Intelligent agent-based large-model hospital infection judgment method and system

Abstract

The invention discloses a large model hospital infection judgment method and system based on an intelligent agent, and relates to the technical field of risk assessment, and the method comprises the steps: obtaining a target physiological feature of a target patient; the target physiological feature at least comprises one of a disease course description, an examination result and an inspection result of the target patient; retrieving a hospital infection knowledge map according to the target physiological features to obtain a corresponding target sub-map; wherein the hospital infection knowledge graph is constructed according to a hospital infection diagnosis standard document; generating a relation vector set according to the target sub-atlas to obtain a target relation vector set; and inputting the target physiological features and the target relationship vector set into a pre-trained hospital infection prediction model to obtain a target infection outcome, thereby reducing the data set screening difficulty of the hospital infection prediction model, and improving the prediction accuracy of hospital infection prediction.

Description

Technical Field

[0001] The present application relates to the technical field of risk assessment, and in particular to a large-model hospital infection determination method and system based on an intelligent agent. Background Art

[0002] Nosocomial infection refers to the infection acquired by inpatients in the hospital, including infections that occurred during hospitalization and infections that occurred after discharge, but excluding infections that started before admission or were in the latent stage at admission. Monitoring whether patients have nosocomial infection not only helps to take effective treatment measures in a timely manner and protect patient safety, but also helps to identify the source of infection as early as possible and control the development trend of infection.

[0003] There are methods for monitoring whether patients have nosocomial infection in the prior art. For example, the patent application number CN201910347848.4 is a method and system for intelligent diagnosis of nosocomial infection based on multi-model fusion. The case data of historical patients are used as the training data set of multiple base models. After the training is completed, the recognition performance of each base model is evaluated, and at least two base models with the best performance are fused by the stacking method to obtain a diagnostic model for diagnosing whether the patient has nosocomial infection. While realizing the monitoring of whether the patient has nosocomial infection, the accuracy of nosocomial infection recognition is improved by the integrated learning method. However, this method essentially simulates the data distribution characteristics in the real world with the data distribution characteristics in the training data set, and the diagnostic experience learned by the diagnostic model is the diagnostic experience implied by the data distribution characteristics in the training data set. When the data distribution characteristics in the training data set contain misleading information or bias, the recognition accuracy of the diagnostic model will decrease as the proportion of misleading information or bias contained in the training data set increases, which leads to the problem that the data set selection in the model training stage is difficult and the diagnostic accuracy is not high. Summary of the invention

[0004] The purpose of the present invention is to solve the technical problem of how to reduce the difficulty of selecting training data sets for hospital infection diagnosis models and improve the accuracy of diagnosis, and to provide a large-model hospital infection determination method and system based on an intelligent agent.

[0005] According to a first aspect of the present invention, the present invention claims a large model hospital infection determination method based on an intelligent agent, comprising: Acquire a target physiological characteristic of a target patient; the target physiological characteristic includes at least one of a description of the course of disease, a test result, and a test result of the target patient; Retrieving a hospital infection knowledge graph according to the target physiological characteristics to obtain a corresponding target sub-graph; wherein the hospital infection knowledge graph is constructed according to a hospital infection diagnosis standard document; Generate a relationship vector set according to the target sub-graph to obtain a target relationship vector set; The target physiological characteristics and the target relationship vector set are input into a pre-trained hospital infection prediction model to obtain a target infection outcome.

[0006] In one embodiment of the present application, the hospital infection knowledge graph includes entities and relationships; the entity types of the entities include symptoms, test results, examination results and diagnosis results of each infection site; the relationships include the relationship between different symptoms, the relationship between different test results, the relationship between different examination results, the relationship between symptoms and test results, the relationship between symptoms and examination results, the relationship between test results and examination results, the relationship between symptoms and diagnosis results, the relationship between examination results and diagnosis results, and the relationship between test results and diagnosis results.

[0007] In one embodiment of the present application, the entity type also includes the diagnostic criteria for each infection site; the relationship between the symptoms and the diagnostic results is obtained based on the relationship between the corresponding symptoms and the diagnostic criteria and the relationship between the diagnostic criteria and the corresponding diagnostic results; the relationship between the examination results and the diagnostic results is obtained based on the relationship between the corresponding examination results and the diagnostic criteria and the relationship between the diagnostic criteria and the corresponding diagnostic results; the relationship between the test results and the diagnostic results is obtained based on the relationship between the corresponding test results and the diagnostic criteria and the relationship between the diagnostic criteria and the corresponding diagnostic results.

[0008] In one embodiment of the present application, the relationship vector set includes a premise entity, a conclusion entity and a weight coefficient, and the weight coefficient is used to evaluate the credibility of the patient's physiological characteristics belonging to the conclusion entity based on the patient's physiological characteristics belonging to the premise entity.

[0009] In one embodiment of the present application, the weight coefficient is obtained according to the proportion of positive examples; wherein, the proportion of positive examples is obtained according to the proportion of historical patients belonging to the conclusion entity among the historical patients belonging to the premise entity.

[0010] In one embodiment of the present application, the relationship type of the relationship includes a subordinate relationship and an associated relationship. When the relationship type of the relationship is the subordinate relationship, the weight coefficient of the corresponding relationship is obtained according to the positive example proportion. When the relationship type of the relationship is the associated relationship, the weight coefficient of the corresponding relationship is obtained according to the positive example proportion and the negative example proportion, wherein the negative example proportion is obtained according to the proportion of historical patients belonging to the conclusion entity among the historical patients who do not belong to the premise proportion.

[0011] In an embodiment of the present application, when the relationship type is the association relationship, the weight coefficient of the corresponding relationship is obtained according to the difference between the positive example ratio and the negative example ratio.

[0012] In one embodiment of the present application, when the relationship type is the subordinate relationship, the value of the weight coefficient of the corresponding relationship is equal to the positive example ratio, and when the relationship type is the association relationship, the value of the weight coefficient of the corresponding relationship is equal to the absolute value of the difference between the positive example ratio and the negative example ratio. In the retrieval of the hospital infection knowledge graph according to the target physiological characteristics, it also includes: Matching entities in the hospital infection knowledge graph according to the target physiological characteristics, and taking the successfully matched entities as target entities; Determine whether the number of the target entity is 0, if so, terminate the search, if not, update the target subgraph; query all the relationships in the hospital infection knowledge graph where the premise entity is the target entity, and obtain candidate relationships; Matching each of the candidate relations with the target subgraph, and removing the candidate relations that successfully match the target subgraph; The conclusion entities corresponding to the remaining candidate relations are taken as candidate entities, and all the candidate relations are divided into a plurality of target relation sets according to the conclusion entities; each target relation set is divided into a plurality of target relation subsets according to the relation type; Obtaining a screening weight corresponding to each of the target relationship subsets; if the relationship type corresponding to the target relationship subset is a subordinate relationship, taking the maximum value of the weight coefficients in the corresponding target relationship subset as the weight coefficient; if the relationship type corresponding to the target relationship subset is an association relationship, counting the number of relationships in the target relationship subset whose positive example ratio is 0 or whose negative example ratio is 0, obtaining a first value, and calculating the sum of all the weight coefficients in the corresponding target relationship subset and the first value to obtain the screening weight; Obtaining a screening threshold corresponding to each of the target relationship subsets, where the value of the screening threshold is greater than 0; comparing the screening weight corresponding to each of the target relationship subsets with the corresponding screening threshold, and when the screening weight is less than the screening threshold, setting the screening result of the corresponding target relationship subset to fail; When all the screening results corresponding to the target relationship set are failed, the corresponding relationship is removed from the candidate relationship, the corresponding candidate entity is removed from the target entity, the remaining candidate entity is used as the new target entity, and the step of determining whether the number of target entities is 0 is returned to continue execution.

[0013] In one embodiment of the present application, after inputting the target physiological characteristics and the target relationship vector set into a pre-trained hospital infection prediction model, it also includes obtaining the actual infection outcome of the target patient, and updating the weight coefficient in the hospital infection knowledge graph according to the actual infection outcome.

[0014] According to the second aspect of the present invention, the present invention requests protection for a large-model hospital infection determination system based on an intelligent agent, comprising: a knowledge graph construction module, a physiological feature acquisition module, a knowledge graph retrieval module, a relationship vector generation module and an infection outcome prediction module.

[0015] A physiological characteristic acquisition module, wherein the physiological characteristic acquisition module is used to acquire a target physiological characteristic of a target patient; the target physiological characteristic includes at least one of a description of the course of disease, an examination result, and a test result of the target patient; A knowledge graph retrieval module, the knowledge graph retrieval module is used to retrieve the hospital infection knowledge graph according to the target physiological characteristics to obtain the corresponding target sub-graph; wherein the hospital infection knowledge graph is constructed according to the hospital infection diagnosis standard document; A relationship vector generation module, wherein the relationship vector generation module is used to generate a relationship vector set according to the target sub-graph to obtain a target relationship vector set; An infection outcome prediction module is used to input the target physiological characteristics and the target relationship vector set into a pre-trained hospital infection prediction model to obtain a target infection outcome.

[0016] In one embodiment of the present application, in the knowledge graph construction module, the hospital infection knowledge graph includes entities and relationships; the entity types of the entities include symptoms, test results, examination results and diagnosis results of each infection site; the relationships include the relationship between different symptoms, the relationship between different test results, the relationship between different examination results, the relationship between symptoms and test results, the relationship between symptoms and examination results, the relationship between test results and examination results, the relationship between symptoms and diagnosis results, the relationship between examination results and diagnosis results, and the relationship between test results and diagnosis results.

[0017] In one embodiment of the present application, in the knowledge graph construction module, the entity type also includes the diagnostic criteria for each infection site; the relationship between the symptoms and the diagnostic results is obtained according to the relationship between the corresponding symptoms and the diagnostic criteria and the relationship between the diagnostic criteria and the corresponding diagnostic results; the relationship between the examination results and the diagnostic results is obtained according to the relationship between the corresponding examination results and the diagnostic criteria and the relationship between the diagnostic criteria and the corresponding diagnostic results; the relationship between the test results and the diagnostic results is obtained according to the relationship between the corresponding test results and the diagnostic criteria and the relationship between the diagnostic criteria and the corresponding diagnostic results.

[0018] In one embodiment of the present application, the relationship vector set includes a premise entity, a conclusion entity and a weight coefficient, and the weight coefficient is used to evaluate the credibility of the patient's physiological characteristics belonging to the conclusion entity based on the patient's physiological characteristics belonging to the premise entity.

[0019] In one embodiment of the present application, the weight coefficient is obtained according to the proportion of positive examples; wherein, the proportion of positive examples is obtained according to the proportion of historical patients belonging to the conclusion entity among the historical patients belonging to the premise entity.

[0020] In one embodiment of the present application, the relationship type of the relationship includes a subordinate relationship and an associated relationship. When the relationship type of the relationship is the subordinate relationship, the weight coefficient of the corresponding relationship is obtained according to the positive example proportion. When the relationship type of the relationship is the associated relationship, the weight coefficient of the corresponding relationship is obtained according to the positive example proportion and the negative example proportion, wherein the negative example proportion is obtained according to the proportion of historical patients belonging to the conclusion entity among the historical patients who do not belong to the premise proportion.

[0021] In an embodiment of the present application, when the relationship type is the association relationship, the weight coefficient of the corresponding relationship is obtained according to the difference between the positive example ratio and the negative example ratio.

[0022] In an embodiment of the present application, when the relationship type is the subordinate relationship, the value of the weight coefficient of the corresponding relationship is equal to the positive example ratio, and when the relationship type is the association relationship, the value of the weight coefficient of the corresponding relationship is equal to the absolute value of the difference between the positive example ratio and the negative example ratio. The knowledge graph retrieval module also includes: Matching entities in the hospital infection knowledge graph according to the target physiological characteristics, and taking the successfully matched entities as target entities; Determine whether the number of the target entity is 0, if so, terminate the search, if not, update the target subgraph; query all the relationships in the hospital infection knowledge graph where the premise entity is the target entity, and obtain candidate relationships; Matching each of the candidate relations with the target subgraph, and removing the candidate relations that successfully match the target subgraph; The conclusion entities corresponding to the remaining candidate relations are taken as candidate entities, and all the candidate relations are divided into a plurality of target relation sets according to the conclusion entities; each target relation set is divided into a plurality of target relation subsets according to the relation type; Obtaining a screening weight corresponding to each of the target relationship subsets; if the relationship type corresponding to the target relationship subset is a subordinate relationship, taking the maximum value of the weight coefficients in the corresponding target relationship subset as the weight coefficient; if the relationship type corresponding to the target relationship subset is an association relationship, counting the number of relationships in the target relationship subset whose positive example ratio is 0 or whose negative example ratio is 0, obtaining a first value, and calculating the sum of all the weight coefficients in the corresponding target relationship subset and the first value to obtain the screening weight; Obtaining a screening threshold corresponding to each of the target relationship subsets, where the value of the screening threshold is greater than 0; comparing the screening weight corresponding to each of the target relationship subsets with the corresponding screening threshold, and when the screening weight is less than the screening threshold, setting the screening result of the corresponding target relationship subset to fail; When all the screening results corresponding to the target relationship set are failed, the corresponding relationship is removed from the candidate relationship, the corresponding candidate entity is removed from the target entity, the remaining candidate entity is used as the new target entity, and the step of determining whether the number of target entities is 0 is returned to continue execution.

[0023] In one embodiment of the present application, the system further includes a knowledge graph updating module, which is used to obtain the actual infection outcome of the target patient and update the weight coefficient in the hospital infection knowledge graph according to the actual infection outcome.

[0024] This application has the following beneficial effects: 1. When judging the infection outcome of the target patient through the hospital infection prediction model, a hospital infection knowledge graph is generated according to the hospital infection diagnostic standard document, and the mapping result of the patient's physiological characteristics on the hospital infection knowledge graph is used as one of the diagnostic criteria of the hospital infection prediction model. When the errors or biases in the training data set of the hospital infection prediction model cause the predicted infection outcome to differ from the actual infection outcome, the predicted infection outcome can be corrected by the relationship vector generated based on the knowledge graph, thereby reducing the difficulty of data set screening of the hospital infection prediction model and improving the interpretability of the model prediction while improving the prediction accuracy.

[0025] 2. By mining the relationship between different symptoms, the relationship between different test results, and the relationship between different examination results, it is helpful to improve the diagnostic scope of the hospital infection prediction model. At the same time, when constructing the conceptual model of the hospital infection knowledge graph, the relationship between symptoms and test results, the relationship between symptoms and examination results, and the relationship type between test results and examination results are established. By extracting the relationship between different categories of entities in the hospital infection diagnosis standard document, it is helpful to improve the recognition accuracy of hospital infection. In particular, when it is difficult to determine whether the patient has an infection in a designated part based on only one of the patient's symptoms, test results, and examination results, the relationship between symptoms and test results, and the relationship between test results and examination results stored in the knowledge graph can be used to infer another entity from one entity, thereby completing the diagnosis of hospital infection and reducing the missed diagnosis rate of hospital infection.

[0026] 3. Use diagnostic criteria as entities in the hospital infection knowledge graph to store different diagnostic criteria for the same infection and the relationship between them. This makes it easier to describe the logical relationship between different diagnostic criteria when separating the corresponding subgraphs from the hospital infection knowledge graph based on the patient's physiological characteristics. For example, when judging whether a patient's urinary system is infected by the diagnostic criteria of etiological diagnosis, it is still necessary to do so based on the diagnostic criteria of clinical diagnosis. It is difficult to confirm whether a patient has a urinary system infection if only relying on the results of etiological tests.

[0027] 4. Using the relationship vector containing the weight coefficient as one of the inputs of the hospital infection prediction model, in addition to enriching the characteristics of the relationship between entities and improving the accuracy of the hospital infection prediction model, it can also be used to identify the "false" relationship between entities extracted from the hospital infection diagnosis standard document and the historical patient data set, that is, the relationship in which the value of the weight coefficient is too small. The recognition result of the hospital infection prediction model trained based on the relationship vector containing the weight coefficient is further reduced by the error or bias in the historical patient data set, thereby improving the recognition accuracy.

[0028] 5. Calculate the weight coefficient based on the positive example ratio. The weight coefficient can be used not only to evaluate the credibility of the knowledge, but also to evaluate the causal relationship between the premise entity and the conclusion entity. Under the same other conditions, the larger the value of the positive example ratio, the greater the probability that the patient's physiological characteristics belong to the conclusion entity when the patient's physiological characteristics belong to the premise entity; the smaller the value of the positive example ratio, the smaller the probability that the patient's physiological characteristics belong to the conclusion entity when the patient's physiological characteristics belong to the premise entity. In particular, when the value of the positive example ratio is 1, it means that when the patient's physiological characteristics belong to the premise entity, the patient's physiological characteristics must belong to the conclusion entity.

[0029] 6. In actual application scenarios, when the relationship type of the relationship is the association relationship, the relationship between entities can be a positive correlation relationship or a negative correlation relationship. At the same time, the weight coefficient calculated based on the positive example ratio and the negative example ratio can be used to evaluate the correlation between two entities with a positive association relationship, or can be used to evaluate the correlation between two entities with a negative association relationship.

[0030] 7. The weight coefficient is calculated by the difference between the positive example ratio and the negative example ratio, thereby evaluating the credibility of knowledge according to the correlation between two entities.

[0031] 8. The technical solution of the present application achieves targeted reduction of the number of relationships in the retrieved target sub-graph, reduces the computational complexity of the hospital infection prediction model, and improves the prediction speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0033] Figure 1 A flowchart of a large-model hospital infection determination method based on an intelligent agent according to an embodiment of the present application; Figure 2 A schematic diagram of an implementation method of the hospital infection knowledge graph involved in the embodiment of the present application; Figure 3 A schematic diagram of another implementation of the hospital infection knowledge graph involved in the embodiment of the present application; Figure 4 A schematic diagram of the retrieval process of the target sub-graph involved in the embodiment of the present application; Figure 5 This is a schematic diagram of the structure of the large-scale model hospital infection determination system based on the agent involved in the embodiment of the present application. Figure 6A schematic diagram of the structure of an electronic device involved in an embodiment of the present application; Symbols in the figure: ω1-the premise entity is the clinical diagnostic standard 1, and the conclusion entity is the weight coefficient of the upper respiratory tract infection, ω2-the premise entity is the etiological diagnostic standard 1, and the conclusion entity is the weight coefficient of the upper respiratory tract infection, ω3-the premise entity is acute inflammation of the upper respiratory tract, and the conclusion entity is the weight coefficient of the clinical diagnostic standard 1, ω4-the premise entity is fever, and the conclusion entity is the weight coefficient of the clinical diagnostic standard 1, ω5-the premise entity is the secretion smear found to be significant pathogenic microorganisms related to the upper respiratory tract, and the conclusion entity is the weight coefficient of the etiological diagnostic standard 1, ω6-the premise entity is the secretion culture found to be significant pathogenic microorganisms related to the upper respiratory tract, and the conclusion entity is the weight coefficient of the etiological diagnostic standard 1, ω7-the premise entity ω8-the premise entity is fever, and the conclusion entity is the weight coefficient of clinical diagnostic criteria 2. ω9-the premise entity is clinical diagnostic criteria 2, and the conclusion entity is the weight coefficient of lower respiratory tract infection. ω10-the premise entity is etiological diagnostic criteria 2, and the conclusion entity is the weight coefficient of lower respiratory tract infection. ω11-the premise entity is acute inflammation of tonsils, and the conclusion entity is the weight coefficient of acute inflammation of the upper respiratory tract. ω12-the premise entity is acute inflammation of nasopharynx, and the conclusion entity is the weight coefficient of acute inflammation of the upper respiratory tract. ω13-the premise entity is nasal congestion, and the conclusion entity is the weight coefficient of acute inflammation of nasopharynx. ω14-the premise entity is nasal congestion, and the conclusion entity is general The weight coefficient of the common cold is ω15, the weight coefficient of the premise entity is the common cold and the conclusion entity is the upper respiratory tract infection, ω1'-the premise entity is the upper respiratory tract infection and the conclusion entity is the weight coefficient of the clinical diagnostic standard 1, ω2'-the premise entity is the upper respiratory tract infection and the conclusion entity is the weight coefficient of the etiological diagnostic standard 1, ω3'-the premise entity is the clinical diagnostic standard 1 and the conclusion entity is the weight coefficient of acute inflammation of the upper respiratory tract, ω4'-the premise entity is the clinical diagnostic standard 1 and the conclusion entity is the weight coefficient of fever, ω5'-the premise entity is the etiological diagnostic standard 1 and the conclusion entity is the weight coefficient of the secretion smear finding of significant pathogenic microorganisms related to the upper respiratory tract, ω6'-the premise entity is the etiological diagnostic standard 1 and the conclusion entity is the weight coefficient of the pathogenic microorganisms related to the upper respiratory tract The theoretical entity is the weight coefficient of the significant pathogenic microorganisms related to the upper respiratory tract found in the secretion culture, ω7'-the premise entity is the weight coefficient of the upper respiratory tract infection and the conclusion entity is the acute inflammation of the upper respiratory tract caused by allergies, ω8'-the premise entity is the weight coefficient of the clinical diagnostic standard 2 and the conclusion entity is the fever, ω9'-the premise entity is the weight coefficient of the lower respiratory tract infection and the conclusion entity is the weight coefficient of the clinical diagnostic standard 2, ω10'-the premise entity is the lower respiratory tract infection and the conclusion entity is the weight coefficient of the etiological diagnostic standard 2, ω11'-the premise entity is the acute inflammation of the upper respiratory tract and the conclusion entity is the acute inflammation of the tonsils, ω12'-the premise entity is the acute inflammation of the upper respiratory tract and the conclusion entity is the acute inflammation of the nasopharynx,ω13'-the weight coefficient of the premise entity being acute nasopharyngeal inflammation and the conclusion entity being nasal congestion, ω14'-the weight coefficient of the premise entity being common cold and the conclusion entity being nasal congestion, ω15'-the weight coefficient of the premise entity being upper respiratory tract infection and the conclusion entity being common cold. DETAILED DESCRIPTION

[0034] The present invention provides a large model hospital infection determination method and system based on an intelligent agent. In order to make the above-mentioned purpose, features and advantages of the present application more obvious and easy to understand, the technical scheme in the embodiment of the present application will be clearly and completely described in conjunction with the drawings in the embodiment of the present application. Obviously, the described embodiment is only a part of the embodiment of the present application, not all embodiments. The components of the embodiment of the present application described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, with reference to the terms "one embodiment", "some embodiments", "implementation method", "embodiment", "illustrative embodiment", "example", "specific example" or "some examples", the following detailed description of the embodiment of the present application provided in the drawings is not intended to limit the scope of the present application to be protected, but only represents that the specific features, structures or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. And the specific features, structures or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0035] It should be noted that similar reference numerals and letters represent similar items in the following figures, so once an item is defined in one figure, it does not need to be further defined and explained in the subsequent figures. At the same time, in the description of this application, the terms "first", "second" and other relational terms are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0036] According to the first aspect of the present invention, the present invention claims a large model hospital infection determination method based on an intelligent agent, referring to the attached Figure 1 As shown, including: S1: Obtain the physiological characteristics of historical patients and obtain historical physiological characteristics; obtain the infection outcomes of historical patients and obtain historical infection outcomes.

[0037] It should be noted that the physiological characteristics include at least one of the course of disease description, examination results and test results, but it is not limited thereto. The infection outcome is used to describe the patient's current infection status, such as whether there is infection, the location of infection, etc., but it is not limited thereto.

[0038] In this embodiment, the infection outcome includes a set of infection sites of the patient. When the patient is infected, the corresponding set of infection sites is the site of infection of the patient; when the patient is not infected, the corresponding set of infection sites is an empty set.

[0039] S2: Generate a hospital infection diagnosis knowledge graph based on the hospital infection diagnosis standard document.

[0040] It should be noted that the hospital infection diagnosis standard document can be formulated based on the specific conditions of the diseases of the patients admitted to the medical institution, or it can be the "Hospital Infection Diagnosis Standard" issued by the National Health Commission of the People's Republic of China, or a combination of multiple different hospital infection diagnosis standards to obtain the hospital infection diagnosis standard document, or other diagnostic standard files that can be used for hospital infection diagnosis. This application does not limit the specific number and source of hospital infection diagnosis standard documents.

[0041] In this embodiment, step S2 also includes: S21, obtaining the conceptual model of the hospital infection diagnosis knowledge graph. The conceptual model includes entities and relationships. The construction of the conceptual model of the hospital infection diagnosis knowledge graph can be completed through knowledge graph open source tools such as protégé, NeOn Toolkit, Altova SemanticWorks, TopBraid Composer, etc. S22, identifying and extracting entities and relationships in the hospital infection diagnosis standard document through natural language processing technology. S23, filling the conceptual model of the hospital infection diagnosis knowledge graph according to the extraction results of the hospital infection diagnosis standard document to obtain the hospital infection diagnosis knowledge graph. S24, verifying the hospital infection diagnosis knowledge graph according to the historical physiological characteristics and the corresponding historical infection outcomes.

[0042] In this embodiment, refer to the attached Figure 2 As shown, in step S21, the entities in the hospital knowledge graph can be described as entity types and entity names, but it is not limited to this. The entity types include symptoms, test results, examination results, diagnostic criteria for infection sites, and diagnostic results for infection sites, but it is not limited to this. The relationships include the relationship between different symptoms, the relationship between different test results, the relationship between different examination results, the relationship between symptoms and test results, the relationship between symptoms and examination results, the relationship between test results and examination results, the relationship between diagnostic criteria and symptoms, the relationship between diagnostic criteria and test results, the relationship between diagnostic criteria and examination results, and the relationship between diagnostic criteria and diagnostic results, but it is not limited to this. Among them, the diagnostic criteria include clinical diagnosis and etiological diagnosis, but it is not limited to this.

[0043] It should be noted that when constructing the conceptual model of the hospital infection knowledge graph, the relationship between symptoms and test results, the relationship between symptoms and examination results, and the relationship type between test results and examination results are established, and the relationship between different categories of entities in the hospital infection diagnosis standard document is extracted, which helps to improve the accuracy of hospital infection recognition. In particular, when it is difficult to determine whether the patient's designated part is infected based on only one of the patient's symptoms, test results, and examination results, the relationship between symptoms and test results, the relationship between symptoms and examination results, and the relationship between test results and examination results stored in the knowledge graph can be used to infer another entity from one entity, thereby completing the diagnosis of hospital infection and reducing the missed diagnosis rate of hospital infection.

[0044] In this embodiment, the diagnostic criteria are used as entities of the hospital infection knowledge graph, and the relationship between different diagnostic criteria for the same infection and the diagnostic criteria is stored. When the corresponding subgraph is separated from the hospital infection knowledge graph according to the patient's physiological characteristics, it is convenient to describe the logical relationship between different diagnostic criteria. For example, when judging whether the patient's urinary system is infected by the diagnostic criteria of etiological diagnosis, it is still necessary to do so on the basis of the diagnostic criteria of clinical diagnosis. It is difficult to confirm whether the patient has a urinary system infection if only relying on the results of etiological tests.

[0045] S3: Retrieve the hospital infection diagnosis knowledge graph according to each of the historical physiological characteristics to obtain a corresponding historical sub-graph.

[0046] In this embodiment, step S3 also includes: S31, identifying the entity corresponding to the historical physiological feature to obtain the historical entity. The historical entity can be identified from the historical physiological feature by named entity recognition (NER) technology. S32, matching the entity in the hospital infection diagnosis knowledge graph with the historical entity, extracting a subgraph from the hospital infection diagnosis knowledge graph according to the successfully matched historical entity, and obtaining the historical subgraph. The size of the subgraph can be determined by a pre-set method.

[0047] S4: Convert each of the historical sub-graphs into a relationship vector set to obtain a corresponding historical relationship vector set.

[0048] It should be noted that the relationship vector set is a set of all relationship vectors corresponding to the subgraph. Each relationship vector is used to describe the relationship between entities in the subgraph. The relationship vector can be represented by (first entity, relationship name, second entity). For example, the vector ("fever", "related symptoms", "clinical diagnostic criteria for upper respiratory tract infection") indicates that "fever" is one of the related symptoms of "clinical diagnostic criteria for upper respiratory tract infection".

[0049] S5: taking each of the historical relationship vectors, the corresponding historical physiological characteristics and the corresponding historical infection outcomes as sample data, all sample data constitute a training data set of a hospital infection prediction model, and training the hospital infection prediction model according to the training data set.

[0050] It should be noted that the hospital infection prediction model can be a large language model built based on transformer, which is used to generate corresponding answers according to user questions. For example, the hospital infection prediction model is established according to the GPT series, BERT, T5, etc., the hospital infection prediction model is pre-trained according to the Internet Chinese text prediction data set, and the pre-trained hospital infection prediction model is supervised and fine-tuned through the training data set. The hospital infection prediction model can also be a large model built based on a neural network model, which is used to obtain the specific value of the preset output result according to the specific value of the preset input feature.

[0051] In this embodiment, a large model constructed based on a neural network model is used as an example for detailed description. The input of the hospital infection prediction model includes physiological characteristics and relationship vectors, and the output includes corresponding predicted infection outcomes. When training the hospital infection prediction model, the historical physiological characteristics and the historical relationship vectors in each of the sample data are used as the input of the hospital infection prediction model, and the corresponding set of infected sites is output, that is, the predicted set of infected sites. The hospital infection prediction model is trained according to a preset loss function, and the loss function is used to calculate the difference between the predicted infection outcomes of all sample data and the historical infection outcomes. The loss function can select the intersection and union ratio between the predicted set of infection sites and the actual set of infection sites.

[0052] S6: Acquire physiological characteristics of a target patient to obtain target physiological characteristics.

[0053] S7: Retrieve the hospital infection knowledge graph according to the target physiological characteristics to obtain the corresponding target sub-graph. Generate the relationship vector set according to the target sub-graph to obtain the target relationship vector set. The method of obtaining the target relationship vector according to the target physiological characteristics is similar to that of step S4 and step S5, and will not be described in detail here.

[0054] S8: Input the target relationship vector and the target physiological feature into the trained hospital infection prediction model to obtain the target infection outcome.

[0055] It should be noted that when judging the infection outcome of the target patient through the hospital infection prediction model, a hospital infection knowledge graph is generated according to the hospital infection diagnostic standard document, and the mapping result of the patient's physiological characteristics on the hospital infection knowledge graph is used as one of the diagnostic criteria of the hospital infection prediction model. When errors or biases in the training data set of the hospital infection prediction model cause the predicted infection outcome to differ from the actual infection outcome, the predicted infection outcome can be corrected through the relationship vector generated based on the knowledge graph, thereby reducing the difficulty of data set screening of the hospital infection prediction model and improving the interpretability of the model prediction while improving the prediction accuracy.

[0056] In a possible implementation, refer to the attached Figure 3 As shown, the relationship types of the relationship include subordinate relationship and associated relationship, which may not be limited to this. Among them, the subordinate relationship is used to indicate that there is a containment relationship between two entities with different referents. For example, the subordinate relationship between "acute inflammation of tonsils" and "acute inflammation of the upper respiratory tract" indicates that "acute inflammation of tonsils" belongs to one of "acute inflammation of the upper respiratory tract". Among them, the associated relationship is used to describe the correlation between two entities with different referents. For example, the associated relationship between "acute inflammation of nasopharynx" and "nasal congestion" indicates that although "nasal congestion" is one of the main symptoms of "acute inflammation of nasopharynx", patients with "nasal congestion" do not necessarily belong to "acute inflammation of nasopharynx", and patients with "acute inflammation of nasopharynx" do not necessarily have "nasal congestion", but may also have other symptoms of "acute inflammation of nasopharynx". In particular, when establishing the conceptual model of the hospital infection knowledge graph, it is pre-set that when the equivalence relationship between two entities in the conceptual model is the subordinate relationship or the associated relationship, such as the scientific name and alias of the same symptom. In this implementation, an equivalence relationship is defined as a special subordinate relationship, that is, when two entities are equivalent, the relationship type of their corresponding relationship is the subordinate relationship.

[0057] In this embodiment, the relationship vector set includes a premise entity, a conclusion entity and a weight coefficient, and the weight coefficient is used to evaluate the credibility of the patient's physiological characteristics belonging to the conclusion entity based on the patient's physiological characteristics belonging to the premise entity.

[0058] It should be noted that the relationship between the weight coefficient and the credibility is preset. For example, it can be preset that the higher the credibility, the larger the weight coefficient, or the lower the credibility, the smaller the weight coefficient, or other feasible settings.

[0059] It should be noted that the relationship vector can be obtained based on the vector (premise entity, relationship type, conclusion entity, weight coefficient), or based on the vector (premise entity, conclusion entity, weight coefficient), and of course can also be obtained through other feasible methods.

[0060] In this embodiment, the weight coefficient and vector (premise entity, relationship type, conclusion entity, weight coefficient) corresponding to the relationship with a higher degree of credibility are used as an example for detailed description. For example, the subordinate relationship between acute tonsil inflammation and acute upper respiratory tract inflammation can be converted into a vector ("acute tonsil inflammation", "subordinate relationship", "acute upper respiratory tract inflammation", 1), that is, the degree of credibility of inferring that the patient's physiological characteristics belong to "acute tonsil inflammation" is 1 based on the fact that the patient's physiological characteristics belong to "acute upper respiratory tract inflammation". At the same time, the subordinate relationship between acute tonsil inflammation and acute upper respiratory tract inflammation can also be described by the vector ("acute upper respiratory tract inflammation", "subordinate relationship", "acute tonsil inflammation", first weight coefficient). Among them, the value of the first weight coefficient is less than 1. That is, the degree of credibility of inferring that the patient's physiological characteristics belong to "acute upper respiratory tract inflammation" is the first weight coefficient with a value less than 1.

[0061] It should be noted that, using the relationship vector containing the weight coefficient as one of the inputs of the hospital infection prediction model, in addition to enriching the characteristics of the relationship between entities and improving the accuracy of the hospital infection prediction model, can also be used to identify the "false" relationship between entities extracted from the hospital infection diagnosis standard document and the historical patient data set, that is, the relationship in which the weight coefficient is too small. The output result of the hospital infection prediction model trained based on the relationship vector containing the weight coefficient is further reduced by the influence of errors or biases in the historical patient data set, thereby improving the recognition accuracy.

[0062] It should be noted that the weight coefficient can be preset according to the relationship between the two entities, and can also be obtained according to a cross neural network model, a ResourceRank algorithm, a PTCA model, a grid search algorithm, and the like.

[0063] It should be noted that when the relationship type of the relationship is the subordinate relationship, the weight coefficient of the corresponding relationship is obtained according to the positive example proportion. Among them, the positive example proportion is obtained according to the proportion of historical patients belonging to the conclusion entity among the historical patients belonging to the premise entity, and the positive example proportion is recorded as p (B|A). Among them, A represents the event that the patient's physiological characteristics belong to the premise entity, and B represents the event that the patient's physiological characteristics belong to the conclusion entity. The value of the weight coefficient of the corresponding relationship can be equal to the positive example proportion, or it can be the result of further processing of the positive example proportion, for example, the value of the weight coefficient is equal to the product of the preset ratio and the positive example proportion, or the value of the weight coefficient is equal to the sum of the preset value and the positive example proportion. The weight coefficient can also be other feasible specific value methods.

[0064] It should be noted that when the relationship type of the relationship is the association relationship, the weight coefficient of the corresponding relationship is obtained according to the positive example ratio and the negative example ratio. Among them, the negative example ratio is obtained according to the ratio of historical patients belonging to the conclusion entity among the historical patients who do not belong to the premise ratio, which is recorded as .in, Indicates that the patient's physiological characteristics do not belong to the event of the premise entity. The weight coefficient corresponding to the relationship can be expressed as a vector (positive example ratio, negative example ratio), or can be obtained by calculating the linear weighted result of the positive example ratio and the negative example ratio, or in other feasible ways.

[0065] It should be noted that the weight coefficient is calculated according to the positive example ratio, and the weight coefficient can be used not only to evaluate the credibility of the knowledge, but also to evaluate the causal relationship between the premise entity and the conclusion entity. Under the same conditions, the larger the value of the positive example ratio, the greater the probability that the patient's physiological characteristics belong to the conclusion entity when the patient's physiological characteristics belong to the premise entity; the smaller the value of the positive example ratio, the smaller the probability that the patient's physiological characteristics belong to the conclusion entity when the patient's physiological characteristics belong to the premise entity. For example, fever is not only one of the clinical diagnostic criteria for upper respiratory tract infection, but also one of the clinical diagnostic criteria for lower respiratory tract infection. In the absence of other symptoms, examination results and test results, if the value of p (meets the clinical diagnostic criteria for upper respiratory tract infection | fever) is greater than the value of p (meets the clinical diagnostic criteria for lower respiratory tract infection | fever), when the patient has "fever", it should be considered that the patient has a greater probability of meeting the clinical diagnostic criteria for upper respiratory tract infection. In particular, when the value of the positive example ratio is 1, it means that when the patient's physiological characteristics belong to the premise entity, the patient's physiological characteristics must belong to the conclusion entity. For example, the value of p (meets the etiological diagnostic criteria for vascular-related infection | Simple catheter culture isolates significant pathogenic microorganisms corresponding to vascular-related infection) is 1, indicating that when the patient's test result is that the catheter tip culture isolates significant pathogenic microorganisms corresponding to vascular-related infection, it means that the patient meets the etiological diagnostic criteria for vascular-related infection. Further, the value of p (vascular-related infection exists | meets the etiological diagnostic criteria for vascular-related infection) is 1, indicating that when the patient's physiological characteristics meet the etiological diagnostic criteria for vascular-related infection, it can be concluded that the patient has vascular-related infection. The above two positive example ratios related to vascular-related infection indicate that when the patient's physiological characteristics meet the criteria for the etiology of vascular-related infection, the significant pathogenic microorganisms corresponding to vascular-related infection isolated by catheter tip culture belong to the symptom markers of vascular-related infection.

[0066] It should be noted that in actual application scenarios, when the relationship type of the relationship is the association relationship, the relationship between entities can be a positive correlation or a negative correlation. For example, in the hospital infection diagnosis standard document, acute diarrhea is one of the clinical diagnostic criteria for infectious diarrhea, that is, when a patient has acute diarrhea, the patient has a probability of having infectious diarrhea, so the entity "acute diarrhea" and the entity "clinical diagnostic criteria for infectious diarrhea" are positively correlated; when a patient meets the diagnostic criteria for infectious diarrhea, it should be excluded that the patient has diarrhea caused by acute onset of chronic diarrhea or non-infectious factors such as diagnosis and treatment reasons, underlying diseases, psychological stress, etc., that is, when the patient's physiological characteristics belong to the entity "acute onset of chronic diarrhea", the patient should not be diagnosed as "infectious diarrhea", and only when the patient's physiological characteristics do not belong to the entity "acute onset of chronic diarrhea" can the patient be diagnosed as "infectious diarrhea", so the entity "acute onset of chronic diarrhea" and the entity "infectious diarrhea" are negatively correlated. At the same time, the weight coefficient calculated according to the positive example ratio and the negative example ratio can be used to evaluate the correlation between two entities with a positive correlation, and can also be used to evaluate the correlation between two entities with a negative correlation.

[0067] In this embodiment, the weight coefficient ω is calculated as follows: ; ; ; ; Wherein, p represents the calculation proportion; B represents the conclusion entity; A represents the premise entity; represents the complement of the premise entity; that is, it is not the premise entity; n(A) represents the number of historical patients whose physiological characteristics belong to the conclusion entity, represents the number of historical patients that do not belong to the conclusion entity, N represents the total number of historical patients, n(AB) represents the number of historical patients that belong to both the premise entity and the conclusion entity, Represents the number of historical patients who do not belong to the antecedent entity but belong to the conclusion entity.

[0068] It should be noted that, for the subordinate relationship, when the value of the weight coefficient is larger, when the patient's physiological characteristics belong to the premise entity, the probability that the patient's physiological characteristics belong to the conclusion entity is greater without other additional evidence. In particular, when the value of the weight coefficient is 1, the premise entity is included in the conclusion entity, and when the patient's physiological characteristics belong to the premise entity, the patient's physiological characteristics must belong to the conclusion entity.

[0069] It should be noted that, for the association relationship, when the value of the positive example ratio is closer to the value of the negative example ratio, it means that whether the patient's physiological characteristics belong to the premise entity has a smaller influence on whether the patient's physiological characteristics belong to the conclusion entity, that is, the correlation between the premise entity and the conclusion entity is smaller. In particular, when the value of the positive example ratio is equal to the value of the negative example ratio, it means that whether the patient's physiological characteristics belong to the premise entity has no effect on whether the patient belongs to the conclusion entity, and the correlation between the premise entity and the conclusion entity is the smallest. When the difference between the value of the positive example ratio and the value of the negative example ratio is larger, it means that whether the patient's physiological characteristics belong to the premise entity has a greater influence on whether the patient's physiological characteristics belong to the conclusion entity, that is, the correlation between the premise entity and the conclusion entity is larger. In particular, for the association relationship in which the value of the positive example ratio is 1 and the value of the negative example ratio is 0, when the patient's physiological characteristics belong to the premise entity, the patient's physiological characteristics must also belong to the conclusion entity, and when the patient's physiological characteristics do not belong to the premise entity, the patient's physiological characteristics must also not belong to the conclusion entity, that is, the premise entity and the conclusion entity are positively correlated, and the correlation is the largest. For example, a disease with a unique marker, etc. Similarly, for the relationship in which the value of the positive example ratio is 0 and the value of the negative example ratio is 1, the relationship between the corresponding premise entity and the corresponding conclusion entity is a negative correlation, and the correlation is the largest. The weight coefficient is calculated by the difference between the positive example ratio and the negative example ratio, so that the credibility of knowledge is evaluated according to the degree of correlation between the two entities.

[0070] In this embodiment, refer to the attached Figure 4 As shown, step S7 also includes the following sub-steps: S70. Initialize the target subgraph to an empty graph, and initialize the candidate relationship set, target entity set, and target relationship set to empty sets respectively.

[0071] S71. Match entities in the hospital infection knowledge graph according to the target physiological characteristics, and use the successfully matched entities as target entities. Identify entities from the target physiological characteristics using named entity recognition (NER) technology. Match the recognition results with entities in the hospital infection diagnosis knowledge graph, and use the successfully matched recognition results as target entities in the target entity set.

[0072] S72, determine whether the target entity set is an empty set. If the target entity set is an empty set, the search is terminated. If the target entity set is not an empty set, each target entity in the target entity set is added to the target sub-graph, and continue to execute step S73.

[0073] S73, updating the target subgraph according to the relations in the candidate relation set. If the number of relations in the candidate relation set is 0, directly execute step S74, if the number of relations in the candidate relation set is not 0, add the relations in the candidate relation set to the target subgraph.

[0074] S74, querying the hospital infection knowledge graph for all relations in which the premise entity is the target entity, and updating the candidate relation set according to the query result. The updated candidate relation set is the relation obtained by the query.

[0075] S75, according to each candidate relationship and the target sub-graph, the candidate relationship that successfully matches the target sub-graph is removed. If and only if there is a relationship in the target sub-graph that is the same as the candidate relationship, the corresponding candidate relationship is considered to be successfully matched to avoid repeated retrieval and computational redundancy. If all the candidate relationships are successfully matched with the target sub-graph, the search is terminated. If there is a candidate relationship that fails to match the target sub-graph, continue to step S76.

[0076] S76. The conclusion entities corresponding to the remaining candidate relations are taken as candidate entities. All the remaining candidate relations are divided into several sets according to the conclusion entities, and the target relation set is updated according to the division results. The conclusion entities of all relations in the same target relation set are the same. Each target relation set is divided into several sets according to the relation type to obtain target relation subsets. The conclusion entities of all relations in the same target relation subset are the same, and the relation types are also the same.

[0077] S77, obtaining the screening weight corresponding to each target relationship subset. i The screening weight α i The calculation methods include: ; ; ; Among them, max represents the function of taking the maximum value; ω i，j Represents the target relation subset R i The weight coefficient of the jth relation in; m represents the target relation subset R i The total number of relationships in C i，j Represents the target relation subset R i The corresponding first value; I (preset condition) represents an indicative function. When the preset condition is met, the value of the indicative function is 1, and when the preset condition is not met, the value of the indicative function is 0; A i，j Represents the target relation subset Ri The premise entity of the jth relationship in B i，j Represents the target relation subset R i The conclusion entity of the jth relation in A i，j Represents the target relation subset R i The complement of the premise entity of the j-th relation in .

[0078] S78, obtaining the screening threshold corresponding to each of the target relationship subsets. Compare the screening weight corresponding to each of the target relationship subsets with the corresponding screening threshold. When the screening weight is less than the screening threshold, the screening result of the corresponding target relationship subset is set to fail; when the screening weight is not less than the screening threshold, the screening result of the corresponding target relationship subset is set to pass.

[0079] It should be noted that the screening threshold can be preset according to the relationship type, or other feasible methods. The screening thresholds corresponding to different target relationship subsets can be the same or different. The larger the value of the screening threshold, the smaller the target sub-graph retrieved from the hospital infection knowledge graph according to the target physiological characteristics, the fewer target relationship vectors generated according to the target sub-graph, and the less calculation amount of the hospital infection prediction model in the prediction process; the closer the value of the screening threshold is to 0, the larger the sub-graph retrieved from the hospital infection knowledge graph according to the target physiological characteristics, the more target relationship vectors generated according to the target sub-graph, and the greater the calculation amount of the hospital infection prediction model in the prediction process.

[0080] In this embodiment, the target relation subset R i The screening threshold β i The calculation methods include: .

[0081] In this embodiment, the target relationship subset with the screening result of passing and the relationship type of subordinate relationship is the relationship in which the premise entity is included in the conclusion entity, that is, the premise entity is a subset of the conclusion entity. The target relationship subset with the screening result of passing and the relationship type of association relationship is the relationship in which the patient's physiological characteristics belong to the conclusion entity and all necessary premise entities are met at the same time. For example, when the hospital infection diagnosis standard document is clear, the clinical diagnosis standard of infectious diarrhea is considered to meet the clinical diagnosis standard if it meets one of the following three items: 1. Acute diarrhea, routine microscopic examination of feces white blood cells is not less than 10 / high-power field of view; 2. Acute diarrhea, or fever, nausea, vomiting, abdominal pain, etc.; 3. Acute diarrhea more than 3 times a day, for 2 consecutive days, or more than 5 times of watery diarrhea in 1 day. From the above, it can be seen that acute diarrhea is a necessary condition for meeting the clinical diagnosis standard of infectious diarrhea. In other words, if and only if the patient has acute diarrhea, it is possible to meet the clinical diagnosis standard of infectious diarrhea. When the patient does not have acute diarrhea, the patient can be preliminarily excluded from meeting the clinical diagnosis standard of infectious diarrhea. In addition, since the clinical diagnostic criteria for acute pelvic inflammatory disease include diarrhea (acute diarrhea is a type of diarrhea), when the patient's physiological characteristics belong to acute diarrhea, the patient may have acute pelvic inflammatory disease in addition to infectious diarrhea. When the patient also has the physiological characteristic of no less than 10 white blood cells per high-power field of view in routine microscopic examination of stool, and there are no other necessary clinical diagnostic criteria for acute pelvic inflammatory disease, the relationship ("diarrhea", "association relationship", "acute pelvic inflammatory disease", the second weight coefficient) should not be included in the retrieved target sub-graph. This method achieves targeted reduction of the number of relationships in the retrieved target sub-graph, reduces the computational complexity of the hospital infection prediction model, and improves the prediction speed.

[0082] S79. When all the screening results corresponding to the target relationship set are failed, remove all the relationships in the target relationship set whose screening results are failed from the candidate relationship set, remove all the candidate entities corresponding to the target relationship set whose screening results are failed from the candidate entity set, take the remaining candidate entities as the new target entities, and return to step S72 to continue execution.

[0083] In this embodiment, the method further includes step S8, obtaining the actual infection outcome of the target patient, and updating the weight coefficient in the hospital infection knowledge graph according to the actual infection outcome to complete the update of the hospital infection knowledge graph.

[0084] According to a second aspect of the present invention, the present invention claims a large model hospital infection determination system based on an intelligent agent, referring to the attached Figure 5 As shown, it includes: a knowledge graph construction module, a physiological feature acquisition module, a knowledge graph retrieval module, a relationship vector generation module and an infection outcome prediction module.

[0085] A physiological characteristic acquisition module, wherein the physiological characteristic acquisition module is used to acquire a target physiological characteristic of a target patient; the target physiological characteristic includes at least one of a description of the course of disease, an examination result, and a test result of the target patient; A knowledge graph retrieval module, the knowledge graph retrieval module is used to retrieve the hospital infection knowledge graph according to the target physiological characteristics to obtain the corresponding target sub-graph; wherein the hospital infection knowledge graph is constructed according to the hospital infection diagnosis standard document; A relationship vector generation module, wherein the relationship vector generation module is used to generate a relationship vector set according to the target sub-graph to obtain a target relationship vector set; An infection outcome prediction module is used to input the target physiological characteristics and the target relationship vector set into a pre-trained hospital infection prediction model to obtain a target infection outcome.

[0086] In a feasible implementation, in the knowledge graph construction module, the hospital infection knowledge graph includes entities and relationships; the entity types of the entities include symptoms, test results, examination results and diagnosis results of each infection site; the relationships include the relationship between different symptoms, the relationship between different test results, the relationship between different examination results, the relationship between symptoms and test results, the relationship between symptoms and examination results, the relationship between test results and examination results, the relationship between symptoms and diagnosis results, the relationship between examination results and diagnosis results, and the relationship between test results and diagnosis results.

[0087] In a feasible embodiment, in the knowledge graph construction module, the entity type also includes the diagnostic criteria for each infection site; the relationship between the symptoms and the diagnostic results is obtained according to the relationship between the corresponding symptoms and the diagnostic criteria and the relationship between the diagnostic criteria and the corresponding diagnostic results; the relationship between the examination results and the diagnostic results is obtained according to the relationship between the corresponding examination results and the diagnostic criteria and the relationship between the diagnostic criteria and the corresponding diagnostic results; the relationship between the test results and the diagnostic results is obtained according to the relationship between the corresponding test results and the diagnostic criteria and the relationship between the diagnostic criteria and the corresponding diagnostic results.

[0088] In a feasible implementation, the relationship vector set includes a premise entity, a conclusion entity and a weight coefficient, and the weight coefficient is used to evaluate the credibility of the patient's physiological characteristics belonging to the conclusion entity based on the patient's physiological characteristics belonging to the premise entity.

[0089] In a feasible implementation, the weight coefficient is obtained according to the proportion of positive examples; wherein, the proportion of positive examples is obtained according to the proportion of historical patients belonging to the conclusion entity among the historical patients belonging to the premise entity.

[0090] In a feasible implementation, the relationship type of the relationship includes a subordinate relationship and an associated relationship. When the relationship type of the relationship is the subordinate relationship, the weight coefficient of the corresponding relationship is obtained according to the positive example proportion. When the relationship type of the relationship is the associated relationship, the weight coefficient of the corresponding relationship is obtained according to the positive example proportion and the negative example proportion, wherein the negative example proportion is obtained according to the proportion of historical patients belonging to the conclusion entity among the historical patients who do not belong to the premise proportion.

[0091] In a feasible implementation manner, when the relationship type is the association relationship, the weight coefficient of the corresponding relationship is obtained according to the difference between the positive example ratio and the negative example ratio.

[0092] In a feasible implementation, when the relationship type is the subordinate relationship, the value of the weight coefficient of the corresponding relationship is equal to the positive example ratio, and when the relationship type is the association relationship, the value of the weight coefficient of the corresponding relationship is equal to the absolute value of the difference between the positive example ratio and the negative example ratio, and the knowledge graph retrieval module further includes: Matching entities in the hospital infection knowledge graph according to the target physiological characteristics, and taking the successfully matched entities as target entities; Determine whether the number of the target entity is 0, if so, terminate the search, if not, update the target subgraph; query all the relationships in the hospital infection knowledge graph where the premise entity is the target entity, and obtain candidate relationships; Matching each of the candidate relations with the target subgraph, and removing the candidate relations that successfully match the target subgraph; The conclusion entities corresponding to the remaining candidate relations are taken as candidate entities, and all the candidate relations are divided into a plurality of target relation sets according to the conclusion entities; each target relation set is divided into a plurality of target relation subsets according to the relation type; Obtaining a screening weight corresponding to each of the target relationship subsets; if the relationship type corresponding to the target relationship subset is a subordinate relationship, taking the maximum value of the weight coefficients in the corresponding target relationship subset as the weight coefficient; if the relationship type corresponding to the target relationship subset is an association relationship, counting the number of relationships in the target relationship subset whose positive example ratio is 0 or whose negative example ratio is 0, obtaining a first value, and calculating the sum of all the weight coefficients in the corresponding target relationship subset and the first value to obtain the screening weight; Obtaining a screening threshold corresponding to each of the target relationship subsets, where the value of the screening threshold is greater than 0; comparing the screening weight corresponding to each of the target relationship subsets with the corresponding screening threshold, and when the screening weight is less than the screening threshold, setting the screening result of the corresponding target relationship subset to fail; When all the screening results corresponding to the target relationship set are failed, the corresponding relationship is removed from the candidate relationship, the corresponding candidate entity is removed from the target entity, the remaining candidate entity is used as the new target entity, and the step of determining whether the number of target entities is 0 is returned to continue execution.

[0093] In a feasible implementation, the system further includes a knowledge graph updating module, which is used to obtain the actual infection outcome of the target patient and update the weight coefficient in the hospital infection knowledge graph according to the actual infection outcome.

[0094] See attached Figure 6 As shown, an embodiment of the present application provides an electronic device, including: a processor and a memory, the processor and the memory are interconnected and communicate with each other through a communication bus and / or other forms of connection mechanisms (not shown), the memory stores a computer program executable by the processor, and when the computing device is running, the processor executes the computer program to execute the system in any optional implementation of the above embodiments.

[0095] The embodiment of the present application provides a storage medium, and when the computer program is executed by the processor, the system in any optional implementation of the above embodiment is executed. The 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 (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read-Only Memory, referred to as EPROM), programmable read-only memory (Programmable Read-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, disk or optical disk.

[0096] In the embodiments provided in the present application, it should be understood that the disclosed system can be implemented in other ways. The system embodiments described above are merely schematic. For example, the division of the modules is only a logical function division, and can be implemented in another way. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some communication interface, system or unit, which can be electrical, mechanical or other forms.

[0097] In addition, the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0098] Furthermore, the functional modules in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0099] Flowcharts are used herein to illustrate the steps of the method of the embodiments of the present disclosure. It should be understood that the preceding or following steps are not necessarily performed precisely in order. On the contrary, various steps may be evaluated in reverse order or simultaneously. At the same time, other operations may also be added to these processes.

[0100] Unless otherwise defined, all terms used herein have the same meaning as commonly understood by those of ordinary skill in the art to which the present disclosure belongs. It should also be understood that terms such as those defined in common dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and should not be interpreted in an idealized or highly formal sense, unless explicitly defined as such herein.

[0101] The above is a detailed introduction to the provided agent-based large-model hospital infection determination method and system. This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only an embodiment of this application, which is only used to help understand the agent-based large-model hospital infection determination method and system of this application, and is not used to limit the scope of protection of this application. At the same time, for those skilled in the art, this application can have various changes and variations. Any modifications and equivalent substitutions made within the spirit and principles of this application should be included in the scope of protection of this application.

Claims

1. A large-model hospital infection determination method based on an intelligent agent, characterized in that: include: Acquire target physiological characteristics of target patients; The target physiological characteristic includes at least one of a description of the course of disease, a test result, and a test result of the target patient; Retrieving a hospital infection knowledge graph according to the target physiological characteristics to obtain a corresponding target sub-graph; wherein the hospital infection knowledge graph is constructed according to a hospital infection diagnosis standard document; Generate a relationship vector set according to the target sub-graph to obtain a target relationship vector set; The target physiological characteristics and the target relationship vector set are input into a pre-trained hospital infection prediction model to obtain a target infection outcome.

2. The agent-based large-model hospital infection determination method according to claim 1, characterized in that: The hospital infection knowledge graph includes entities and relationships; the entity types of the entities include symptoms, test results, examination results and diagnosis results of each infection site; the relationships include the relationship between different symptoms, the relationship between different test results, the relationship between different examination results, the relationship between symptoms and test results, the relationship between symptoms and examination results, the relationship between test results and examination results, the relationship between symptoms and diagnosis results, the relationship between examination results and diagnosis results, and the relationship between test results and diagnosis results.

3. The agent-based large-model hospital infection determination method according to claim 2, characterized in that: The entity type also includes the diagnostic criteria for each infection site; the relationship between the symptoms and the diagnostic results is obtained according to the relationship between the corresponding symptoms and the diagnostic criteria and the relationship between the diagnostic criteria and the corresponding diagnostic results; the relationship between the examination results and the diagnostic results is obtained according to the relationship between the corresponding examination results and the diagnostic criteria and the relationship between the diagnostic criteria and the corresponding diagnostic results; The relationship between the test result and the diagnosis result is obtained according to the relationship between the corresponding test result and the diagnosis standard and the relationship between the diagnosis standard and the corresponding diagnosis result.

4. The agent-based large-model hospital infection determination method according to claim 3, characterized in that: The relationship vector set includes a premise entity, a conclusion entity and a weight coefficient, and the weight coefficient is used to evaluate the credibility of the patient's physiological characteristics belonging to the conclusion entity based on the patient's physiological characteristics belonging to the premise entity.

5. The agent-based large-model hospital infection determination method according to claim 4, characterized in that: The weight coefficient is obtained according to the proportion of positive examples; wherein, the proportion of positive examples is obtained according to the proportion of historical patients belonging to the conclusion entity among historical patients belonging to the premise entity.

6. The agent-based large-model hospital infection determination method according to claim 5, characterized in that: The relationship types of the relationship include subordinate relationships and associated relationships. When the relationship type of the relationship is the subordinate relationship, the weight coefficient of the corresponding relationship is obtained according to the positive example proportion. When the relationship type of the relationship is the associated relationship, the weight coefficient of the corresponding relationship is obtained according to the positive example proportion and the negative example proportion, wherein the negative example proportion is obtained according to the proportion of historical patients belonging to the conclusion entity among the historical patients who do not belong to the premise proportion.

7. The agent-based large-model hospital infection determination method according to claim 6, characterized in that: When the relationship type is the association relationship, the weight coefficient of the corresponding relationship is obtained according to the difference between the positive example ratio and the negative example ratio.

8. The agent-based large-model hospital infection determination method according to claim 7, characterized in that: When the relationship type is the subordinate relationship, the value of the weight coefficient of the corresponding relationship is equal to the positive example ratio; when the relationship type is the associated relationship, the value of the weight coefficient of the corresponding relationship is equal to the absolute value of the difference between the positive example ratio and the negative example ratio. In the retrieving the hospital infection knowledge graph according to the target physiological characteristics, it also includes: Matching entities in the hospital infection knowledge graph according to the target physiological characteristics, and taking the successfully matched entities as target entities; Determine whether the number of the target entity is 0, if so, terminate the search, if not, update the target subgraph; query all the relationships in the hospital infection knowledge graph where the premise entity is the target entity, and obtain candidate relationships; Matching each of the candidate relations with the target subgraph, and removing the candidate relations that successfully match the target subgraph; The conclusion entities corresponding to the remaining candidate relations are taken as candidate entities, and all the candidate relations are divided into a plurality of target relation sets according to the conclusion entities; each target relation set is divided into a plurality of target relation subsets according to the relation type; Obtaining a screening weight corresponding to each of the target relationship subsets; if the relationship type corresponding to the target relationship subset is a subordinate relationship, taking the maximum value of the weight coefficients in the corresponding target relationship subset as the weight coefficient; if the relationship type corresponding to the target relationship subset is an association relationship, counting the number of relationships in the target relationship subset whose positive example ratio is 0 or whose negative example ratio is 0, obtaining a first value, and calculating the sum of all the weight coefficients in the corresponding target relationship subset and the first value to obtain the screening weight; Obtaining a screening threshold corresponding to each of the target relationship subsets, where the value of the screening threshold is greater than 0; comparing the screening weight corresponding to each of the target relationship subsets with the corresponding screening threshold, and when the screening weight is less than the screening threshold, setting the screening result of the corresponding target relationship subset to fail; When all the screening results corresponding to the target relationship set are failed, the corresponding relationship is removed from the candidate relationship, the corresponding candidate entity is removed from the target entity, the remaining candidate entity is used as the new target entity, and the step of determining whether the number of target entities is 0 is returned to continue execution.

9. The agent-based large-model hospital infection determination method according to any one of claims 5 to 8, characterized in that: After inputting the target physiological characteristics and the target relationship vector set into a pre-trained hospital infection prediction model, the method further includes obtaining the actual infection outcome of the target patient, and updating the weight coefficient in the hospital infection knowledge graph according to the actual infection outcome.

10. The large-model hospital infection determination system based on intelligent agent is characterized by: It includes a knowledge graph construction module, a physiological feature acquisition module, a knowledge graph retrieval module, a relationship vector generation module and an infection outcome prediction module; The knowledge graph construction module is used to construct a hospital infection knowledge graph based on the hospital infection diagnosis standard document; Wherein, the physiological characteristic acquisition module is used to acquire the target physiological characteristic of the target patient; the target physiological characteristic includes at least one of the course of disease description, examination result and test result of the target patient; The knowledge graph retrieval module is used to retrieve the hospital infection knowledge graph according to the target physiological characteristics to obtain the corresponding target sub-graph; The relationship vector generation module is used to generate a relationship vector set according to the target sub-graph to obtain a target relationship vector set; The infection outcome prediction module is used to input the target physiological characteristics and the target relationship vector set into a pre-trained hospital infection prediction model to obtain the target infection outcome.

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