Regional virus infection risk assessment method, device, equipment, medium and product

Abstract

The invention relates to a regional virus infection risk assessment method and device, equipment, a medium and a product. The method comprises the following steps: acquiring infection associated data generated by a target area in a preset historical time period; according to the infection associated data, constructing an infection knowledge graph corresponding to the target area; entities in the infection knowledge map comprise at least two of patients, pathogens, medical staff, wards, medical instruments and symptom eyes; and for any target entity, evaluating the virus infection risk of the target area according to the relationship between the target entity and the associated entity corresponding to the target entity. By adopting the method, the regional virus infection risk can be evaluated, so that prevention can be carried out in time under the condition of high infection risk, and large-range infection in the region is avoided.

Description

Technical Field

[0001] This application relates to the field of data analysis technology, and particularly to a method, device, equipment, medium, and product for evaluating the risk of regional virus infection. Background Art

[0002] The prevention and control of regional infections (such as hospital infections, also known as nosocomial infections) play a crucial role in medical quality management, and its importance is self-evident.

[0003] However, for a long time, regional infection detection has mainly relied on traditional means such as manual reporting and post-event data analysis. This mode has many limitations. Problems such as data lag, lack of accuracy, and incomplete information coverage have seriously hindered the timeliness and accuracy of regional infection prevention and control work, making it difficult to identify and control potential risks in a timely and effective manner, and urgent solutions are needed. Summary of the Invention

[0004] Based on this, in view of the above technical problems, it is necessary to provide a method, device, equipment, medium, and product for evaluating the risk of regional virus infection, which can evaluate the risk of regional virus infection, so as to prevent in a timely manner when the infection risk is high and avoid large-scale infections within the region.

[0005] In the first aspect, this application provides a method for evaluating the risk of regional virus infection, including:

[0006] Obtain the infection-related data generated in the target region during a preset historical period;

[0007] Construct an infection knowledge graph corresponding to the target region according to the infection-related data; the entities in the infection knowledge graph include at least two of patients, pathogens, medical staff, wards, medical devices, and symptoms;

[0008] For any target entity, evaluate the risk of virus infection in the target region according to the relationship between the target entity and the associated entities corresponding to the target entity.

[0009] In one of the embodiments, evaluating the risk of virus infection in the target region according to the relationship between the target entity and the associated entities corresponding to the target entity includes:

[0010] Determine the infection risk value corresponding to the target entity according to the number of associated entities corresponding to the target entity, the number of other associated entities corresponding to the associated entities, and the relationship between the associated entities and the other associated entities;

[0011] Evaluate the risk of virus infection in the target region according to the infection risk value;

[0012] Wherein, the other associated entities are other entities that have a relationship with the associated entity except the target entity.

[0013] In one embodiment, the method further includes:

[0014] If the infection risk value indicates that there is an infection risk for the target entity, select the entity to be intervened corresponding to the target entity from the infection knowledge graph.

[0015] In one embodiment, selecting the entity to be intervened corresponding to the target entity from the infection knowledge graph includes:

[0016] Select the entity to be intervened corresponding to the target entity from the infection knowledge graph according to the entity type of the target entity.

[0017] In one embodiment, selecting the entity to be intervened corresponding to the target entity from the infection knowledge graph according to the entity type of the target entity includes:

[0018] When the target entity is a type of entity, other entities in the infection knowledge graph that have an association relationship with the target entity are used as the entities to be intervened that need to take prevention and control measures; the type of entity includes medical staff, wards, and medical devices.

[0019] In one embodiment, selecting the entity to be intervened corresponding to the target entity from the infection knowledge graph according to the entity type of the target entity includes:

[0020] When the target entity is a symptom, the associated entity corresponding to the symptom is used as the entity to be intervened that needs to be subjected to infection detection.

[0021] In a second aspect, the present application further provides a regional virus infection risk assessment device, including:

[0022] An acquisition module, configured to acquire infection-related data generated in a preset historical period for a target area;

[0023] A construction module, configured to construct an infection knowledge graph corresponding to the target area according to the infection-related data; the entities in the infection knowledge graph include at least two of patients, pathogens, medical staff, wards, medical devices, and symptoms;

[0024] An evaluation module, configured to evaluate the virus infection risk of the target area for any target entity according to the relationship between the target entity and the associated entity corresponding to the target entity.

[0025] In a third aspect, the present application further provides a computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0026] Acquire infection-related data generated in a preset historical period for a target area;

[0027] Construct an infection knowledge graph corresponding to the target area based on the infection-related data; the entities in the infection knowledge graph include at least two of patients, pathogens, medical staff, wards, medical devices, and symptoms.

[0028] For any target entity, evaluate the risk of virus infection in the target area according to the relationship between the target entity and the associated entities corresponding to the target entity.

[0029] In a fourth aspect, the present application also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0030] Obtain the infection-related data generated in the target area during a preset historical period;

[0031] Construct an infection knowledge graph corresponding to the target area based on the infection-related data; the entities in the infection knowledge graph include at least two of patients, pathogens, medical staff, wards, medical devices, and symptoms.

[0032] For any target entity, evaluate the risk of virus infection in the target area according to the relationship between the target entity and the associated entities corresponding to the target entity.

[0033] In a fifth aspect, the present application also provides a computer program product, including a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0034] Obtain the infection-related data generated in the target area during a preset historical period;

[0035] Construct an infection knowledge graph corresponding to the target area based on the infection-related data; the entities in the infection knowledge graph include at least two of patients, pathogens, medical staff, wards, medical devices, and symptoms.

[0036] For any target entity, evaluate the risk of virus infection in the target area according to the relationship between the target entity and the associated entities corresponding to the target entity.

[0037] The above-mentioned method, device, equipment, medium and product for regional virus infection risk assessment obtain the infection-related data generated in a preset historical period for a target region; construct an infection knowledge graph corresponding to the target region according to the infection-related data; the entities in the infection knowledge graph include at least two of patients, pathogens, medical staff, wards, medical devices and symptoms; for any target entity, the virus infection risk of the target region is evaluated according to the relationship between the target entity and the associated entity corresponding to the target entity. It can achieve real-time, accurate monitoring and early warning of nosocomial infection risks. On the one hand, by identifying and marking the entities and relationships related to hospital infection risks, the transmission path and influence scope of risks in the medical network can be clearly seen. This helps to identify potential risk points and latent risks, such as specific patient groups, medical equipment or medical operations, etc.; on the other hand, using the data and relationships in the infection knowledge graph, the magnitude and impact degree of risks are quantified. For example, the infection probability of a certain type of patient can be calculated based on historical data, or the infection risk of a medical device can be evaluated according to its usage situation. By analyzing the relationships between entities, the relevance and mutual influence between risks can be evaluated, so as to more comprehensively understand the overall situation of hospital infection risks; on the one hand again, the infection knowledge graph can also be used to monitor the changes of hospital infection risks in real time. When the entities or relationships in the infection knowledge graph change, such as the infection situation of newly admitted patients, the disinfection status of medical equipment, etc., the system can automatically trigger the early warning mechanism. Remind relevant personnel to pay attention and take corresponding countermeasures, so as to effectively prevent and control the occurrence of hospital infections; on the other hand again, based on the risk assessment results of the infection knowledge graph, it can provide strong decision-making support for the hospital infection management department. By visually displaying the distribution and transmission of risks in the medical network, it helps decision-makers more intuitively understand the risk situation. Thus, more scientific and reasonable prevention and control strategies can be formulated to improve the efficiency and effect of hospital infection management. Brief Description of the Drawings

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required for the description of the embodiments of the present application or related technologies. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0039] Figure 1 It is a schematic flowchart of the method for regional virus infection risk assessment in an embodiment;

[0040] Figure 2 It is a schematic flowchart of the steps for virus infection risk assessment in an embodiment;

[0041] Figure 3Schematic flowchart of steps for selecting an entity to be intervened in an embodiment;

[0042] Figure 4 Schematic flowchart of a method for assessing the risk of regional virus infection in another embodiment;

[0043] Figure 5 Block diagram of the structure of a device for assessing the risk of regional virus infection in an embodiment;

[0044] Figure 6 Internal structure diagram of a computer device in an embodiment. Detailed implementation manners

[0045] In order to make the objectives, technical solutions and advantages of the present application clearer and more understandable, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0046] In one embodiment, as Figure 1 shown, a method for assessing the risk of regional virus infection is provided. In this embodiment, the method is exemplified by being applied to a terminal. It can be understood that the method can also be applied to a server, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0047] S110, obtaining infection-related data generated in a preset historical period in a target area.

[0048] Among them, the target area may be an area where there may be an infection risk. Exemplarily, the target area may be a hospital. In the following embodiments, the target area is taken as a hospital for illustration.

[0049] Among them, the preset historical period can be set based on user requirements. Exemplarily, it can be a preset duration before the current moment. Among them, the preset duration can be one month or half a year. The present application does not make any limitation thereto.

[0050] Among them, the infection-related data can be understood as data related to the historical infection records of the target area. Exemplarily, taking the target area as a hospital, the infection-related data may include basic patient information, diagnosis information, test results, medication records, surgical information, medical staff operation records, and ward environment data, etc.

[0051] Exemplarily, in this embodiment, various types of data related to hospital infections within a preset historical period can be collected from multiple data sources such as the Hospital Information System (HIS), Laboratory Information System (LLS), and Electronic Medical Record (EMR), and used as infection-related data.

[0052] S120. Construct an infection knowledge graph corresponding to the target area according to the infection-related data.

[0053] Among them, the entities in the infection knowledge graph include at least two of patients, pathogens, medical staff, wards, medical devices, and symptoms.

[0054] Exemplarily, in this embodiment, the collected infection-related data can be cleaned, sorted, and standardized, key entities and relationships can be extracted, and an infection knowledge graph can be constructed using natural language processing techniques and semantic annotation methods. Among them, the entities can include patients, pathogens, medical devices, wards, medical staff, etc., and the relationships include infection relationships, contact relationships, treatment relationships, disease symptoms, and related detection types.

[0055] Exemplarily, the construction of the infection knowledge graph can include the following steps: 1. Data collection and integration: Collect all infection-related data related to hospital infection risks, including historical infection events, patient information, medical staff information, medical operation records, environmental data, etc.; 2. Integrate the infection-related data and construct an infection knowledge graph, where the entities can include patients, medical staff, medical equipment, medical operations, etc., and the relationships may involve treatment relationships, contact relationships, environmental influencing factors, disease symptoms, and related detection types; 3. Define the description method of the target knowledge, such as using the Resource Description Framework (RDF) data model for description, that is, the triple pattern of entity, relationship, entity; 4. Perform relationship extraction to identify the relationships between entities in the text or other data, such as the treatment relationship between patients and medical staff, the usage relationship between patients and medical equipment, etc.

[0056] S130. For any target entity, evaluate the risk of viral infection in the target area according to the relationship between the target entity and the associated entity corresponding to the target entity.

[0057] Among them, the target entity can be a specified entity; for any target entity, its corresponding associated entity can be an entity that has a relationship with the target entity.

[0058] In an alternative implementation, the relationship between the target entity and the associated entity corresponding to the target entity can be input into a pre-trained risk assessment model to obtain an assessment result of the risk of virus infection in the target area.

[0059] In another alternative implementation, the relationship between the target entity and the associated entity corresponding to the target entity, the historical record data of the target entity, and the historical record data of the associated entity can be input into a pre-trained risk assessment model to obtain an infection risk value corresponding to the target entity; wherein, the risk assessment model is trained based on the relationships between sample entity groups and the sample record data of each sample entity in the sample entity groups.

[0060] Among them, the historical record data includes, but is not limited to, at least one of basic information, operation information, environmental information, infection information, etc. Taking the entity as a patient as an example, the corresponding basic information may include age, gender, underlying disease status, immune status, etc. These information may affect the risk of patient infection. For example, patients with older age and low immune status may be more prone to nosocomial infection; operation information may include surgeries, invasive operations (such as indwelling urinary catheters, using ventilators, etc.), antibacterial drug use conditions, etc. These operations may increase the risk of patient infection, especially when the operations are not standardized or the patient has certain susceptible factors; hospital environmental information may include ward cleanliness, air quality, disinfection effect of medical devices, etc. Infection information may include historical data of nosocomial infections, distribution and transmission of drug-resistant strains, etc. These information help to identify high-risk factors and potential risks of nosocomial infections.

[0061] Furthermore, the collected infection-related data can be updated in real time, and the new data can be incorporated into the infection knowledge graph. Through the reasoning and analysis functions of the infection knowledge graph, the changes of virus infection risk factors can be dynamically monitored, such as the development of the patient's condition, the transmission path of the pathogen, the use of medical devices, etc. For example: in the infection knowledge graph, there are two disease types, "symptom 1" and "symptom 2", related to fever and dry cough. "Symptom 1" requires test 1, and "symptom 2" requires test 2. If test 1 is negative, it can be inferred from the infection knowledge graph that test 2 is still needed.

[0062] Exemplarily, the update mechanism of the knowledge graph may include at least one of the following: 1. Dynamic update mechanism: Adopt a combination of real-time update, incremental update, and batch update to ensure that the knowledge graph of hospital infection can continuously reflect the latest hospital infection risk information; 2. Real-time update mechanism: Continuously update the infection knowledge graph by streaming data sources (such as real-time monitoring data, new admission patient information, etc.); 3. Incremental update mechanism: Update the infection knowledge graph by periodically obtaining update information of existing entities (such as changes in patient conditions, new medical operations, etc.); 4. Batch update mechanism: Update the infection knowledge graph by periodically processing large datasets (such as regular medical quality inspection data, environmental detection data, etc.).

[0063] It should be noted that during the update process, new knowledge can be integrated with the existing knowledge base to resolve conflicts caused by inconsistencies or contradictions. By means of entity disambiguation algorithms, relationship extraction algorithms, or pattern matching techniques, etc., ensure the effective integration of new knowledge and existing knowledge.

[0064] In the above-mentioned method for evaluating the risk of regional virus infection, infection-related data generated in a preset historical period for the target area is obtained; an infection knowledge graph corresponding to the target area is constructed based on the infection-related data; the entities in the infection knowledge graph include at least two of patients, pathogens, medical staff, wards, medical devices, and symptoms; for any target entity, the risk of virus infection in the target area is evaluated according to the relationship between the target entity and the associated entity corresponding to the target entity. It can realize real-time, accurate monitoring and early warning of nosocomial infection risks. On the one hand, by identifying and marking the entities and relationships related to hospital infection risks, the transmission path and scope of influence of risks in the medical network can be clearly seen. This helps to identify potential risk points and latent risks, such as specific patient groups, medical equipment, or medical operations, etc.; on the other hand, using the data and relationships in the infection knowledge graph, the magnitude and degree of influence of risks are quantified. For example, the infection probability of a certain type of patient can be calculated based on historical data, or the infection risk of medical equipment can be evaluated according to its usage situation. By analyzing the relationships between entities, the correlation and mutual influence between risks can be evaluated, so as to more comprehensively understand the overall situation of hospital infection risks; on the one hand again, the infection knowledge graph can also be used to monitor the changes in hospital infection risks in real time. When the entities or relationships in the infection knowledge graph change, such as the infection situation of newly admitted patients, the disinfection status of medical equipment, etc., the system can automatically trigger the early warning mechanism. Remind relevant personnel to pay attention and take corresponding countermeasures, so as to effectively prevent and control the occurrence of hospital infections; on the other hand again, based on the risk assessment results of the infection knowledge graph, it can provide strong decision-making support for the hospital infection management department. By visually displaying the distribution and spread of risks in the medical network, it helps decision-makers more intuitively understand the risk situation. Thus, more scientific and reasonable prevention and control strategies can be formulated to improve the efficiency and effect of hospital infection management.

[0065] Based on the technical solutions of the above embodiments, the present application also provides an alternative embodiment. In this alternative embodiment, the process of evaluating the risk of virus infection in the target area according to the relationship between the target entity and the associated entity corresponding to the target entity is refined.

[0066] See Figure 2 The virus infection risk assessment steps shown include:

[0067] S210, determine the infection risk value corresponding to the target entity according to the number of associated entities corresponding to the target entity, the number of other associated entities corresponding to the associated entities, and the relationship between the associated entities and the other associated entities.

[0068] Among them, the other associated entity is another entity that has a relationship with the associated entity except the target entity.

[0069] In an alternative embodiment, when the number of associated entities corresponding to the target entity exceeds the first preset threshold, the number of other associated entities corresponding to the associated entities may be obtained. And when this number exceeds the second preset threshold and the relationship between the associated entities and the other associated entities indicates an infection relationship, it is determined that the target entity has a relatively high risk of infection. Then, based on a pre-determined risk value determination formula, the above two numbers are processed to determine a specific infection risk value. Exemplarily, the risk value determination formula may simply be to determine the weighted sum value of the two numbers.

[0070] Among them, the first preset threshold and the second preset threshold can be determined based on manual experience, and no limitation is imposed on this.

[0071] S220. Evaluate the virus infection risk of the target area according to the infection risk value.

[0072] Exemplarily, the virus infection risk of the target area can be evaluated according to the magnitude relationship between the infection risk value and the preset risk threshold. Exemplarily, when the infection risk value exceeds the preset risk threshold, it is determined that the risk is relatively high; otherwise, it is determined that the risk is relatively low.

[0073] Among them, the preset risk threshold can be determined based on manual experience, and the preset risk thresholds corresponding to different target entities may be different, and this application does not impose any limitation on this.

[0074] Optionally, the determination of the threshold should not be solely based on manual experience. The following are some more accurate ways to determine the threshold and other ways to determine the risk value in addition to processing data based on a model:

[0075] 1. Frequency analysis based on historical data statistics: Collect and organize the data of each risk factor when nosocomial infections occurred historically, and calculate the occurrence frequency and distribution of each risk factor in nosocomial infection events. For example, count the number of days of antibacterial drug use by patients when nosocomial infections occurred in the past, and calculate statistical quantities such as the average value, median, and standard deviation. Based on these statistical quantities, combined with a certain safety margin, the threshold is determined.

[0076] 2. Correlation analysis: Analyze the correlation between risk factors and the occurrence of nosocomial infections. By calculating indicators such as correlation coefficients, determine which risk factors have a strong correlation with nosocomial infections. For risk factors with a strong correlation, determine the threshold according to the data change law in nosocomial infection events. For example, if it is found that the bacterial content in the ward air is highly positively correlated with the incidence of nosocomial infections, the threshold can be determined based on the critical value of the air bacterial content when nosocomial infections occur in historical data. 3. Unsupervised learning method based on machine learning algorithms: Use clustering algorithms, such as K-Means clustering, to perform clustering analysis on risk factor data. Divide the data into different clusters, observe the characteristics and distributions of each cluster, and use the boundaries or outliers between clusters as potential threshold reference points. For example, perform clustering on multiple risk factors such as the length of a patient's hospital stay and the number of underlying diseases. If it is found that nosocomial infections generally occur in patients in a certain cluster and there are obvious data differences from other clusters, the threshold for relevant risk factors can be determined accordingly. 4. Supervised learning method: Use classification algorithms, such as decision trees and random forests, with the occurrence of nosocomial infections as the label to train risk factor data. During the model training process, it will learn the influence degree and threshold boundaries of different risk factors on nosocomial infections. For example, a decision tree model may determine based on training data that when the patient's age is greater than a certain value and the types of antibacterial drugs used exceed a certain number, the probability of nosocomial infections increases significantly, and this age value and the number of drug types can be used as thresholds. 5. Delphi method of multi-expert consensus: Organize experts in multiple fields, such as infection control experts, clinicians, nurses, and hospital administrators, to conduct independent judgments and evaluations on risk factor thresholds through multiple rounds of questionnaires and feedback. After collecting the opinions of experts, conduct statistical analysis and synthesis to form a consensus threshold. For example, in the first round of investigation, experts each give their views on the concentration threshold of a certain pathogen in the ward environment. The organizer summarizes and feedbacks the statistical results to the experts, and the experts adjust their opinions again according to the overall situation. After several rounds, a relatively consistent threshold is reached.

[0077] Based on the technical solutions of the above embodiments, the present application also provides an alternative embodiment. In this alternative embodiment, a supplementary introduction is made to the situation where the infection risk value characterizes that the target entity has an infection risk.

[0078] See Figure 3 The steps for selecting the entity to be intervened as shown, include:

[0079] S310, if the infection risk value characterizes that the target entity has an infection risk, then select the entity to be intervened corresponding to the target entity from the infection knowledge graph.

[0080] Among them, the entity to be intervened can be understood as the entity that needs to be treated for infection prevention.

[0081] In an alternative embodiment, the intervention entities corresponding to the target entity can be selected from the infection knowledge graph according to the entity type of the target entity. Exemplarily, when the target entity is a type of entity, other entities in the infection knowledge graph that have an association relationship with the target entity are used as the intervention entities that need to take prevention and control measures; the type of entity includes medical staff, wards, and medical devices.

[0082] Exemplarily, when the infection risk value corresponding to a certain ward is relatively high, all entities related to the ward can be used as the intervention entities.

[0083] When the target entity is a symptom, the associated entity corresponding to the symptom is used as the intervention entity that needs to be tested for infection. Exemplarily, the item to be tested corresponding to symptom 1 is used as the item to be tested for the patient who has a relationship with symptom 1.

[0084] Based on the technical solutions of the above embodiments, the present application also provides an alternative embodiment. In this alternative embodiment, a detailed introduction to the regional virus infection risk assessment method provided by the present application is given.

[0085] See Figure 4 The regional virus infection risk assessment method shown in, includes:

[0086] S410, obtaining the infection-related data generated in the target area during a preset historical period;

[0087] S420, constructing an infection knowledge graph corresponding to the target area according to the infection-related data;

[0088] Among them, the entities in the infection knowledge graph include at least two of patients, pathogens, medical staff, wards, medical devices, and symptoms;

[0089] S430, determining the infection risk value corresponding to the target entity according to the number of associated entities corresponding to the target entity, the number of other associated entities corresponding to the associated entities, and the relationship between the associated entities and the other associated entities;

[0090] S440, if the infection risk value indicates that the target entity has an infection risk, then determine whether the target entity is a type of entity. If so, execute S450; if not, execute S460;

[0091] S450, if so, then use other entities in the infection knowledge graph that have an association relationship with the target entity as the intervention entities that need to take prevention and control measures;

[0092] Among them, the type of entity includes medical staff, wards, and medical devices;

[0093] S460. If not, in the case where the target entity is a symptom, the associated entity corresponding to the symptom is used as the entity to be intervened for which infection detection needs to be performed.

[0094] Among them, other associated entities are other entities that have a relationship with the associated entity except for the target entity.

[0095] The method provided by this application can comprehensively and accurately grasp the changes in hospital infection risks through steps such as data collection and integration, knowledge modeling and relationship extraction, dynamic update mechanism, risk entity and relationship identification, risk quantification and assessment, risk early warning and monitoring, and decision support, providing a scientific basis for hospital infection management. The following is a detailed description of the effects of changes in different factors:

[0096] First, the effect of changes in infection source management factors: 1. Changes in patient management: When patient management is strengthened, such as timely isolation of infectious disease patients and control of cross-infection between patients, the incidence of nosocomial infections can be significantly reduced. This change helps protect other patients from infection and reduces waste of medical resources at the same time. 2. Changes in medical staff management: When the awareness of medical staff in managing infection sources is improved, such as correctly wearing protective equipment and implementing hand hygiene norms, the risk of themselves becoming infection sources can be effectively reduced. This change helps ensure the occupational safety of medical staff and reduces nosocomial infection incidents caused by improper operations of medical staff.

[0097] Second, the effect of changes in medical operation specification factors: 1. Changes in operation procedures: When medical operation procedures are standardized, such as implementing strict disinfection procedures and following aseptic operation principles, the risk of nosocomial infections caused by improper operations can be significantly reduced. This change helps improve the safety of medical operations and ensure the treatment effect of patients. 2. When medical operations are effectively supervised, such as real-time monitoring of the operation process through a monitoring system and regular evaluation of the implementation of operation specifications, non-standard operations can be detected and corrected in a timely manner. This change helps improve the overall quality of medical operations and reduce the occurrence of nosocomial infection incidents.

[0098] Third, the effect of changes in environmental cleaning and disinfection factors: 1. Changes in cleaning frequency: When the cleaning frequency of the hospital environment increases, such as regularly performing deep cleaning on areas such as operating rooms, wards, and restrooms, the breeding and spread of pathogens in the environment can be effectively reduced. This change helps improve the overall hygiene of the hospital and reduce the infection risk of patients caused by environmental factors. 2. Changes in disinfection effect: When disinfection measures are effectively implemented, such as using appropriate disinfectants, ensuring disinfection time and concentration, etc., pathogens in the environment can be killed, reducing the chance of cross-infection. This change helps improve the hygiene quality of the hospital environment and ensure the safety of patients.

[0099] Furthermore, the effects of changes in equipment and instrument management factors are as follows: 1. When medical equipment and instruments are regularly cleaned and disinfected, the hygiene and safety of the equipment can be ensured, and nosocomial infection incidents caused by equipment contamination can be reduced. This change helps to improve the safety of using medical equipment and guarantee the treatment effect of patients. 2. When the maintenance and repair work of medical equipment is strengthened, problems existing in the equipment can be discovered and solved in a timely manner, and the nosocomial infection risk caused by equipment failure can be prevented. This change helps to improve the reliability and stability of medical equipment and ensure the smooth progress of medical work.

[0100] In addition, the effects of changes in patients' individual resistance factors are as follows: 1. Changes in nutritional management: When patients receive reasonable nutritional management, their nutritional status can be improved, their physical resistance can be enhanced, and the risk of infection can be reduced. This change helps to improve the overall health of patients and promote the rehabilitation process. 2. Changes in rehabilitation training and psychological counseling: When patients receive reasonable rehabilitation training and psychological counseling, their physical functions and psychological states can be improved, and their ability to cope with infection can be enhanced. This change helps to improve the rehabilitation effect and quality of life of patients.

[0101] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are shown in sequence according to the indications of the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same moment, but can be executed at different moments. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or steps or stages in other steps.

[0102] Based on the same inventive concept, the embodiments of the present application also provide a regional virus infection risk assessment device for implementing the above-mentioned regional virus infection risk assessment method. The solution provided by this device to solve the problem is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the following regional virus infection risk assessment device can refer to the limitations on the regional virus infection risk assessment method in the above text, and will not be repeated here.

[0103] In an exemplary embodiment, as Figure 5 shown, a regional virus infection risk assessment device is provided, including: an acquisition module 510, a construction module 520, and an evaluation module 530, where:

[0104] An acquisition module 510, configured to acquire infection-related data generated in a preset historical period in a target area;

[0105] A construction module 520, configured to construct an infection knowledge graph corresponding to the target area according to the infection-related data; entities in the infection knowledge graph include at least two of patients, pathogens, medical staff, wards, medical devices, and symptoms;

[0106] An evaluation module 530, configured to evaluate the virus infection risk of the target area for any target entity according to the relationship between the target entity and the associated entities corresponding to the target entity.

[0107] In one embodiment, the evaluation module 530 includes a determination unit, configured to determine an infection risk value corresponding to the target entity according to the number of associated entities corresponding to the target entity, the number of other associated entities corresponding to the associated entities, and the relationship between the associated entities and the other associated entities; an evaluation unit, configured to evaluate the virus infection risk of the target area according to the infection risk value; wherein, the other associated entities are other entities that have a relationship with the associated entity except the target entity.

[0108] In one embodiment, the regional virus infection risk assessment device further includes a determination module, configured to select an entity to be intervened corresponding to the target entity from the infection knowledge graph if the infection risk value indicates that the target entity has an infection risk.

[0109] In one embodiment, the determination module is specifically configured to select an entity to be intervened corresponding to the target entity from the infection knowledge graph according to the entity type of the target entity.

[0110] In one embodiment, the determination module is specifically configured to, when the target entity is a type of entity, use other entities in the infection knowledge graph that have an association relationship with the target entity as entities to be intervened that need to take prevention and control measures; the type of entity includes medical staff, wards, and medical devices.

[0111] In one embodiment, the determination module is specifically configured to, when the target entity is a symptom, use the associated entity corresponding to the symptom as an entity to be intervened that needs to be subjected to infection detection.

[0112] Each module in the above regional virus infection risk assessment device can be implemented in whole or in part by software, hardware, and their combination. The above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above respective modules.

[0113] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be asFigure 6 As shown in the figure. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface, the display unit, and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, near field communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a method for evaluating the risk of regional virus infection. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the housing of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0114] Those skilled in the art can understand that Figure 6 the structure shown in the figure is only a block diagram of some structures related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0115] In an exemplary embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory. When the processor executes the computer program, the following steps are implemented:

[0116] Obtain the infection-related data generated in the target area during a preset historical period;

[0117] According to the infection-related data, construct an infection knowledge graph corresponding to the target area; the entities in the infection knowledge graph include at least two of patients, pathogens, medical staff, wards, medical devices, and symptoms.

[0118] For any target entity, evaluate the virus infection risk of the target area according to the relationship between the target entity and the associated entity corresponding to the target entity.

[0119] In one embodiment, when the processor executes the computer program, the following steps are further implemented:

[0120] Determine the infection risk value corresponding to the target entity according to the number of associated entities corresponding to the target entity, the number of other associated entities corresponding to the associated entities, and the relationship between the associated entities and the other associated entities;

[0121] Evaluate the virus infection risk of the target area according to the infection risk value;

[0122] Wherein, the other associated entities are other entities that have a relationship with the associated entity except the target entity.

[0123] In one embodiment, when the processor executes the computer program, the following steps are further implemented:

[0124] If the infection risk value indicates that the target entity has an infection risk, select the entity to be intervened corresponding to the target entity from the infection knowledge graph.

[0125] In one embodiment, when the processor executes the computer program, the following steps are further implemented:

[0126] Select the entity to be intervened corresponding to the target entity from the infection knowledge graph according to the entity type of the target entity.

[0127] In one embodiment, when the processor executes the computer program, the following steps are further implemented:

[0128] In the case where the target entity is a type of entity, other entities in the infection knowledge graph that have an association relationship with the target entity are used as entities to be intervened that need to take prevention and control measures; the type of entity includes medical staff, wards, and medical devices.

[0129] In one embodiment, when the processor executes the computer program, the following steps are further implemented:

[0130] In the case where the target entity is a symptom, the associated entity corresponding to the symptom is used as the entity to be intervened that needs to be subjected to infection detection.

[0131] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0132] Obtain the infection association data generated in the target area during the preset historical period;

[0133] Construct an infection knowledge graph corresponding to the target area according to the infection association data; the entities in the infection knowledge graph include at least two of patients, pathogens, medical staff, wards, medical devices, and symptoms;

[0134] For any target entity, evaluate the risk of virus infection in the target area according to the relationship between the target entity and the associated entity corresponding to the target entity.

[0135] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0136] Determine the infection risk value corresponding to the target entity according to the number of associated entities corresponding to the target entity, the number of other associated entities corresponding to the associated entity, and the relationship between the associated entity and the other associated entity;

[0137] Evaluate the risk of virus infection in the target area according to the infection risk value;

[0138] Wherein, the other associated entity is another entity that has a relationship with the associated entity except the target entity.

[0139] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0140] If the infection risk value indicates that the target entity has an infection risk, select the entity to be intervened corresponding to the target entity from the infection knowledge graph.

[0141] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0142] Select the entity to be intervened corresponding to the target entity from the infection knowledge graph according to the entity type of the target entity.

[0143] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0144] In the case where the target entity is a type of entity, other entities in the infection knowledge graph that have an associated relationship with the target entity are used as the entities to be intervened that need to take prevention and control measures; the type of entity includes medical staff, wards, and medical devices.

[0145] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0146] In the case where the target entity is a symptom, the associated entity corresponding to the symptom is used as the entity to be intervened that needs to be subjected to infection detection.

[0147] In one embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0148] Obtain the infection association data generated in the target area during a preset historical period;

[0149] Construct an infection knowledge graph corresponding to the target area based on the infection-related data; the entities in the infection knowledge graph include at least two of patients, pathogens, medical staff, wards, medical devices, and symptoms.

[0150] For any target entity, evaluate the risk of virus infection in the target area according to the relationship between the target entity and the associated entities corresponding to the target entity.

[0151] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0152] Determine the infection risk value corresponding to the target entity according to the number of associated entities corresponding to the target entity, the number of other associated entities corresponding to the associated entities, and the relationship between the associated entities and the other associated entities.

[0153] Evaluate the risk of virus infection in the target area according to the infection risk value.

[0154] Wherein, the other associated entity is another entity that has a relationship with the associated entity except the target entity.

[0155] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0156] If the infection risk value indicates that the target entity has an infection risk, select the entity to be intervened corresponding to the target entity from the infection knowledge graph.

[0157] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0158] Select the entity to be intervened corresponding to the target entity from the infection knowledge graph according to the entity type of the target entity.

[0159] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0160] In the case where the target entity is a type of entity, use the other entities in the infection knowledge graph that have an associated relationship with the target entity as the entities to be intervened that need to take prevention and control measures; the type of entity includes medical staff, wards, and medical devices.

[0161] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0162] In the case where the target entity is a symptom, use the associated entity corresponding to the symptom as the entity to be intervened that needs to be subjected to infection detection.

[0163] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in this application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in this application can be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, data processing logics based on quantum computing, artificial intelligence (AI) processors, etc., without limitation.

[0164] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered to be within the scope recorded in this application.

[0165] The above embodiments only represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims

1. A method for assessing the risk of regional virus infection, characterized in that, The method includes: Obtaining infection-related data generated in a target area during a preset historical period; Constructing an infection knowledge graph corresponding to the target area according to the infection-related data; the entities in the infection knowledge graph include at least two of patients, pathogens, medical staff, wards, medical devices, and symptoms; For any target entity, evaluating the risk of virus infection in the target area according to the relationship between the target entity and the associated entity corresponding to the target entity.

2. The method according to claim 1, wherein The evaluating the risk of virus infection in the target area according to the relationship between the target entity and the associated entity corresponding to the target entity includes: Determining an infection risk value corresponding to the target entity according to the number of associated entities corresponding to the target entity, the number of other associated entities corresponding to the associated entities, and the relationship between the associated entities and the other associated entities; Evaluating the risk of virus infection in the target area according to the infection risk value; Wherein, the other associated entity is another entity that has a relationship with the associated entity except the target entity.

3. The method according to claim 2, characterized in that, The method further includes: If the infection risk value indicates that the target entity has an infection risk, selecting a to-be-intervened entity corresponding to the target entity from the infection knowledge graph.

4. The method according to claim 3, characterized in that The selecting a to-be-intervened entity corresponding to the target entity from the infection knowledge graph includes: Selecting a to-be-intervened entity corresponding to the target entity from the infection knowledge graph according to the entity type of the target entity.

5. The method according to claim 4, wherein The selecting a to-be-intervened entity corresponding to the target entity from the infection knowledge graph according to the entity type of the target entity includes: When the target entity is a type of entity, taking other entities in the infection knowledge graph that have an associated relationship with the target entity as to-be-intervened entities that need to take prevention and control measures; the type of entity includes medical staff, wards, and medical devices.

6. The method according to claim 4, characterized in that, The selecting a to-be-intervened entity corresponding to the target entity from the infection knowledge graph according to the entity type of the target entity includes: When the target entity is a symptom, taking the associated entity corresponding to the symptom as a to-be-intervened entity that needs to be subjected to infection detection.

7. A device for assessing the risk of regional virus infection, characterized in that, The device includes: An obtaining module, configured to obtain infection-related data generated in a target area during a preset historical period; A constructing module, configured to construct an infection knowledge graph corresponding to the target area according to the infection-related data; the entities in the infection knowledge graph include at least two of patients, pathogens, medical staff, wards, medical devices, and symptoms; An evaluating module, configured to evaluate the risk of virus infection in the target area for any target entity according to the relationship between the target entity and the associated entity corresponding to the target entity.

8. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program, characterized in that, When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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