CN118072926B - Medical institution department two-stage infection risk assessment system and method - Google Patents
Medical institution department two-stage infection risk assessment system and method Download PDFInfo
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Abstract
The application discloses a medical institution department two-stage infection risk assessment system and a method, which relate to the field of intelligent assessment, and are characterized in that infection related data of each department are collected, a data analysis algorithm based on risk assessment and weight processing is adopted, infection risk factors are extracted from the infection related data of each department, risk coefficients are assigned to each department aiming at the infection factors of each department, and then the risk coefficients of each department are sequentially processed based on a first risk weight vector and a second risk weight vector, so that department-level risk assessment coefficients and yard-level risk assessment coefficients are determined. Therefore, the actual risk condition of each department can be reflected more accurately, and the infection data of each department can be integrated systematically, so that the comprehensive risk assessment coefficient of the yard grade can be obtained accurately. In this way, the management and medical staff can be helped to better know and manage the infection risk, so that the effect and efficiency of risk management are improved, and corresponding preventive and control measures can be timely taken.
Description
Technical Field
The application relates to the field of intelligent evaluation, and more particularly relates to a medical institution hospital two-stage infection risk evaluation system and method.
Background
A medical institution department two-stage infection risk assessment system is a system for assessing infection risk of medical institutions and departments. The system is mainly used for comprehensive analysis, evaluation, prejudgment, screening, intervention and other activities carried out by medical institutions and medical staff aiming at sensing and controlling risks, so that the normative requirements of infection occurrence risks are reduced. However, the traditional two-stage infection risk assessment method of the medical institution department usually only focuses on the infection risk assessment of a specific department or the whole medical institution, and the lack of risk comparison and comprehensive assessment between different departments leads to a relatively average whole assessment result, and some important risks may be ignored. In addition, the conventional method may rely on only empirical judgment or only simple statistical analysis, and cannot accurately evaluate the influence degree of different factors on infection risk, resulting in uncertainty and inaccuracy of the evaluation result. Accordingly, an optimized medical facility hospital two-stage infection risk assessment system and method are desired.
Disclosure of Invention
The present application has been made to solve the above-mentioned technical problems.
According to one aspect of the present application, there is provided a medical institution hospital two-stage infection risk assessment system comprising:
the infection data collection module is used for collecting infection related data of each department;
The infection risk factor extraction module is used for extracting infection risk factors from the infection related data of each department respectively to obtain a sequence of a department infection factor set;
The risk coefficient assignment module is used for assigning risk coefficients to each department infection factor in the department infection factor set respectively aiming at each department infection factor set in the sequence of the department infection factor set to obtain a plurality of risk coefficient sets;
The department risk coefficient evaluation module is used for respectively processing each risk coefficient set in the plurality of risk coefficient sets by using a first risk weight vector to obtain a sequence of department-level risk evaluation coefficients;
the yard risk coefficient evaluation module is used for processing the sequence of department level risk evaluation coefficients by using a second risk weight vector to obtain yard risk evaluation coefficients;
wherein processing the sequence of department level risk assessment coefficients using a second risk weight vector to obtain an yard level risk assessment coefficient comprises:
the risk assessment coefficient arrangement unit is used for arranging the sequence of department level risk assessment coefficients into department level risk assessment coefficient input vectors according to sample dimensions;
The incidence matrix calculating unit is used for calculating a position-by-position incidence matrix between the department level risk assessment coefficient input vector and the second risk weight vector;
the distributed response stacking optimization unit is used for performing position sensitivity distributed response stacking optimization on the position-by-position correlation matrix to obtain an optimized position-by-position correlation matrix;
the global mean value calculation unit is used for calculating the global mean value of the optimized position-by-position correlation matrix to obtain the yard-level risk assessment coefficient;
wherein the distributed response stack optimization unit comprises:
a location susceptibility value determining subunit, configured to determine a location susceptibility value of each location in the location-by-location correlation matrix by using a location susceptibility function to obtain a location susceptibility feature matrix, where the location susceptibility function is: ， Representing the locations in the location-by-location correlation matrix, Position sensitivity values representing respective positions in the position-by-position correlation matrix,Representing a sine function;
a matrix expansion subunit, configured to expand the position-by-position correlation matrix and the position-sensitive feature matrix into a position-by-position correlation vector and a position-sensitive feature vector, respectively;
The characteristic optimization subunit is used for processing the position-by-position association vector and the position-sensitive characteristic vector by using a distributed response stacking optimization formula to obtain an optimized position-by-position association vector;
the dimension reconstruction subunit is used for carrying out dimension reconstruction on the optimized position-by-position correlation vector so as to obtain the optimized position-by-position correlation matrix;
wherein, the distributed response stacking optimization formula is:
；
Wherein, AndRepresenting the position-wise correlation vector and the position-sensitive feature vector respectively,Is vectorThe square of the norm,Is the parameter of the ultrasonic wave to be used as the ultrasonic wave,Representing the optimized position-by-position correlation vector.
In the medical institution hospital two-stage infection risk assessment system, the infection-related data includes department infection control data, patient safety event reports and environmental monitoring results.
In the medical institution hospital two-stage infection risk assessment system described above, the infection risk factors include a specific patient population including intensive care patients and immunosuppressed patients, a specific surgery which is an invasive surgery, and a specific environment which is chemotherapy, including an isolation ward and an operating room.
In the two-stage infection risk assessment system of medical institution department, the risk coefficient assignment module is configured to: assigning risk factors to each department infection factor in the set of department infection factors based on reference risk factor assessment criteria to obtain the set of risk factors.
In the two-stage infection risk assessment system for medical institutions, the department-level risk assessment coefficient module comprises: the risk coefficient sample arrangement unit is used for arranging the risk coefficient sets into risk coefficient input vectors according to sample dimensions; and a coefficient weighted sum unit for calculating a position weighted sum of the risk coefficient input vectors by using the first risk weight vector to obtain the department level risk assessment coefficient.
In the medical institution department two-stage infection risk assessment system, the feature optimization subunit is configured to: creating a SPRING MVC controller for processing the request; extracting, in a controller, the distributed response stack optimization formula using a distributed response stack library; mapping the controller to a URL so that the client can send the request; and creating a client application for sending a request to the controller and receiving the optimized location-by-location association vector.
According to another aspect of the present application, there is provided a medical institution hospital two-stage infection risk assessment method comprising:
Collecting infection related data of each department;
extracting infection risk factors from infection related data of each department respectively to obtain sequences of department infection factor sets;
Aiming at each department infection factor set in the sequence of the department infection factor sets, respectively designating risk factors for each department infection factor in the department infection factor sets to obtain a plurality of risk factor sets;
Processing each risk coefficient set in the plurality of risk coefficient sets by using a first risk weight vector to obtain a sequence of department level risk assessment coefficients;
processing the sequence of department level risk assessment coefficients by using a second risk weight vector to obtain an yard level risk assessment coefficient;
wherein processing the sequence of department level risk assessment coefficients using a second risk weight vector to obtain an yard level risk assessment coefficient comprises:
Arranging the sequence of department level risk assessment coefficients into department level risk assessment coefficient input vectors according to sample dimensions;
calculating a position-by-position correlation matrix between the department level risk assessment coefficient input vector and the second risk weight vector;
Performing position sensitivity distribution response stacking optimization on the position-by-position correlation matrix to obtain an optimized position-by-position correlation matrix;
Calculating a global mean value of the optimized position-by-position incidence matrix to obtain the yard-level risk assessment coefficient;
Performing position sensitivity distribution response stacking optimization on the position-by-position correlation matrix to obtain an optimized position-by-position correlation matrix, including:
Determining a position sensitivity value of each position in the position-by-position correlation matrix by using a position sensitivity function to obtain a position sensitivity characteristic matrix, wherein the position sensitivity function is as follows: ， Representing the locations in the location-by-location correlation matrix, Position sensitivity values representing respective positions in the position-by-position correlation matrix,Representing a sine function;
a matrix expansion subunit, configured to expand the position-by-position correlation matrix and the position-sensitive feature matrix into a position-by-position correlation vector and a position-sensitive feature vector, respectively;
The characteristic optimization subunit is used for processing the position-by-position association vector and the position-sensitive characteristic vector by using a distributed response stacking optimization formula to obtain an optimized position-by-position association vector;
the dimension reconstruction subunit is used for carrying out dimension reconstruction on the optimized position-by-position correlation vector so as to obtain the optimized position-by-position correlation matrix;
wherein, the distributed response stacking optimization formula is:
；
Wherein, AndRepresenting the position-wise correlation vector and the position-sensitive feature vector respectively,Is vectorThe square of the norm,Is the parameter of the ultrasonic wave to be used as the ultrasonic wave,Representing the optimized position-by-position correlation vector.
Compared with the prior art, the two-stage infection risk assessment system and method for the medical institution department provided by the application have the advantages that the infection related data of each department are collected, the data analysis algorithm based on risk assessment and weight processing is adopted, the infection risk factors are extracted from the infection related data of each department, the risk factors are assigned to each department aiming at the infection factors of each department, and then the risk factors of each department are sequentially processed based on the first risk weight vector and the second risk weight vector, so that the department-level risk assessment coefficients and the yard-level risk assessment coefficients are determined. By the method, the actual risk conditions of the departments can be reflected more accurately, and the infection data of the departments are integrated systematically, so that the comprehensive risk assessment coefficient of the yard grade can be obtained accurately. In this way, the management and medical staff can be helped to better know and manage the infection risk, so that the effect and efficiency of risk management are improved, and corresponding preventive and control measures can be timely taken.
Drawings
The above and other objects, features and advantages of the present application will become more apparent by describing embodiments of the present application in more detail with reference to the attached drawings. The accompanying drawings are included to provide a further understanding of embodiments of the application and are incorporated in and constitute a part of this specification, illustrate the application and together with the embodiments of the application, and not constitute a limitation to the application. In the drawings, like reference numerals generally refer to like parts or steps.
FIG. 1 is a block diagram of a medical facility hospital two-stage infection risk assessment system according to an embodiment of the present application.
Fig. 2 is a schematic diagram of a medical institution hospital two-stage infection risk assessment system according to an embodiment of the present application.
Fig. 3 is a block diagram of a department level risk assessment coefficient module in a medical institution hospital two-stage infection risk assessment system according to an embodiment of the present application.
Fig. 4 is a block diagram of a hospital-level risk assessment coefficient module in a medical facility hospital-department two-level infection risk assessment system according to an embodiment of the present application.
Fig. 5 is a flow chart of a medical institution hospital two-stage infection risk assessment method according to an embodiment of the present application.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure have been shown in the accompanying drawings, it is to be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustration purposes only and are not intended to limit the scope of the present disclosure.
It should be understood that the various steps recited in the method embodiments of the present disclosure may be performed in a different order and/or performed in parallel. Furthermore, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the present disclosure is not limited in this respect.
In describing embodiments of the present disclosure, the term "comprising" and its like should be taken to be open-ended, i.e., including, but not limited to. The term "based on" should be understood as "based at least in part on". The term "one embodiment" or "the embodiment" should be understood as "at least one embodiment". The terms "first," "second," and the like, may refer to different or the same object. Other explicit and implicit definitions are also possible below.
It should be noted that references to "one", "a plurality" and "a plurality" in this disclosure are intended to be illustrative rather than limiting, and those of ordinary skill in the art will appreciate that "one or more" is intended to be construed unless the context clearly indicates otherwise.
A medical institution department two-stage infection risk assessment system is a system for assessing infection risk of medical institutions and departments. The system is mainly used for comprehensive analysis, evaluation, prejudgment, screening, intervention and other activities carried out by medical institutions and medical staff aiming at sensing and controlling risks. Through two-stage infection risk assessment of medical institutions and departments, the infection risk levels of different areas and departments in the medical institutions can be more accurately assessed, so that a more effective infection control strategy is formulated, and the infection control effect is improved.
However, the traditional two-stage infection risk assessment method of the medical institution department usually only focuses on the infection risk assessment of a specific department or the whole medical institution, and the lack of risk comparison and comprehensive assessment between different departments leads to a relatively average whole assessment result, and some important risks may be ignored. In addition, the conventional method may rely on only empirical judgment or simple statistical analysis, and cannot accurately evaluate the influence degree of different factors on infection risk, resulting in uncertainty and inaccuracy of the evaluation result.
Therefore, in order to solve the technical problems, the technical concept of the application is to extract infection risk factors from infection related data of each department by collecting the infection related data of each department and adopting a data analysis algorithm based on risk assessment and weight processing, assign risk factors to each department for each department infection factor, and then process the risk factors of each department based on a first risk weight vector and a second risk weight vector in sequence so as to determine department-level risk assessment coefficients and yard-level risk assessment coefficients. By the method, the actual risk conditions of the departments can be reflected more accurately, and the infection data of the departments are integrated systematically, so that the comprehensive risk assessment coefficient of the yard grade can be obtained accurately. In this way, the management and medical staff can be helped to better know and manage the infection risk, so that the effect and efficiency of risk management are improved, and corresponding preventive and control measures can be timely taken.
FIG. 1 is a block diagram of a medical facility hospital two-stage infection risk assessment system according to an embodiment of the present application. Fig. 2 is a schematic diagram of a medical institution hospital two-stage infection risk assessment system according to an embodiment of the present application. As shown in fig. 1 and 2, a medical institution hospital two-stage infection risk assessment system 100 according to an embodiment of the present application includes: an infection data collection module 110 for collecting infection-related data of each department; an infection risk factor extracting module 120, configured to extract infection risk factors from the infection related data of each department, so as to obtain a sequence of a department infection factor set; a risk factor assignment module 130, configured to assign risk factors to respective department infection factors in the department infection factor set for respective department infection factor sets in the sequence of the department infection factor set to obtain a plurality of risk factor sets; a department level risk assessment coefficient module 140, configured to process each risk coefficient set in the plurality of risk coefficient sets by using a first risk weight vector to obtain a sequence of department level risk assessment coefficients; and a yard-level risk assessment coefficient module 150, configured to process the sequence of department-level risk assessment coefficients using a second risk weight vector to obtain a yard-level risk assessment coefficient.
In the embodiment of the present application, the infection data collection module 110 is configured to collect infection related data of each department. Specifically, in the embodiment of the application, the infection related data comprises department infection control data, patient safety event reports and environmental monitoring results. It should be appreciated that taking into account the department infection control data, the patient safety event report and the environmental monitoring results in the infection-related data are important factors for infection risk assessment. Specifically, the department infection control data reflects the execution of the department infection control measures and the occurrence of the department infection. For example, information about the execution of disinfection, equipment cleaning, etc. and the incidence of infection, type of infection, site of infection, etc., can help to evaluate the effectiveness of infection control measures in a department. The patient safety event report reflects the occurrence of a patient safety event. For example, patient safety issues may be found in reporting patient safety events such as medical related infections, medication errors, and the like. The environmental monitoring result reflects the clean condition of the department environment. For example, air quality monitoring, water quality monitoring, etc., can help assess environmental impact on infection transmission. Based on the method, in the technical scheme of the application, the relevant infection data of each department is collected and processed and analyzed, so that the infection risk level of the department can be quantitatively estimated, and objective basis is provided for two-stage infection risk estimation of the department, therefore, the medical institution can continuously improve the medical quality, improve the safety level of patients and reduce the infection rate.
In the embodiment of the present application, the infection risk factor extracting module 120 is configured to extract infection risk factors from the infection related data of each department to obtain a sequence of a department infection factor set. Specifically, in embodiments of the present application, the infection risk factors include a particular patient population including intensive care patients and immunosuppressed patients, a particular surgery that is invasive surgery, a particular treatment that is chemotherapy, and a particular environment including an isolation ward and an operating room. Accordingly, in order to better understand the sources of infection risk factors of different departments and effectively and quantitatively evaluate the infection risk level of each department according to the influence degree of the different infection risk factors, in the technical scheme of the application, the infection risk factors are respectively extracted from the infection related data of each department to obtain the sequences of the infection factor sets of the departments, so that the infection risk factors of specific patient groups, specific operations, specific treatments, specific environments and the like can be accurately known, basis and support are provided for subsequent risk evaluation of each department and risk evaluation of the courtyard, thereby establishing personalized infection control strategies and pertinently enhancing the infection prevention measures of each department.
In the embodiment of the present application, the risk factor assignment module 130 is configured to assign risk factors to each department infection factor in the department infection factor set, for each department infection factor in the sequence of the department infection factor set, so as to obtain multiple risk factor sets. Specifically, in the embodiment of the present application, the risk factor assignment module is configured to: assigning risk factors to each department infection factor in the set of department infection factors based on reference risk factor assessment criteria to obtain the set of risk factors. It is worth mentioning that the reference risk factor assessment criterion is a criterion or method for assessing and quantifying the extent to which a specific risk factor contributes to the overall risk. In particular, in the medical field, risk factor assessment criteria are commonly used to determine the extent to which a particular factor affects the risk of infection, helping medical institutions identify and manage the risk of infection. Therefore, in the technical scheme of the application, based on the reference risk factor evaluation standard, the risk factors are designated for each department infection factor in the department infection factor set to obtain the risk factor set, and the medical institution can be helped to evaluate the infection risk of different departments in a standardized manner so as to ensure objective, scientific and consistent evaluation process, thereby helping the medical institution to better manage the infection risk, taking measures in time to reduce the risk and guaranteeing the safety of patients.
In the embodiment of the present application, the department level risk assessment coefficient module 140 is configured to process each risk coefficient set of the plurality of risk coefficient sets by using a first risk weight vector to obtain a sequence of department level risk assessment coefficients. Fig. 3 is a block diagram of a department level risk assessment coefficient module in a medical institution hospital two-stage infection risk assessment system according to an embodiment of the present application. Specifically, in the embodiment of the present application, as shown in fig. 3, the department level risk assessment coefficient module 140 includes: a risk coefficient sample arrangement unit 141, configured to arrange the risk coefficient sets into risk coefficient input vectors according to sample dimensions; and a coefficient weighted sum unit 142 for calculating a position weighted sum of the risk coefficient input vectors using the first risk weight vector to obtain the department level risk assessment coefficient. Accordingly, it is contemplated that the first risk weight vector refers to a vector used to weight different risk factors in risk assessment or risk management. In particular, this weight vector may reflect the importance or contribution of different laboratory infective elements in the overall risk of infective element, thereby giving the different infective elements the appropriate weight when comprehensively considering the infective risk factors. Therefore, in the technical scheme of the application, the risk coefficient sets are arranged into the risk coefficient input vectors according to sample dimensions, and the first risk weight vector is used for calculating the weighted sum of the risk coefficient input vectors according to positions, so that the contribution degree of each infection risk factor to different department risks can be more clearly known, and the obtained more accurate and comprehensive department-level risk assessment coefficients are obtained.
In an embodiment of the present application, the yard-level risk assessment coefficient module 150 is configured to process the sequence of department-level risk assessment coefficients using a second risk weight vector to obtain a yard-level risk assessment coefficient. Fig. 4 is a block diagram of a hospital-level risk assessment coefficient module in a medical facility hospital-department two-level infection risk assessment system according to an embodiment of the present application. Specifically, in the embodiment of the present application, as shown in fig. 4, the yard-level risk assessment coefficient module 150 includes: a risk assessment coefficient arrangement unit 151, configured to arrange the sequence of department level risk assessment coefficients into department level risk assessment coefficient input vectors according to sample dimensions; a correlation matrix calculating unit 152, configured to calculate a position-by-position correlation matrix between the department level risk assessment coefficient input vector and the second risk weight vector; a distributed response stack optimization unit 153, configured to perform a position-sensitive distributed response stack optimization on the position-by-position correlation matrix to obtain an optimized position-by-position correlation matrix; and a global average calculating unit 154, configured to calculate a global average of the optimized position-by-position association matrix to obtain the yard-level risk assessment coefficient.
Specifically, the distributed response stack optimization unit 153 is configured to perform position-sensitive distributed response stack optimization on the position-by-position correlation matrix to obtain an optimized position-by-position correlation matrix. It should be understood that each element in the position-by-position correlation matrix corresponds to a specific position in the department level risk assessment coefficient and the second risk weight vector, so in order to improve accuracy and robustness of the yard level risk assessment coefficient, in the technical solution of the present application, position-sensitive distributed response stacking optimization is performed on the position-by-position correlation matrix to obtain an optimized position-by-position correlation matrix. In particular, the location sensitivity takes into account dependencies between different locations in the location-by-location correlation matrix and allows a model to learn the unique contribution of each location in the location-by-location correlation matrix to the yard-level risk assessment coefficient. The distributed response stacking is a machine learning technique that improves the accuracy of predictions by combining the predictions of multiple models. That is, in this case, distributed response stack optimization involves optimizing the position-wise correlation matrix using a plurality of optimization algorithms, and combining their predicted results. In general, the goal of location-sensitive distribution response stack optimization is to find an optimized location-by-location correlation matrix to minimize errors between the department level risk assessment coefficients and the yard level risk assessment coefficients. That is, by performing the position-sensitive distributed response stacking optimization on the position-by-position correlation matrix, a more accurate and more robust optimized position-by-position correlation matrix can be obtained, thereby improving the overall quality of the yard-level risk assessment coefficient.
In particular, here, the position sensitive distributed response stack optimization uses multiple optimization algorithms, which help reduce over-fitting and improve the robustness of the model to different data sets. The obtained optimized position-by-position correlation matrix can capture complex relations between the department level risk assessment coefficients and the yard level risk assessment coefficients, and provide insight of the relative importance of each department level risk assessment coefficient to the yard level risk assessment coefficients, so that the interpretability of the model is enhanced, and the prediction accuracy is improved.
Specifically, in the embodiment of the present application, the distributed response stack optimization unit 153 includes: a location susceptibility value determining subunit, configured to determine a location susceptibility value of each location in the location-by-location correlation matrix by using a location susceptibility function to obtain a location susceptibility feature matrix, where the location susceptibility function is:， Representing the locations in the location-by-location correlation matrix, Position sensitivity values representing respective positions in the position-by-position correlation matrix,Representing a sine function; a matrix expansion subunit, configured to expand the position-by-position correlation matrix and the position-sensitive feature matrix into a position-by-position correlation vector and a position-sensitive feature vector, respectively; the characteristic optimization subunit is used for processing the position-by-position association vector and the position-sensitive characteristic vector by using a distributed response stacking optimization formula to obtain an optimized position-by-position association vector; and the dimension reconstruction subunit is used for carrying out dimension reconstruction on the optimized position-by-position correlation vector so as to obtain the optimized position-by-position correlation matrix.
Specifically, in the embodiment of the present application, the distributed response stacking optimization formula is:
；
Wherein, AndRepresenting the position-wise correlation vector and the position-sensitive feature vector respectively,Is vectorThe square of the norm,Is the parameter of the ultrasonic wave to be used as the ultrasonic wave,Representing the optimized position-by-position correlation vector.
Specifically, in an embodiment of the present application, the feature optimization subunit is configured to: creating a SPRING MVC controller for processing the request; extracting, in a controller, the distributed response stack optimization formula using a distributed response stack library; mapping the controller to a URL so that the client can send the request; and creating a client application for sending a request to the controller and receiving the optimized location-by-location association vector.
In particular, in one embodiment of the present application, the processing of the position-by-position correlation vector and the position-sensitive feature vector using a distributed response stack optimization formula to obtain an optimized position-by-position correlation vector may be implemented by the following codes:
@RestController
public class OptimizationController {
@PostMapping("/optimize")
public OptimizedLocationAssociationVector optimize(@RequestBody LocationAssociationVector lav,
@RequestBody LocationSensitiveFeatureVector lsfv) {
return new OptimizedLocationAssociationVector(DistributedResponseStacking.optimize(lav, lsfv));
}
}
public class Client {
public static void main(String[] args) {
// Create the vectors
LocationAssociationVector lav = new LocationAssociationVector(...);
LocationSensitiveFeatureVector lsfv = new LocationSensitiveFeatureVector(...);
// Send the request to the controller
OptimizedLocationAssociationVector optimizedLav = restTemplate.postForObject("https://localhost:8080/optimize", lav, lsfv, OptimizedLocationAssociationVector.class);
// Use the optimized vector
...
}
}
It should be noted that SPRING MVC is a highly scalable framework that can easily handle large numbers of concurrent requests, which is very important for applications that need to process large amounts of data in real time. In particular, SPRING MVC controllers are loosely coupled to other parts of the application, which allows easy modification or replacement of the optimization algorithm without affecting the rest of the application. And SPRING MVC provides built-in test support, so that the test optimization algorithm is easy, and the accuracy and reliability of the algorithm can be ensured. Also SPRING MVC provides comprehensive security functions and a large and active community that protects applications from attacks and provides support and resources to process applications of sensitive data (e.g., location information) so that help and guidance can be easily obtained when needed.
In summary, the medical institution hospital two-stage infection risk assessment system 100 according to the embodiment of the present application is illustrated, by collecting infection related data of each department, and adopting a data analysis algorithm based on risk assessment and weight processing, extracting infection risk factors from the infection related data of each department, assigning risk coefficients to each department for each department infection factor, and then sequentially processing the risk coefficients of each department based on the first risk weight vector and the second risk weight vector, so as to determine a department-level risk assessment coefficient and an yard-level risk assessment coefficient. By the method, the actual risk conditions of the departments can be reflected more accurately, and the infection data of the departments are integrated systematically, so that the comprehensive risk assessment coefficient of the yard grade can be obtained accurately. In this way, the management and medical staff can be helped to better know and manage the infection risk, so that the effect and efficiency of risk management are improved, and corresponding preventive and control measures can be timely taken.
As described above, the medical institution hospital two-stage infection risk assessment system 100 according to the embodiment of the present application may be implemented in various wireless terminals, for example, a server or the like having a medical institution hospital two-stage infection risk assessment algorithm. In one possible implementation, the medical facility hospital two-stage infection risk assessment system 100 according to embodiments of the present application may be integrated into the wireless terminal as one software module and/or hardware module. For example, the medical facility hospital two-level infection risk assessment system 100 may be a software module in the operating system of the wireless terminal, or may be an application developed for the wireless terminal; of course, the medical facility hospital two-stage infection risk assessment system 100 can also be one of the plurality of hardware modules of the wireless terminal.
Alternatively, in another example, the medical facility hospital two-stage infection risk assessment system 100 and the wireless terminal may be separate devices, and the medical facility hospital two-stage infection risk assessment system 100 may be connected to the wireless terminal through a wired and/or wireless network and transmit interaction information in a contracted data format.
Fig. 5 is a flow chart of a medical institution hospital two-stage infection risk assessment method according to an embodiment of the present application. As shown in fig. 5, a medical institution hospital two-stage infection risk assessment method according to an embodiment of the present application includes: s110, collecting infection related data of each department; s120, extracting infection risk factors from the infection related data of each department respectively to obtain sequences of department infection factor sets; s130, aiming at each department infection factor set in the sequence of the department infection factor sets, respectively assigning risk factors to each department infection factor in the department infection factor sets to obtain a plurality of risk factor sets; s140, respectively processing each risk coefficient set in the plurality of risk coefficient sets by using a first risk weight vector to obtain a sequence of department-level risk assessment coefficients; and S150, processing the sequence of department level risk assessment coefficients by using a second risk weight vector to obtain yard level risk assessment coefficients.
Here, it will be understood by those skilled in the art that the specific operations of the respective steps in the above-described medical institution hospital two-stage infection risk assessment method have been described in detail in the above description of the medical institution hospital two-stage infection risk assessment system with reference to fig. 1 to 4, and thus, repetitive descriptions thereof will be omitted.
Implementations of the present disclosure have been described above, the foregoing description is exemplary rather than exhaustive. And is not limited to the implementations disclosed, many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the implementations described. The terminology used herein was chosen in order to best explain the principles of each implementation, the practical application, or the improvement of technology in the marketplace, or to enable others of ordinary skill in the art to understand each of the implementations disclosed herein.
Claims (7)
1. A medical facility hospital two-stage infection risk assessment system, comprising:
the infection data collection module is used for collecting infection related data of each department;
The infection risk factor extraction module is used for extracting infection risk factors from the infection related data of each department respectively to obtain a sequence of a department infection factor set;
The risk coefficient assignment module is used for assigning risk coefficients to each department infection factor in the department infection factor set respectively aiming at each department infection factor set in the sequence of the department infection factor set to obtain a plurality of risk coefficient sets;
the department level risk assessment coefficient module is used for respectively processing each risk coefficient set in the plurality of risk coefficient sets by using a first risk weight vector to obtain a sequence of department level risk assessment coefficients;
The yard-level risk assessment coefficient module is used for processing the sequence of department-level risk assessment coefficients by using a second risk weight vector to obtain yard-level risk assessment coefficients;
wherein processing the sequence of department level risk assessment coefficients using a second risk weight vector to obtain an yard level risk assessment coefficient comprises:
the risk assessment coefficient arrangement unit is used for arranging the sequence of department level risk assessment coefficients into department level risk assessment coefficient input vectors according to sample dimensions;
The incidence matrix calculating unit is used for calculating a position-by-position incidence matrix between the department level risk assessment coefficient input vector and the second risk weight vector;
the distributed response stacking optimization unit is used for performing position sensitivity distributed response stacking optimization on the position-by-position correlation matrix to obtain an optimized position-by-position correlation matrix;
the global mean value calculation unit is used for calculating the global mean value of the optimized position-by-position correlation matrix to obtain the yard-level risk assessment coefficient;
wherein the distributed response stack optimization unit comprises:
a location susceptibility value determining subunit, configured to determine a location susceptibility value of each location in the location-by-location correlation matrix by using a location susceptibility function to obtain a location susceptibility feature matrix, where the location susceptibility function is: ， Representing the locations in the location-by-location correlation matrix, Position sensitivity values representing respective positions in the position-by-position correlation matrix,Representing a sine function;
a matrix expansion subunit, configured to expand the position-by-position correlation matrix and the position-sensitive feature matrix into a position-by-position correlation vector and a position-sensitive feature vector, respectively;
The characteristic optimization subunit is used for processing the position-by-position association vector and the position-sensitive characteristic vector by using a distributed response stacking optimization formula to obtain an optimized position-by-position association vector;
the dimension reconstruction subunit is used for carrying out dimension reconstruction on the optimized position-by-position correlation vector so as to obtain the optimized position-by-position correlation matrix;
wherein, the distributed response stacking optimization formula is:
；
Wherein, AndRepresenting the position-wise correlation vector and the position-sensitive feature vector respectively,Is vectorThe square of the norm,Is the parameter of the ultrasonic wave to be used as the ultrasonic wave,Representing the optimized position-by-position correlation vector.
2. The medical facility hospital two-stage infection risk assessment system of claim 1, wherein the infection-related data comprises department infection control data, patient safety event reports, and environmental monitoring results.
3. The medical facility hospital two-stage infection risk assessment system of claim 2, wherein the infection risk factors include a specific patient population including intensive care patients and immunosuppressed patients, a specific procedure that is invasive surgery, and a specific environment that is chemotherapy, including an isolation ward and an operating room.
4. The medical facility hospital two-stage infection risk assessment system of claim 3, wherein the risk factor assignment module is configured to: assigning risk factors to each department infection factor in the set of department infection factors based on reference risk factor assessment criteria to obtain the set of risk factors.
5. The medical facility hospital two-stage infection risk assessment system of claim 4, wherein the department level risk assessment coefficient module comprises:
The risk coefficient sample arrangement unit is used for arranging the risk coefficient sets into risk coefficient input vectors according to sample dimensions;
And the coefficient weighted sum unit is used for calculating a position weighted sum of the risk coefficient input vectors by using the first risk weight vector so as to obtain the department level risk assessment coefficient.
6. The medical facility hospital two-stage infection risk assessment system of claim 5, wherein the feature optimization subunit is configured to:
Creating a SPRING MVC controller for processing the request;
extracting, in a controller, the distributed response stack optimization formula using a distributed response stack library;
Mapping the controller to a URL so that the client can send the request;
A client application is created for sending a request to the controller and receiving the optimized location-by-location association vector.
7. A medical institution hospital two-stage infection risk assessment method, comprising:
Collecting infection related data of each department;
extracting infection risk factors from infection related data of each department respectively to obtain sequences of department infection factor sets;
Aiming at each department infection factor set in the sequence of the department infection factor sets, respectively designating risk factors for each department infection factor in the department infection factor sets to obtain a plurality of risk factor sets;
Processing each risk coefficient set in the plurality of risk coefficient sets by using a first risk weight vector to obtain a sequence of department level risk assessment coefficients;
processing the sequence of department level risk assessment coefficients by using a second risk weight vector to obtain an yard level risk assessment coefficient;
wherein processing the sequence of department level risk assessment coefficients using a second risk weight vector to obtain an yard level risk assessment coefficient comprises:
Arranging the sequence of department level risk assessment coefficients into department level risk assessment coefficient input vectors according to sample dimensions;
calculating a position-by-position correlation matrix between the department level risk assessment coefficient input vector and the second risk weight vector;
Performing position sensitivity distribution response stacking optimization on the position-by-position correlation matrix to obtain an optimized position-by-position correlation matrix;
Calculating a global mean value of the optimized position-by-position incidence matrix to obtain the yard-level risk assessment coefficient;
Performing position sensitivity distribution response stacking optimization on the position-by-position correlation matrix to obtain an optimized position-by-position correlation matrix, including:
Determining a position sensitivity value of each position in the position-by-position correlation matrix by using a position sensitivity function to obtain a position sensitivity characteristic matrix, wherein the position sensitivity function is as follows: ， Representing the locations in the location-by-location correlation matrix, Position sensitivity values representing respective positions in the position-by-position correlation matrix,Representing a sine function;
respectively expanding the position-by-position correlation matrix and the position-sensitive feature matrix into a position-by-position correlation vector and a position-sensitive feature vector;
processing the position-by-position correlation vector and the position-sensitive feature vector by using a distributed response stacking optimization formula to obtain an optimized position-by-position correlation vector;
performing dimension reconstruction on the optimized position-by-position correlation vector to obtain the optimized position-by-position correlation matrix;
wherein, the distributed response stacking optimization formula is:
；
Wherein, AndRepresenting the position-wise correlation vector and the position-sensitive feature vector respectively,Is vectorThe square of the norm,Is the parameter of the ultrasonic wave to be used as the ultrasonic wave,Representing the optimized position-by-position correlation vector.
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| CN114692946A (en) * | 2022-02-21 | 2022-07-01 | 湖南省蓝蜻蜓网络科技有限公司 | Hospital infection risk assessment method, equipment and storage medium |
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| CN114692946A (en) * | 2022-02-21 | 2022-07-01 | 湖南省蓝蜻蜓网络科技有限公司 | Hospital infection risk assessment method, equipment and storage medium |
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