CN117423475A - Department master infection risk identification method and system applied to hospital scene - Google Patents
Department master infection risk identification method and system applied to hospital scene Download PDFInfo
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- 208000015181 infectious disease Diseases 0.000 title claims abstract description 79
- 238000000034 method Methods 0.000 title claims abstract description 49
- 238000011156 evaluation Methods 0.000 claims abstract description 31
- 239000011159 matrix material Substances 0.000 claims abstract description 19
- 238000000513 principal component analysis Methods 0.000 claims abstract description 16
- 206010011409 Cross infection Diseases 0.000 claims description 46
- 238000004458 analytical method Methods 0.000 claims description 46
- 230000006870 function Effects 0.000 claims description 40
- 230000000474 nursing effect Effects 0.000 claims description 29
- 206010029803 Nosocomial infection Diseases 0.000 claims description 11
- 238000011835 investigation Methods 0.000 claims description 7
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- 238000012847 principal component analysis method Methods 0.000 abstract description 5
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- 238000012502 risk assessment Methods 0.000 description 3
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- 238000007619 statistical method Methods 0.000 description 2
- 241000894006 Bacteria Species 0.000 description 1
- 208000001528 Coronaviridae Infections Diseases 0.000 description 1
- 206010035664 Pneumonia Diseases 0.000 description 1
- 208000009470 Ventilator-Associated Pneumonia Diseases 0.000 description 1
- 230000002411 adverse Effects 0.000 description 1
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
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- G06F18/2135—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods based on approximation criteria, e.g. principal component analysis
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Abstract
The invention provides a method and a system for identifying risk of main infection of a department, which are applied to a hospital scene, and the method is used for carrying out principal component analysis based on a matrix algorithm, and compared with the traditional principal component analysis method with single result, the method provided by the invention provides a multi-element evaluation system; compared with the traditional multi-result principal component analysis method based on the deep learning training algorithm, the method provided by the invention has the advantages that an evaluation system based on a high-calculation-power computer is not needed, and the degree of dependence on hardware equipment and professional knowledge of personnel is reduced.
Description
Technical Field
The invention relates to the technical field of hospital risk analysis, in particular to a method and a system for identifying risk of infection of a department owner applied to a hospital scene.
Background
Risk assessment is the process of quantitative analysis and assessment before or after the occurrence of a risk event (but not yet completed). Developing a risk assessment of nosocomial infections is an important way to effectively identify and control the risk of nosocomial infections, in which the development of risk identification, analysis and assessment methods is closely related to the continual improvement of medical quality and patient safety. Over the last few decades, hospital risk management has gradually shifted from simple post-hoc error correction to preventive and systematic approaches. The technical background comprises a medical accident report system, an event analysis method, establishment of a quality index system, and application of statistical analysis and data mining technology. These methods help hospitals discover potential risks and take action improvements by collecting, analyzing, and evaluating data of patient events, medical errors, and adverse events. In addition, the introduction of tools such as expert assessment, risk matrix, failure mode and effect analysis also promotes the comprehensiveness and systematicness of risk identification and evaluation. Through continuous risk monitoring and improvement, hospitals can improve patient safety level, reduce medical accident risk, provide better medical quality and nursing service.
The conventional risk identification, analysis and evaluation methods applied to hospital scenes have some defects. First, conventional methods generally rely on manual subjective judgment, are susceptible to individual experience and bias, and lack objectivity and consistency. Second, traditional methods are typically limited to historical data and statistical analysis, failing to adequately account for emerging risks and unknown risks. In addition, conventional methods may have tedious, time-consuming and error problems in data collection, integration and analysis, limiting the accuracy and real-time of risk identification and assessment. In addition, traditional methods lack comprehensiveness and systemicity, and it is difficult to comprehensively and dynamically analyze and evaluate risk factors and risk events. In summary, the shortcomings of the conventional methods in risk identification, analysis and evaluation need to be overcome by introducing new innovative methods and techniques to improve the effectiveness and efficiency of risk management.
The Chinese patent with the bulletin number of CN108461154B discloses a hospital infection monitoring and managing device and a monitoring and managing method; specifically disclosed is: the system comprises a hospital infection monitoring management server, a hospital information integration platform, a hospital information system, a laboratory information system, a medical image archiving and communication system, an electronic medical record system, a hospital infection monitoring server, a WeChat enterprise number server and a smart phone; through the multi-aspect interaction functions of new media information pushing, image-text advertising, questionnaire investigation and the like, the whole hospital infection flow management of a Plan (Plan), an execution (Do), a Check and an adjustment (Action) closed loop is realized. The invention improves the suspected nosocomial infection detection accuracy, reduces the workload of doctors, optimizes the hospital infection monitoring management flow, and can be applied to hospital infection monitoring.
Chinese patent application publication No. CN111768874a discloses a novel method for assessing risk of pneumonic infection by coronavirus infection; specifically disclosed is: the method comprises the following steps: (1) evaluation index screening; (2) Establishing an HVA scoring standard by applying an improved Kaiser model; (3) creating an HVA score table; (4) calculating a Risk value and finally taking an average value; (5) determining a priority improvement event. The improved Kaiser model is applied to infectious disease public health events for the first time, has an important reference function for screening potential infection risk events in the same infectious disease events, avoids the occurrence of risk events, and effectively reduces the risk of cross infection; the emergency plan made by the risk event has extremely strong reference value and has important reference function for other hospitals in the range of the area.
The Chinese patent application with publication number of CN1684082A discloses a system and a method for evaluating and managing the performance of modern hospitals; specifically disclosed is: comprising the following steps: the system comprises an information acquisition subsystem, a database subsystem, a performance analysis subsystem and a leading decision subsystem, wherein the performance analysis subsystem comprises a performance analysis module, a performance index module and a performance report generation module, and the performance index module comprises a resource configuration structure A, a working efficiency B, a medical quality C, an economic benefit D, a service quality E and a development potential F; the performance index system is screened by using a plurality of subjective and objective methods, and the weight coefficient is obtained by integrating a plurality of subjective and objective methods, so that the 6 types of 14 indexes disclosed by the invention are more objective and comprehensive, and the performance calculation formula can reflect the performance level of the evaluated hospital more scientifically and truly.
The partial scheme provides a technical means of principal component analysis, but the calculation method is complex, has high calculation force requirements, and lacks evaluation of nosocomial infection risks.
The problem of "hospital infection management" is actually a "black box problem", that is, different departments exist different technical indexes including medical conditions, nursing conditions and the like, but the problem of hospital infection still occurs, the existing scheme for controlling hospital infection of different departments does not know which index is mainly used for causing the hospital infection of different departments, that is, the main factor of the hospital infection of different departments cannot be quantitatively determined, and during subsequent treatment, no targeted improvement can be performed, so that the current practice requires the departments to improve all indexes, and thus, manpower, material resources and time are greatly consumed.
Disclosure of Invention
The invention provides a department master infection risk identification method applied to a hospital scene, which comprises the following steps:
s1, determining a scene range; based on the specifications of the hospital infection control manual and the specifications, determining a hospital infection control area, and determining a target department for identifying the risk of main infection;
s2, establishing a department infection black box model; the method specifically comprises the following steps:
s21, determining input and output of a department infection black box model; consulting a hospital infection control manual, specifications, external documents and expert opinions; determining an in-hospital infection risk index as an input quantity of a department infection black box model; determining an in-hospital infection result index as the output quantity of a department infection black box model;
s22, determining the kinds of relation functions of a department infection black box model;
for a target department of primary infection risk identification, determining intra-hospital infection risk indicators includes managing risk indicatorsMedical scienceTherapeutic risk index->And nursing risk index->The method comprises the steps of carrying out a first treatment on the surface of the Determining that the nosocomial infection outcome measure comprises outcome score +.>Constructing a multidimensional linear relation function>The method comprises the steps of carrying out a first treatment on the surface of the The method meets the following conditions:
；/>；
；/>；
wherein,、/>and->Management risk index->Medical risk index->And nursing risk index->Risk factors of (2); />For managing risk index->Is used for the value range of the (a),is a medical risk index->Is a value range of>Is a nursing risk index->Is a value range of (a);
s23, determining the investigation amount of a department infection black box model;
will be unknown to manage risk indicatorsMedical risk index->And nursing risk index->Risk coefficient of (2)、/>And->Determining the investigation quantity of a department infection black box model;
s3, analyzing a department infection black box model; the method specifically comprises the following steps:
s31, calling the past data; invoking the existing management risk index of the main infection risk identification target departmentA previous medical risk index>A nursing risk index>And the former result index ++>Constructing a mapping of past risk result data>；
Wherein the subscriptDenoted as +.>Grouping past risk result data;
s32, importing the existing data into a department infection black box model;
mapping past risk outcome data for which data is knownIntroducing risk factors->、And->Unknown multidimensional linear relation function->Completing data import;
s33, main component analysis; principal component analysis is carried out on the multidimensional linear relation function with the data imported to obtain each result indexDetermining the main infection risk of a target department and completing the analysis of a department infection black box model;
s4, determining a risk coefficient; determining the main infection risk of the target department according to the analysis result of the department infection black box model of the target department, and assigning a risk coefficient of the main infection risk to obtain a quantized linear risk result relation function;
s5, risk identification and analysis; and (3) based on the quantized linear risk result relation function, carrying out risk identification, analysis, evaluation and rectification on the nosocomial infection of each target department.
Further, the step S4 specifically includes:
s41, to risk coefficientAssigning a value; for any result index->Will manage the risk index->And outcome index->Respectively sorting according to the sequence from high to low of the analysis results of the main components, assigning risk coefficients, wherein the higher the main component sequence is, the risk coefficients are->The greater the assignment;
s42, substituting the assigned risk coefficient into a basic linear risk result relation function to obtain a quantized linear risk result relation function, wherein the quantized linear risk result relation function is a risk evaluation matrix.
Further, the risk evaluation matrix satisfies:；
；
；/>；
wherein,、/>and->The management risk evaluation matrix, the medical risk evaluation matrix and the nursing risk evaluation matrix are respectively.
Further, in step S41: management risk indexes are managed according to the sequence of the principal component analysis structure from high to lowRisk factors of->Assigned 4, 3, 2, 1 respectively.
Further, in step S41: according to the sequence of the main component analysis structure from high to low, the medical risk indexRisk factors of->Assigned 4, 3, 2, 1 respectively.
Further, in step S41: according to the sequence of the main component analysis structure from high to low, the medical risk indexRisk factors of->Assigned 3, 2 and 1 respectively.
Further, in step S41: management risk indexes are managed according to the sequence of the principal component analysis structure from high to lowRisk factors of->Assigned 1.4, 1.3, 1.2, 1.1, respectively.
Further, in step S41: according to the sequence of the main component analysis result from high to low, the medical risk indexRisk factors of->Assigned 1.4, 1.3, 1.2, 1.1, respectively.
Further, in step S41: according to the sequence of the main component analysis structure from high to low, the medical risk indexRisk factors of->Assigned 1.3, 1.2, 1.1 respectively.
The invention also provides a department master infection risk identification system applied to the hospital scene, which is used for executing the department master infection risk identification method applied to the hospital scene, and comprises the following steps:
a statistics module for statistically managing risk indexesMedical risk index->And nursing risk index->；
A past data unit for storing past management risk indexA previous medical risk index>A nursing risk index>And the former result index ++>；
A modeling unit for constructing to manage risk indexesMedical risk index->And nursing risk index->As an independent variable, result score ∈ ->Basic linear risk outcome relation function for dependent variables>；
An analysis unit for completing principal component analysis;
assignment unit for aiming at any result indexWill manage the risk index->And outcome index->Respectively sorting the risks according to the sequence from high to low of the principal component analysis resultsCoefficient assignment, the higher the principal component order, the risk coefficientThe greater the assignment; substituting the assigned risk coefficient into a basic linear risk result relation function to obtain a quantized linear risk result relation function;
and the identification analysis evaluation unit is used for identifying, evaluating and analyzing the hospital infection risk of each department based on the quantitative linear risk result relation function according to the data provided by the statistics module.
The invention has the following beneficial effects:
1. the invention carries out principal component analysis based on a matrix algorithm, and compared with the traditional single-result principal component analysis method, the application provides a multi-element evaluation system;
2. compared with the traditional multi-result principal component analysis method based on the deep learning training algorithm, the principal component analysis method based on the matrix algorithm provided by the invention has the advantages that an evaluation system based on a high-calculation-power computer is not needed, and the degree of dependence on hardware equipment and professional knowledge of personnel is reduced;
3. the technical scheme of the invention actually comprises two parts, wherein the first part is 'main factor identification', and the second part is 'evaluation and improvement'; the first part aims at the technical problem that the problem exists in the aspect of infection in the hospital of the department, but the problem is not clear in the aspect of infection in the hospital of different departments, and provides the technical means of establishing a black box model and quantitatively evaluating, and the adopted technical means comprise: constructing a risk result relation function, calling past data, and identifying the main infection risk of a target department; through the technical characteristics, the obtained technical effects are as follows: the quantitative and accurate determination of the main cause of the hospital infection of different departments makes targeted improvement to avoid the interference of the whole disc improvement on the business work of medical staff.
Drawings
FIG. 1 is a schematic overall flow chart of the present invention.
Detailed Description
Examples: the invention provides S1, determining a scene range; based on the specifications of the hospital infection control manual and the specifications, determining a hospital infection control area, and determining a target department for identifying the risk of main infection;
s2, establishing a department infection black box model; the method specifically comprises the following steps:
s21, determining input and output of a department infection black box model; consulting a hospital infection control manual, specifications, external documents and expert opinions; determining an in-hospital infection risk index as an input quantity of a department infection black box model; determining an in-hospital infection result index as the output quantity of a department infection black box model;
s22, determining the kinds of relation functions of a department infection black box model;
for a target department of primary infection risk identification, determining intra-hospital infection risk indicators includes managing risk indicatorsMedical risk index->And nursing risk index->The method comprises the steps of carrying out a first treatment on the surface of the Determining that the nosocomial infection outcome measure comprises outcome score +.>Constructing a multidimensional linear relation function
The method comprises the steps of carrying out a first treatment on the surface of the The method meets the following conditions: />；
；
；/>；
Wherein,、/>and->Management risk index->Medical risk index->And nursing risk index->Risk factors of (2); />For managing risk index->Is used for the value range of the (a),is a medical risk index->Is a value range of>Is a nursing risk index->Is a value range of (a);
s23, determining the investigation amount of a department infection black box model;
will be unknown to manage risk indicatorsMedical risk index->And nursing risk index->Risk coefficient of (2)、/>And->Determining the investigation quantity of a department infection black box model;
the risk index of nosocomial infection and the result index of nosocomial infection as input and output are shown in the following table:
TABLE 1 Risk index coefficient Table
；
Wherein MDRO is multi-drug resistant bacteria, VAP is ventilator-associated pneumonia, CLABSI is central catheter-associated blood flow infection, and CAUTI is catheter-associated urinary tract infection;、/>、/>and->The serial numbers are marked respectively and are natural numbers;
s3, analyzing a department infection black box model; the method specifically comprises the following steps:
s31, calling the past data; invoking the existing management risk index of the main infection risk identification target departmentA previous medical risk index>A nursing risk index>And the former result index ++>Constructing a mapping of past risk result data>；
Wherein the subscriptDenoted as +.>Grouping past risk result data;
s32, importing the existing data into a department infection black box model;
mapping past risk outcome data for which data is knownIntroducing risk factors->、And->Unknown multidimensional linear relation function->Completing data import;
s33, main component analysis; principal component analysis is carried out on the multidimensional linear relation function with the data imported to obtain each result indexDetermining the main infection risk of a target department and completing the analysis of a department infection black box model;
s4, determining a risk coefficient; determining the main infection risk of the target department according to the analysis result of the department infection black box model of the target department, and assigning a risk coefficient of the main infection risk to obtain a quantized linear risk result relation function;
s5, risk identification and analysis; and (3) based on the quantized linear risk result relation function, carrying out risk identification, analysis, evaluation and rectification on the nosocomial infection of each target department.
Further, the step S4 specifically includes:
s41, to risk coefficientAssigning a value; for any result index->Will manage the risk index->And a result indexRespectively sorting according to the sequence from high to low of the analysis results of the main components, assigning risk coefficients, wherein the higher the main component sequence is, the risk coefficients are->The greater the assignment;
s42, substituting the assigned risk coefficient into a basic linear risk result relation function to obtain a quantized linear risk result relation function, wherein the quantized linear risk result relation function is a risk evaluation matrix.
Further, the risk evaluation matrix satisfies:
；
；
；/>；
wherein,、/>and->The management risk evaluation matrix, the medical risk evaluation matrix and the nursing risk evaluation matrix are respectively.
Further, in step S41: management risk indexes are managed according to the sequence of the principal component analysis structure from high to lowRisk factors of->Assigned 4, 3, 2, 1 respectively.
Further, in step S41: according to the sequence of the main component analysis structure from high to low, the medical risk indexRisk factors of->Assigned 4, 3, 2, 1 respectively.
Further, in step S41: according to the sequence of the main component analysis structure from high to low, the medical risk indexRisk factors of->Assigned 3, 2 and 1 respectively.
Further, in step S41: management risk indexes are managed according to the sequence of the principal component analysis structure from high to lowRisk factors of->Assigned 1.4, 1.3, 1.2, 1.1, respectively.
Further, in step S41: according to the sequence of the main component analysis result from high to low, the medical risk indexRisk factors of->Assigned 1.4, 1.3, 1.2, 1.1, respectively.
Further, in step S4 1: according to the sequence of the main component analysis structure from high to low, the medical risk indexRisk factors of->Assigned 1.3, 1.2, 1.1 respectively.
The invention also provides a department master infection risk identification system applied to the hospital scene, which is used for executing the department master infection risk identification method applied to the hospital scene, and comprises the following steps:
a statistics module for statistically managing risk indexesMedical risk index->And nursing risk index->；
A past data unit for storing past management risk indexA previous medical risk index>A nursing risk index>And the former result index ++>；
A modeling unit for constructing to manage risk indexesMedical risk index->And nursing risk index->As an independent variable, result score ∈ ->Basic linear risk outcome relation function for dependent variables>；
An analysis unit for completing principal component analysis;
assignment unit for aiming at any result indexWill manage the risk index->And outcome index->Respectively according to principal componentsSorting the analysis results from high to low, assigning risk coefficients, wherein the higher the main component order is, the risk coefficients areThe greater the assignment; substituting the assigned risk coefficient into a basic linear risk result relation function to obtain a quantized linear risk result relation function;
and the identification analysis evaluation unit is used for identifying, evaluating and analyzing the hospital infection risk of each department based on the quantitative linear risk result relation function according to the data provided by the statistics module.
The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the above-described embodiments, and that the above-described embodiments and descriptions are only preferred embodiments of the present invention, and are not intended to limit the invention, and that various changes and modifications may be made therein without departing from the spirit and scope of the invention as claimed. The scope of the invention is defined by the appended claims and their equivalents.
Claims (10)
1. The department master infection risk identification method applied to the hospital scene is characterized by comprising the following steps of:
s1, determining a scene range; based on the specifications of the hospital infection control manual and the specifications, determining a hospital infection control area, and determining a target department for identifying the risk of main infection;
s2, establishing a department infection black box model; the method specifically comprises the following steps:
s21, determining input and output of a department infection black box model; consulting a hospital infection control manual, specifications, external documents and expert opinions; determining an in-hospital infection risk index as an input quantity of a department infection black box model; determining an in-hospital infection result index as the output quantity of a department infection black box model;
s22, determining the kinds of relation functions of a department infection black box model;
for the mainTarget departments for infection risk identification, and determining intra-hospital infection risk indexes comprises management risk indexesMedical risk index->And nursing risk index->The method comprises the steps of carrying out a first treatment on the surface of the Determining that the nosocomial infection outcome measure comprises outcome score +.>Constructing a multidimensional linear relation function>The method comprises the steps of carrying out a first treatment on the surface of the The method meets the following conditions:
；/>；
；/>；
wherein,、/>and->Management risk index->Medical risk index->And nursing risk index->Risk factors of (2); />For managing risk index->Is a value range of>Is a medical risk index->Is a value range of>Is a nursing risk index->Is a value range of (a);
s23, determining the investigation amount of a department infection black box model;
will be unknown to manage risk indicatorsMedical risk index->And nursing risk index->Risk factors of->、And->Determining the investigation quantity of a department infection black box model;
s3, analyzing a department infection black box model; the method specifically comprises the following steps:
s31, calling the past data; invoking the existing management risk index of the main infection risk identification target departmentA previous medical risk index>A nursing risk index>And the former result index ++>Constructing a mapping of past risk result data>；
Wherein the subscriptDenoted as +.>Grouping past risk result data;
s32, importing the existing data into a department infection black box model;
mapping past risk outcome data for which data is knownIntroducing risk factors/>、/>Andunknown multidimensional linear relation function->Completing data import;
s33, main component analysis; principal component analysis is carried out on the multidimensional linear relation function with the data imported to obtain each result indexDetermining the main infection risk of a target department and completing the analysis of a department infection black box model;
s4, determining a risk coefficient; determining the main infection risk of the target department according to the analysis result of the department infection black box model of the target department, and assigning a risk coefficient of the main infection risk to obtain a quantized linear risk result relation function;
s5, risk identification and analysis; and (3) based on the quantized linear risk result relation function, carrying out risk identification, analysis, evaluation and rectification on the nosocomial infection of each target department.
2. The method for identifying risk of infection of a department owner applied to a hospital scene according to claim 1, wherein step S4 specifically comprises:
s41, to risk coefficientAssigning a value; for any result index->Will manage the risk index->And outcome index->Respectively sorting according to the sequence from high to low of the analysis results of the main components, assigning risk coefficients, wherein the higher the main component sequence is, the risk coefficients are->The greater the assignment;
s42, substituting the assigned risk coefficient into a basic linear risk result relation function to obtain a quantized linear risk result relation function, wherein the quantized linear risk result relation function is a risk evaluation matrix.
3. The method for identifying risk of infection of a subject in a hospital setting according to claim 2, wherein the risk evaluation matrix satisfies:；/>；
；/>；
wherein,、/>and->The management risk evaluation matrix, the medical risk evaluation matrix and the nursing risk evaluation matrix are respectively.
4. The department master infection risk identification method applied to a hospital scene according to claim 3, wherein in step S41: management risk indexes are managed according to the sequence of the principal component analysis structure from high to lowRisk factors of->Assigned 4, 3, 2, 1 respectively.
5. The department master infection risk identification method applied to a hospital scene according to claim 3, wherein in step S41: according to the sequence of the main component analysis structure from high to low, the medical risk indexRisk factors of->Assigned 4, 3, 2, 1 respectively.
6. The department master infection risk identification method applied to a hospital scene according to claim 3, wherein in step S41: according to the sequence of the main component analysis structure from high to low, the medical risk indexRisk factors of->Assigned 3, 2 and 1 respectively.
7. The department master infection risk identification method applied to hospital scenes according to claim 3, wherein the method is characterized in thatIn step S41: management risk indexes are managed according to the sequence of the principal component analysis structure from high to lowRisk factors of->Assigned 1.4, 1.3, 1.2, 1.1, respectively.
8. The department master infection risk identification method applied to a hospital scene according to claim 3, wherein in step S41: according to the sequence of the main component analysis result from high to low, the medical risk indexRisk factors of->Assigned 1.4, 1.3, 1.2, 1.1, respectively.
9. The department master infection risk identification method applied to a hospital scene according to claim 3, wherein in step S41: according to the sequence of the main component analysis structure from high to low, the medical risk indexRisk factors of->Assigned 1.3, 1.2, 1.1 respectively.
10. A department master infection risk identification system applied to a hospital scene for performing the department master infection risk identification method applied to a hospital scene as claimed in any one of claims 1 to 9, comprising:
a statistics module for statistically managing risk indexesMedical risk index->And nursing risk index->；
A past data unit for storing past management risk indexA previous medical risk index>A nursing risk index>And the former result index ++>；
A modeling unit for constructing to manage risk indexesMedical risk index->And nursing risk index->As an independent variable, result score ∈ ->Basic linear risk outcome relation function for dependent variables>；
An analysis unit for completing principal component analysis;
assignment unit for aiming at any result indexWill manage the risk index->And outcome index->Respectively sorting according to the sequence from high to low of the analysis results of the main components, assigning risk coefficients, wherein the higher the main component sequence is, the risk coefficients are->The greater the assignment; substituting the assigned risk coefficient into a basic linear risk result relation function to obtain a quantized linear risk result relation function;
and the identification analysis evaluation unit is used for identifying, evaluating and analyzing the hospital infection risk of each department based on the quantitative linear risk result relation function according to the data provided by the statistics module.
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