship between the clinical findings of the abnormal item and the abnormal information appears in the literature corresponding to the target knowledge graph.

[0098] The auxiliary diagnosis analysis module 302 will repeat the above steps until the combination of the test item names and the floating conditions of the test items in the target knowledge graph is completely traversed, and finally statistically obtains the first occurrence frequency of at least one abnormal item corresponding to the test sample to be tested in the target knowledge graph.

[0099] Furthermore, the auxiliary diagnosis report output module 303 generates an auxiliary diagnosis report based on the first occurrence frequency corresponding to each abnormal item, so as to display the number of times the corresponding relationship between the clinical findings of each abnormal item and the abnormal information appears in the literature corresponding to the target knowledge graph through the auxiliary diagnosis report.

[0100] Taking the corresponding relationship including "Clinical finding 1 - Abnormal information B, Clinical finding 1 - Abnormal information B" as an example, it can be understood that if the number of times "Clinical finding 1 - Abnormal information A" appears in the literature is greater than the number of times "Clinical finding 1 - Abnormal information B" appears in the literature, it means that when the information to be diagnosed points to Clinical finding 1, the target knowledge graph is more likely to point to Abnormal information A, that is, the sample provider is more likely to have the abnormal item corresponding to Abnormal information A.

[0101] In summary, since the larger the value of the first occurrence frequency, that is, the more the corresponding relationship between the clinical findings and the abnormal information appears in the literature, the higher the accuracy of the corresponding relationship between the clinical findings and the abnormal information. That is to say, the higher the accuracy of the sample provider having the abnormal item corresponding to the abnormal information. Therefore, in the embodiments of the present application, by displaying the first occurrence frequency in the auxiliary diagnosis radar chart, the occurrence frequency of the abnormal items suffered by the sample provider in the literature can be intuitively displayed, thereby helping medical staff to make more accurate and reliable decisions.

[0102] Optionally, when generating the auxiliary diagnosis radar chart, the auxiliary diagnosis analysis module 302 is specifically configured to: determine the probability corresponding to the first abnormal item whose first occurrence frequency in the abnormal items meets the first preset requirement.

[0103] Specifically, the embodiments of the present application do not limit the specific content of the first abnormal item that meets the first preset requirement. Exemplarily, it can be set that an abnormal item with "the first occurrence frequency is greater than N" is the first abnormal item; alternatively, it can also be set that an abnormal item with "the first occurrence frequency ranks among the top M" is the first abnormal item. 1 " is the first abnormal item; or, it can also be set that an abnormal item with "the first occurrence frequency ranks among the top M 1 " is the first abnormal item.

[0104] Exemplarily, this step can be set as "obtaining the probabilities of the abnormal items whose occurrence frequencies rank among the top 20 among the abnormal items".

[0105] Furthermore, the auxiliary diagnosis analysis module 302 is further configured to determine a second abnormal item among the first abnormal items whose probability meets the second preset requirement according to the probability.

[0106] Similarly, the embodiments of the present application do not limit the specific content of the second abnormal item that meets the second preset requirement. Exemplarily, it can be set that an abnormal item with "the second occurrence frequency is greater than N 2 " is the second abnormal item; or, it can also be set that an abnormal item with "the second occurrence frequency ranks among the top M 2 " is the second abnormal item.

[0107] Exemplarily, this step can be set as "determining the abnormal items whose probabilities rank among the top 10 among the first abnormal items".

[0108] Even further, after obtaining the probability corresponding to the second abnormal item, the auxiliary diagnosis report output module 303 is further configured to: obtain an auxiliary diagnosis radar chart according to the probability corresponding to the second abnormal item.

[0109] In this embodiment, by setting the first preset requirement and the second preset requirement to screen abnormal items, abnormal items with relatively low probabilities of the sample provider can be excluded, improving the output efficiency of the auxiliary diagnosis report.

[0110] In addition, by generating an auxiliary diagnosis radar chart for indicating the probabilities of each abnormal item, it is possible to intuitively understand the current existing or potential abnormal conditions of the sample provider and the occurrence probabilities of each abnormal condition through the auxiliary diagnosis radar chart, thereby helping medical staff to make targeted diagnostic decisions.

[0111] It should be added that after obtaining the first occurrence frequencies of each abnormal item, there may be a situation where the frequencies of multiple abnormal items are the same, and the more times a clinical finding and abnormal information relationship appears in different guideline documents, the greater the weight of this piece of knowledge. Therefore, when the frequencies of the abnormal items corresponding to the clinical findings are the same, they can also be sorted according to the frequencies in different documents of the clinical findings and abnormal information.

[0112] Specifically, the auxiliary diagnosis and analysis module 302 is further configured to: when the first occurrence frequencies of at least two abnormal items corresponding to the sample to be tested are the same, obtain the second occurrence frequencies of the abnormal items with the same frequency in the preset literature, and determine the first abnormal items and the probabilities corresponding to each first abnormal item according to the first occurrence frequencies and the second occurrence frequencies.

[0113] Regarding the preset literature, the embodiments of the present application do not make special limitations. For example, different to-be-diagnosed information corresponds to different preset literature sets, and the required preset literature can be determined according to the to-be-diagnosed information.

[0114] Exemplarily, after matching through the target knowledge graph, the first occurrence frequencies of abnormal item A, abnormal item B, and abnormal item C are all X times. The preset literature corresponding to the to-be-diagnosed information can be found to be Literature 1, Literature 2, and Literature 3. At this time, respectively find the total number of occurrences of abnormal item A, abnormal item B, and abnormal item C in the 3 literatures, which is recorded as the second occurrence frequency. If the second occurrence frequency of abnormal item A is greater than that of abnormal item B, and the second occurrence frequency of abnormal item C is greater than that of abnormal item A, then the target abnormal information is obtained as: the probability of abnormal item C is higher than that of abnormal item A, and the probability of abnormal item A is higher than that of abnormal item B.

[0115] Further, according to the method of obtaining the probabilities of the first abnormal items and the second abnormal items as described above, obtain the probabilities of the first abnormal items and the second abnormal items, and output an auxiliary diagnosis radar chart based on the probabilities of the second abnormal items. It should be understood that for the specific process, please refer to the above embodiments and will not be elaborated here.

[0116] In some alternative embodiments, after obtaining the first occurrence frequencies of each abnormal item, the auxiliary diagnosis and analysis module 302 can also sort at least one abnormal item according to the first occurrence frequencies of at least one abnormal item corresponding to the sample to be tested, so as to quickly determine the abnormal items with the same occurrence frequency in the sample to be tested according to the sorting result, thereby improving the efficiency of determining the abnormal items with the same frequency.

[0117] Figure 6 This is a structural schematic diagram of the to-be-diagnosed information provided by the embodiments of the present application. It should be noted that after receiving the test data, the data processing module 301 will convert the test data into a knowledge display manner as Figure 6 shown, and in the auxiliary diagnosis report output module, this content will also be embedded in the visual auxiliary diagnosis report.

[0118] In some embodiments, the auxiliary diagnosis report includes at least one of the following: abnormal information corresponding to the sample to be tested, an auxiliary diagnosis radar chart, a distribution map of abnormal groups, and historical abnormal information of the sample provider. The auxiliary diagnosis radar chart is used to indicate the abnormal items corresponding to the sample provider and the probabilities, the first occurrence frequencies of the abnormal items, the number of directed edges, etc. corresponding to each abnormal item. Among them, the auxiliary diagnosis radar chart includes sector regions respectively corresponding to each abnormal item. Optionally, the sector regions display the first occurrence frequency, probability level, number of directed edges, etc. of the abnormal items through at least one of the radius length, central angle, and region color. For the convenience of understanding, the following will be combined with Figure 7 to explain the auxiliary diagnosis radar chart in detail.

[0119] Figure 7 is an exemplary auxiliary diagnosis radar chart provided by an embodiment of the present application. As Figure 7 shown in the figure (a) therein, the auxiliary diagnosis radar chart includes sector regions corresponding one by one to each abnormal item, and users can clearly see the abnormal items corresponding to the sample to be tested in the auxiliary diagnosis radar chart.

[0120] It should be noted that Figure 7 is only shown exemplarily and is not limited thereto in actual applications.

[0121] In a specific implementation, the sector regions display the first occurrence frequency, probability level, number of directed edges of the abnormal items through at least one of the radius length, central angle, and region color. For example, the probability levels corresponding to each abnormal item can be displayed through colors. Exemplarily, the probability levels corresponding to each abnormal item can be displayed through the change in color depth. In addition, the first occurrence frequency corresponding to each abnormal item can be indicated by the radius of the sector region, that is, the relationship between the clinical findings and abnormal information corresponding to each abnormal item appears in how many documents. Exemplarily, the larger the sector radius, the greater the first occurrence frequency of the abnormal item corresponding to the sector, and the smaller the sector radius, the smaller the first occurrence frequency of the abnormal item corresponding to the sector. Furthermore, the number of directed edges of each abnormal item in the target knowledge graph can be indicated by the central angle degree of the sector region, where the number of directed edges is used to indicate the number of clinical findings corresponding to each abnormal item in the target knowledge graph. Exemplarily, the larger the central angle degree of the sector, the more clinical findings of the abnormal item corresponding to the sector in the target knowledge graph, and the smaller the central angle degree of the sector, the fewer clinical findings of the abnormal item corresponding to the sector in the target knowledge graph.

[0122] In the embodiments of the present application, compared with manual analysis, through a multi-dimensional analysis process of the probability, first occurrence frequency, and number of directed edges corresponding to abnormal items, the test sample can be analyzed more deeply and accurately, making the diagnosis result more accurate and reliable. At the same time, by using the color, central angle degree, and radius size of the sectors in the auxiliary diagnosis radar chart to display these data, the degree of data richness provided is relatively high, and the situation of the abnormal items suffered by the sample provider can be displayed more intuitively.

[0123] In some embodiments, after generating the auxiliary diagnosis report, the auxiliary diagnosis information providing device can also display the auxiliary diagnosis report through a display module. Specifically, in the embodiments of the present application, the auxiliary diagnosis information providing device further includes: a display module, and the display module is configured to: in response to a selection instruction for a sector area of the auxiliary diagnosis radar chart, display a diagnosis description of the abnormal item corresponding to the sector area.

[0124] Among them, the selection instruction includes at least one of moving the cursor to the sector area, a click operation on the sector area, and the cursor staying in the sector area for more than a predetermined time;

[0125] Exemplarily, the display module can specifically implement the following functions:

[0126] Function 1: In response to moving the cursor for selecting the target area to a certain sector area of the auxiliary diagnosis radar chart, display a diagnosis description of the abnormal item corresponding to the sector area through the display module;

[0127] Function 2: In response to a click operation on a certain sector area of the auxiliary diagnosis radar chart, display a diagnosis description of the abnormal item corresponding to the sector area through the display module;

[0128] Function 3: In response to moving the cursor for selecting the target area to a certain sector area of the auxiliary diagnosis radar chart and staying for a predetermined time, display a diagnosis description of the abnormal item corresponding to the sector area through the display module.

[0129] For the above Function 1, please refer to Figure 7 Figures (a) and (b) in. When the cursor of the mouse hovers over the sector area of "Blood / Bone Marrow Abnormality or Tumor", a diagnosis description of the abnormal item corresponding to the sector area will pop up.

[0130] In some embodiments, the diagnosis description includes but is not limited to at least one of the following: diagnosis basis, test item, or disease description.

[0131] Optionally, the popped-up floating window adjusts its position and size adaptively according to the position of the mouse. Exemplarily, when the mouse is on the left side of the center of the auxiliary diagnostic radar chart, the pop-up window is displayed on the left side, and the right border of the pop-up window is close to the position of the mouse cursor; otherwise, the pop-up window is on the right side.

[0132] Optionally, the display module can display the auxiliary diagnostic radar chart in the first area. At the same time, the display module will also respond to the selection indication of the sector area and display the diagnostic description of the abnormal item corresponding to the selected sector in the second area.

[0133] It should be understood that for the above function 2, the selection operation of the sector area of the auxiliary diagnostic radar chart is not particularly limited in the embodiments of the present application. For example, when the display device is a touch screen, the selection operation can be clicking on a certain sector area. When controlling the display device with a mouse, the selection operation can be clicking on a certain sector area with the mouse. It should be noted that other similar selection operations are not elaborated one by one in the embodiments of the present application, but all belong to the inventive concept of the present application.

[0134] It should be noted that the above display module can be any device with a display function, such as a smart phone, a personal computer, a medical diagnostic instrument, etc.; or, when the sample analyzer integrated with the auxiliary diagnostic information providing device has a display function, the display module is the sample analyzer, that is, the auxiliary diagnostic report can be displayed through the sample analyzer.

[0135] In the embodiments of the present application, the auxiliary diagnostic report output module 303 is further configured to output an abnormal population distribution map, where the abnormal population distribution map includes the population distribution information of the abnormal population and the position of the sample provider in the abnormal population.

[0136] In some optional embodiments, when the auxiliary diagnostic report output module 303 outputs the abnormal population distribution map, it is specifically configured to: output the population distribution information according to the historical test results, and mark the position of the sample provider in the population distribution information according to the diagnostic result corresponding to the sample to be tested, so as to obtain the abnormal population distribution map.

[0137] Specifically, the population distribution information is used to indicate the distribution of abnormal information of the population that has undergone sample testing. Next, in combination with Figure 8 The construction process of the abnormal population distribution map will be described in detail.

[0138] Figure 8 This is a schematic diagram of the abnormal population distribution when the abnormal information provided by the embodiments of the present application is the deviation of the erythrocyte series. As Figure 8As shown, taking the sample provider as an animal as an example, first, obtain the historical test results of the animal population that has undergone tests, then count the number of individuals with red blood cell line deviation values based on these historical test results, and finally obtain the population distribution information according to the statistical results. It can be understood that the population distribution information can be the abnormal population distribution diagram when the position of the unlabeled sample provider shown in Figure 8 is not marked.

[0139] Furthermore, after obtaining the population distribution information, determine the red blood cell line deviation value of the sample provider and mark it in the population distribution information, then the abnormal population distribution diagram shown in Figure 8 can be obtained.

[0140] It should be noted that for the type of the abnormal population distribution diagram, the embodiments of the present application do not make special limitations. For example, the abnormal population distribution diagram can be any type of graph such as a bar chart, a line chart, a pie chart, etc., Figure 8 which is shown by taking a bar chart as an example in

[0141] but not limited thereto. Optionally, the user can select the output type of the abnormal population distribution diagram according to their preferences or needs. Figure 8 As shown, in this figure, the horizontal axis represents the deviation value of the red blood cell line, and the vertical axis represents the number of individuals in the population corresponding to each deviation value. Among them, the number of individuals with a red blood cell line deviation value of -1 in the abnormal population is 100, the number of individuals with a red blood cell line deviation value of -0.5 in the abnormal population is 300, the number of individuals with a red blood cell line deviation value of -0.25 in the abnormal population is 600, the number of individuals with a red blood cell line deviation value of -0.05 in the abnormal population is 1200, the number of individuals with a red blood cell line deviation value of 0.05 in the abnormal population is 1300, the number of individuals with a red blood cell line deviation value of 0.25 in the abnormal population is 700, the number of individuals with a red blood cell line deviation value of 0.5 in the abnormal population is 300, and the number of individuals with a red blood cell line deviation value of 1 in the abnormal population is 500.

[0142] In the embodiments of the present application, the red blood cell line deviation value of the sample provider is 0.05, then mark the sample provider at the position corresponding to 0.05, and the abnormal population distribution diagram shown in Figure 8 can be obtained.

[0143] It should be noted that the construction method of the abnormal population distribution diagram corresponding to other types of abnormal information is similar to the above process and will not be elaborated here.

[0144] In the embodiments of the present application, by presenting the abnormal population distribution map, the distribution of sample providers in the big data can be intuitively shown, helping medical staff and sample providers quickly understand the position of the abnormalities suffered by the sample providers among the abnormal population, facilitating understanding of the stage and abnormal conditions of the sample providers in the population, and thus making more accurate and reasonable diagnostic decisions.

[0145] In some embodiments, the auxiliary diagnosis report output module 303 is further configured to output the historical abnormal information of the sample provider through the auxiliary diagnosis report. Among them, the historical abnormal information is used to show the historical test records of the sample provider.

[0146] In some embodiments, the auxiliary diagnosis report output module 303 is specifically configured to: obtain the historical test records of the sample provider, and output the historical abnormal information according to the time axis corresponding to the historical test records. Next, Figure 9 this embodiment will be described in detail.

[0147] Figure 9 It is a schematic diagram of the historical abnormal information of the sample provider provided by the embodiments of the present application. As Figure 9 shown, this figure includes the test date, the test results corresponding to each test date, and the test results of the current test process obtained by combining the historical abnormal information.

[0148] It can be understood that the schematic diagram of abnormal information can be in various forms, such as a line chart, a bar chart, etc. Figure 9 Taking the line chart as an example, but not limited thereto. As Figure 9 shown in the historical abnormal information, the abscissa is the test date, that is, the time axis, and the ordinate is the deviation degree of the erythrocyte series corresponding to each test date.

[0149] When determining the test result, the deviation degree of the current test process can be compared with a preset range. When the deviation value is within the preset range, it indicates that the test result of the sample provider deviates from the normal level less, and the current test result is normal; when the deviation value is not within the preset range, it indicates that the test result of the sample provider deviates from the normal level more, and the current test result is abnormal. It should be noted that the specific value of the preset range is not particularly limited in the embodiments of the present application. For example, the preset range can be [-0.1, 0.1].

[0150] Exemplarily, from Figure 9 it can be obtained that the date of the current test process is "2021 / 9 / 23", and the corresponding deviation value is 0.1, which is within the preset range, indicating that the erythrocyte series of the sample provider deviates from the normal level less. Therefore, the deviation degree of the sample provider is at the normal level, that is, the current test result of the sample provider is normal.

[0151] In the embodiments of the present application, by providing an auxiliary diagnostic report with a high degree of visualization, it is possible to more intuitively understand the current existing or potential abnormal conditions of the sample provider to which the sample to be tested belongs. And in the case where an abnormality is determined, the decision-making basis can be known through the diagnostic basis provided in the auxiliary diagnostic report, and an accurate auxiliary diagnostic result can be obtained without relying on the personal experience level of the inspection medical staff. On the one hand, the inspection efficiency can be improved and the inspection result can be more accurate; on the other hand, diverse auxiliary diagnostic reports such as the auxiliary diagnostic radar chart, abnormal population distribution information obtained from the sample analyzer, and the historical abnormal information of the sample provider of the sample to be tested can be used, so that the diagnostic conclusion is more interpretable, which can assist the inspection doctor to reduce the workload of screening inspection data, and at the same time, the results in the auxiliary diagnostic report can be used to better match the hierarchical diagnosis and treatment policy.

[0152] In addition, by providing a distribution map of abnormal populations, the distribution of sample providers in the big data can be intuitively displayed, which is convenient for medical staff to grasp the index abnormal conditions of sample providers. Furthermore, by providing the historical abnormal information of the sample provider, all the historical test records of the sample provider can be intuitively viewed, and at the same time, its health condition can be understood according to the time axis, which is convenient for tracking the changes in the condition of the sample provider. When making a diagnostic decision, medical staff can fully consider the historical abnormal information of the sample provider and then make a more reasonable decision. At the same time, it can also be intuitively seen what level the current test result is compared with the historical results of the sample provider, and the condition can be tracked and the treatment effect can be viewed accordingly.

[0153] Figure 10 Structural schematic of the auxiliary diagnostic information providing device provided by the embodiments of the present application Figure 2 As Figure 10 shown, in some embodiments, the auxiliary diagnostic information providing device 300 provided by the embodiments of the present application further includes: a data storage module 304 and / or a feedback collection module 305.

[0154] Among them, the data storage module 304 is used to store at least one of the following information: the medical knowledge graph corresponding to the sample test item, the preset literature, the historical test results and diagnostic results of historical samples;

[0155] Among them, the medical knowledge graph includes the target knowledge graph.

[0156] The feedback collection module 305 is used to obtain the review result of the auxiliary diagnostic report and update the target knowledge graph according to the review result.

[0157] In this embodiment, after obtaining the information to be diagnosed, the auxiliary diagnosis analysis module 302 will, according to the test category indicated by the information to be diagnosed, obtain the target knowledge graph corresponding to the information to be diagnosed from the data storage module 304. By storing the knowledge graph in the data storage module 304, the auxiliary diagnosis analysis module 302 can directly match the target knowledge graph with the information to be diagnosed in the data storage module. This process is highly efficient and can ensure the output efficiency of the auxiliary diagnosis report.

[0158] In addition, after the auxiliary diagnosis analysis module 302 performs a sample test and outputs the test result and the diagnosis result, the test result and the diagnosis result can also be stored in the data storage module 304 to obtain the historical test result and the diagnosis result.

[0159] In another embodiment, the target knowledge graph can also be obtained online in real time. Exemplarily, after the auxiliary diagnosis analysis module 302 obtains the information to be diagnosed, it will, according to the test category indicated by the information to be diagnosed, obtain the target knowledge graph corresponding to the information to be diagnosed from the network or other mobile terminals.

[0160] It should be noted that the information to be diagnosed and the target knowledge graph can be in a one-to-one correspondence, or in a one-to-many correspondence, that is, one piece of information to be diagnosed may correspond to one or more target knowledge graphs. By obtaining the target knowledge graph online, the storage pressure of the auxiliary diagnosis information providing device can be decoupled, thereby improving the processing efficiency of the auxiliary diagnosis information providing device.

[0161] In some embodiments, the auxiliary diagnosis report output module 303 is further configured to: according to the historical test results and diagnosis results in the data storage module 304, output Figure 8 the abnormal population distribution information as shown.

[0162] And / or, obtain the historical test records of the sample provider to which the sample to be tested belongs in the data storage module 304, and according to the time axis corresponding to the historical test records, output Figure 9 the historical abnormal information of the sample provider to which the sample to be tested belongs as shown.

[0163] In some embodiments, the feedback collection module 305 is specifically configured to: after obtaining the auxiliary diagnosis report, professionals can review the auxiliary diagnosis report on the client side and give corrective opinions. Correspondingly, the collection feedback module 305 will collect the corrective opinions and optimize the underlying knowledge base model according to the corrective opinions.

[0164] Specifically, the feedback collection module 305 can also collect the usage feedback of users on the auxiliary diagnosis information providing device so that the development team can improve the product. The feedback collection module 305 is specifically used in the following aspects:

[0165] 1. Set a custom button option for the abnormal items in the diagnosis on the result display page. Professionals can judge the results and check the abnormal items with a high possibility.

[0166] 2. Set an option for the medical staff's diagnosis text box. Doctors make their own judgments based on data such as indicators, symptoms, and medical history, and determine and fill in the abnormal items.

[0167] 3. Set a product satisfaction survey to allow users to rate the satisfaction of various aspects of the auxiliary diagnosis information providing device, such as the accuracy of the test and the simplicity of the operation.

[0168] 4. Add a submission button. After the professional fills in the feedback, the feedback content is uploaded to the database of the server, and a statement is added that the feedback is only used for product improvement and will not disclose user information.

[0169] 5. Use a program on the server side to process the submitted feedback. According to the results feedback by the professionals, increase and decrease the weight w of the specified edge (clinical findings - disease). Specifically, the calculation formula of the weight w is as follows:

[0170] W 1 = W 0 *(1 + f)

[0171] Among them, W 0 is the initial weight 1, and f is the weight update speed. The update speed can be customized, for example, it is 0.05.

[0172] Exemplarily, when the disease is checked, f is 0.05, and when it is not checked, f is -0.05. When W 1 is less than 0.1, it is set to 0.1 and will not be updated anymore.

[0173] In this embodiment, by setting up a feedback collection module, the'self - evolution' of the system can be achieved, thereby continuously improving the accuracy of the auxiliary diagnosis information providing device.

[0174] This application embodiment also provides an auxiliary diagnosis information providing device, whose structure is similar to Figure 3 which is not shown here. Specifically, the auxiliary diagnosis information providing device provided by this application embodiment further includes:

[0175] A data processing module, configured to obtain the test data of the sample to be tested, and pre - process the test data to obtain the information to be diagnosed. The information to be diagnosed includes at least one of the following: test items, numerical information corresponding to the test items, and basic information corresponding to the sample to be tested;

[0176] An auxiliary diagnosis analysis module, configured to obtain a target knowledge graph, and match the information to be diagnosed with the target knowledge graph to obtain a matching result;

[0177] An auxiliary diagnosis report output module, configured to output an auxiliary diagnosis report according to the matching result.

[0178] It should be noted that the solutions executed by the above data processing module, auxiliary diagnosis analysis module, and auxiliary diagnosis report output module, and the beneficial effects corresponding to each solution are similar to those described in the above embodiments. For details, reference can be made to the above embodiments, and details will not be elaborated here.

[0179] In the embodiments of the present application, the auxiliary diagnosis report includes at least one of the following: abnormal information corresponding to the sample to be tested, an auxiliary diagnosis radar chart, an abnormal population distribution chart, and historical abnormal information of the sample provider. The auxiliary diagnosis radar chart is used to indicate at least one of the abnormal items corresponding to the sample provider, the probability corresponding to each abnormal item, the first occurrence frequency, and the number of directed edges.

[0180] Among them, the first occurrence frequency is used to indicate the number of times the relationship between the clinical findings of the abnormal item and the abnormal information appears in the literature corresponding to the target knowledge graph, and the number of directed edges is used to indicate the number of clinical findings corresponding to each abnormal item in the target knowledge graph.

[0181] It should be noted that for the acquisition methods of each part of the content in the auxiliary diagnosis report provided in the embodiments of the present application, reference can be made to the foregoing embodiments, and details will not be elaborated here.

[0182] In the embodiments of the present application, by outputting diversified auxiliary diagnosis reports such as an auxiliary diagnosis radar chart, abnormal population distribution information, and historical abnormal information of the sample provider, the sample analysis results of the sample provider can be more intuitively displayed, with a high degree of visualization, making the diagnosis conclusion more interpretable, thereby helping medical staff make more reliable and accurate diagnosis decisions. Specifically, in the embodiments of the present application, by displaying the auxiliary diagnosis radar chart, the abnormal items suffered by the sample provider and the probability of suffering from each abnormal item can be clearly and intuitively displayed, helping medical staff quickly identify the abnormal items suffered by the sample provider, and then making targeted decisions. At the same time, by displaying the abnormal population distribution chart, it can help medical staff and sample providers quickly understand the position of the abnormalities suffered by the sample provider in the abnormal population, facilitating an understanding of the stage and abnormal conditions of the sample provider in the population. Furthermore, by displaying the historical abnormal information of the sample provider, all historical test records of the sample provider can be intuitively viewed, and at the same time, its health status can be understood according to the time axis, facilitating tracking of the disease condition changes of the sample provider, and then making more accurate and reasonable diagnosis decisions.

[0183] Figure 11A schematic diagram of the structure of the auxiliary diagnosis information providing system provided in an embodiment of the present application. Figure 11 As shown, the auxiliary diagnosis information providing system 1100 includes: a sampling component 1101, which is used to collect samples to be tested;

[0184] Reaction component 1102, used for processing the sample to be tested to form a test liquid;

[0185] A driving component 1103 is used to drive the fluid path between the sampling component 1101 and the reaction component 1102;

[0186] The testing component 1104 is used to test the test fluid and obtain test data;

[0187] And an auxiliary diagnosis information providing device 300 as in any one of the above embodiments, wherein the auxiliary diagnosis information providing device 300 is used to output an auxiliary diagnosis report based on the inspection data.

[0188] In this embodiment, the auxiliary diagnosis information providing system 1100 is a medical auxiliary diagnosis device that integrates the inspection data obtained by testing and analyzing the biological sample, and the information to be diagnosed obtained by processing the inspection data, and performs biological sample analysis and outputs a visual diagnosis auxiliary diagnosis report. The auxiliary diagnosis information providing system 1100 can be, for example, a blood analysis system, which can be obtained by upgrading a blood analyzer, including a sampling component, a reaction component, a driving component and a test component for testing and analyzing the blood sample to obtain sample test data. The auxiliary diagnosis information providing device, as a computer program product that implements the auxiliary diagnosis function based on a computer program flow, can be stored in the memory of the blood analysis system, and the auxiliary diagnosis function of the auxiliary diagnosis information providing device of the embodiment of the present application is implemented by the processor.

[0189] In the above embodiments, it should be understood that the disclosed apparatus and its implementation can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of modules is only a logical function division, and there may be other division methods in actual implementation, for example, multiple modules can be combined or integrated into another system, or some features can be ignored or not executed.

[0190] Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or modules, which can be electrical, mechanical or other forms. In addition, each functional module in each embodiment of the present application can be integrated into a processing unit, or each module can exist physically separately, or two or more modules can be integrated into one unit. The above-mentioned module-composed unit can be implemented in the form of hardware or in the form of hardware plus software functional units.

[0191] The integrated modules implemented in the form of software functional modules can be stored in a computer-readable storage medium. The above-mentioned software functional modules are stored in a storage medium and include several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to execute some steps of the methods according to the embodiments of the present application.

[0192] The above storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, a magnetic disk or an optical disk, etc. The storage medium can be any available medium accessible by a general-purpose or special-purpose computer.

Claims

1. An auxiliary diagnosis information providing device, characterized in that, it includes: A data processing module, configured to obtain test data of a sample to be tested, and preprocess the test data to obtain information to be diagnosed, where the information to be diagnosed includes at least one of the following: test items, numerical information corresponding to the test items, and basic information corresponding to the sample to be tested; An auxiliary diagnosis analysis module, configured to obtain a target knowledge graph, and match the information to be diagnosed with the target knowledge graph to obtain a matching result; An auxiliary diagnosis report output module, configured to output an auxiliary diagnosis report according to the matching result, where the auxiliary diagnosis report includes at least one of the following: abnormal information corresponding to the sample to be tested, an auxiliary diagnosis radar chart, an abnormal population distribution chart, and historical abnormal information of the sample provider, and the auxiliary diagnosis radar chart is used to indicate abnormal items corresponding to the sample provider and probabilities corresponding to each of the abnormal items.

2. The auxiliary diagnosis information providing device according to claim 1, characterized in that, the auxiliary diagnosis radar chart is further configured to indicate the number of directed edges of each abnormal item corresponding to the sample to be tested, and the number of directed edges is used to indicate the number of clinical findings corresponding to each abnormal item in the target knowledge graph; the target knowledge graph includes clinical finding nodes and abnormal information nodes, and the abnormal information nodes include abnormal items; the clinical finding nodes and the abnormal information nodes are connected by directed edges, and the direction of the directed edges is used to indicate the hint of the clinical finding to the abnormal information; the auxiliary diagnosis analysis module is specifically configured to: match the information to be diagnosed with the clinical finding nodes in the target knowledge graph, and determine the directed edges corresponding to the clinical findings that match the information to be diagnosed, where each of the directed edges points to an abnormal item in an abnormal information node.

3. The auxiliary diagnosis information providing device according to claim 2, characterized in that, the auxiliary diagnosis radar chart is further configured to indicate the first occurrence frequency corresponding to the abnormal item; the first occurrence frequency is used to indicate the number of occurrences of the correspondence between the clinical finding and the abnormal information of the abnormal item in the literature corresponding to the target knowledge graph; the auxiliary diagnosis analysis module is specifically configured to: according to the test items and / or the numerical information corresponding to the test items indicated in the information to be diagnosed, search for the abnormal items corresponding to the information to be diagnosed in the target knowledge graph, and obtain the first occurrence frequency of at least one abnormal item corresponding to the sample to be tested according to the search result.

4. The auxiliary diagnosis information providing device according to claim 3, characterized in that, the auxiliary diagnosis analysis module is further configured to: determine the probability of a first abnormal item whose first occurrence frequency meets a first preset requirement among the abnormal items; according to the probability, determine a second abnormal item whose probability meets a second preset requirement among the first abnormal items; the auxiliary diagnosis report output module is further configured to: obtain the auxiliary diagnosis radar chart according to the probability corresponding to the second abnormal item.

5. The auxiliary diagnosis information providing device according to claim 4, It is characterized in that the auxiliary diagnosis analysis module is further configured to: when the first occurrence frequencies of at least two abnormal items corresponding to the sample to be tested are the same, obtain the second occurrence frequencies of the abnormal items with the same frequency in a preset document, and determine the first abnormal items and the probabilities corresponding to each of the first abnormal items according to the first occurrence frequencies and the second occurrence frequencies.

6. The auxiliary diagnosis information providing device according to claim 3, It is characterized in that the auxiliary diagnosis radar chart includes sector areas respectively corresponding to each of the abnormal items; the sector area displays at least one of the first occurrence frequency, probability level, and number of directed edges of the abnormal item through at least one of the radius length, central angle, and area color.

7. The auxiliary diagnosis information providing device according to claim 6, It is characterized in that it further includes: a display module, and the display module is configured to: in response to a selection instruction for the sector area of the auxiliary diagnosis radar chart, display a diagnosis description of the abnormal item corresponding to the sector area; the selection instruction includes at least one of moving the cursor to the sector area, a click operation on the sector area, and the cursor staying in the sector area for more than a predetermined time; the diagnosis description includes at least one of the following: diagnosis basis, test item, or abnormal item description.

8. The auxiliary diagnosis information providing device according to any one of claims 1 to 7, It is characterized in that: the abnormal population distribution map includes the population distribution information of the abnormal population and the position of the sample provider in the abnormal population; the auxiliary diagnosis report output module is specifically configured to: output the population distribution information according to the historical test results, and mark the position of the sample provider in the population distribution information according to the diagnosis result corresponding to the sample to be tested, so as to obtain the abnormal population distribution map; the historical abnormal information is used to display the historical test records of the sample provider; the auxiliary diagnosis report output module is further configured to: obtain the historical test records of the sample provider, and output the historical abnormal information according to the time axis corresponding to the historical test records.

9. An auxiliary diagnosis information providing device, It is characterized in that it includes: a data processing module, configured to obtain test data of a sample to be tested, and preprocess the test data to obtain information to be diagnosed, where the information to be diagnosed includes at least one of the following: test item, numerical information corresponding to the test item, and basic information corresponding to the sample to be tested; an auxiliary diagnosis analysis module, configured to obtain a target knowledge graph, and match the information to be diagnosed with the target knowledge graph to obtain a matching result; an auxiliary diagnosis report output module, configured to output an auxiliary diagnosis report according to the matching result, where the auxiliary diagnosis report includes at least one of the following: abnormal information corresponding to the sample to be tested, an auxiliary diagnosis radar chart, an abnormal population distribution map, and historical abnormal information of the sample provider, and the auxiliary diagnosis radar chart is used to indicate at least one of the abnormal items corresponding to the sample provider, the probabilities corresponding to each of the abnormal items, the first occurrence frequency, and the number of directed edges. The first occurrence frequency is used to indicate the number of occurrences of the relationship between the clinical findings of the abnormal item and the abnormal information in the literature corresponding to the target knowledge graph, and the number of directed edges is used to indicate the number of clinical findings corresponding to each abnormal item in the target knowledge graph.

10. An auxiliary diagnosis information providing system, characterized in that it includes: a sampling component for collecting a sample to be tested; a reaction component for processing the sample to be tested to form a liquid to be tested; a driving component for driving the liquid path between the sampling component and the reaction component; a testing component for testing the liquid to be tested to obtain test data; and the auxiliary diagnosis information providing device according to any one of claims 1 to 9, the auxiliary diagnosis information providing device being used to output an auxiliary diagnosis report based on the test data.

Images