Medical record data quality monitoring method and system based on knowledge base

Abstract

The invention relates to a knowledge base-based medical record data quality monitoring method and system, and the method comprises the steps: dividing medical record data into N medical record components, and carrying out the graph representation of the medical record components to obtain a medical record graph; assigning values to nodes in the medical record graph based on the knowledge base, wherein the values are called node vectors; and acquiring a medical record quality type corresponding to the medical record based on the node vector and the medical record graph. Quantitative calculation of the medical record quality type based on the graph is carried out under the support of the knowledge base, and the efficiency of medical record quality monitoring is greatly improved.

Description

Technical Field

[0001] The present invention belongs to the field of smart medical technology, and in particular relates to a medical record data quality monitoring method and system based on a knowledge base. Background Art

[0002] With the development of information technology, the medical and health industry has gradually realized informatization. As the core of the hospital information system (HIS), the electronic medical record organizes data "with patients as the center", provides electronic storage, query, statistics, and data exchange, and can timely integrate scattered patient information from all aspects into electronic data, and assist doctors in making diagnoses. Electronic medical records are the record carrier of diagnosis and treatment behavior. Therefore, by monitoring and quality control of electronic medical records, we can grasp the implementation and execution of the core system of medical quality and safety management. To monitor the electronic medical record in real time, it is necessary to connect with the medical record system first, and use the medical record big data as the basis, and use the medical quality and safety core system quality control rule library for analysis and quality control. Timely discover hidden dangers and defects to prevent the occurrence of medical quality and safety incidents. The construction scope of smart hospitals in my country mainly includes three major areas: "smart medical care", "smart services" and "smart management". Smart medical quality management is an important feature of realizing smart hospitals. However, hospitals currently generally have problems such as low standardization of medical data, unstructured expression of medical record data, and scattered medical quality control points. As a result, the massive data accumulated in existing HIS, electronic medical records, hand numbness, inspection and other information systems cannot play their due role and value in medical quality management. According to the existing diagnosis and treatment standards and medical record quality control requirements, the control of the quality of medical record content cannot be achieved by manual labor alone. Due to the large number of medical record quality control nodes, it is difficult for existing technologies to control every department, every doctor, and every medical record, and it is even more difficult to achieve full traceability. Therefore, the level of medical record information management and the efficiency and quality of medical record supervision in existing technologies are relatively low. Especially for traditional Chinese medical records, the means of medical data standardization are limited, and the means of monitoring the quality of medical record data by using knowledge bases are still limited to semantic analysis, and the usability of the monitoring results that can be provided is not strong. Due to the large workload of medical record quality control, the insufficient quality control level of quality control personnel, and the scarcity of highly educated full-time quality control personnel with clinical backgrounds, the quality control of medical records is not ideal. Moreover, traditional quality control is limited to final quality control and cannot monitor and remind the clinical diagnosis and treatment process in real time, resulting in medical record quality control being limited to form and lagging behind the medical process. Only by establishing and improving the medical record quality control system from the source can the fundamental problem of medical record quality be systematically solved.

[0003] Applying knowledge base and artificial intelligence technology to medical record quality data monitoring, analyzing complex medical data through algorithms and software to achieve the purpose of approximating human cognition, and discovering clinical trial data quality problems through automatic scanning technology, and conducting risk and quality control. It can also be used to build an intelligent screening system to improve the efficiency of clinical trial patient recruitment and data quality control. Using machine learning technology to build an early warning prediction model, the model predicts the trend of indicators in the future, and can warn the analysis indicators. In addition, it can also be used to analyze the causes of medical quality problems, and achieve all-round tracking of the improvement process and multi-dimensional analysis of the improvement effect. The large amount of medical data stored in the knowledge base can be used for multi-dimensional analysis and query of medical records, providing the basis and samples for monitoring and abnormal data cause analysis. Knowledge graph technology can use the knowledge points formed by the structured medical records to establish a knowledge graph, complete the deep semantic understanding of electronic medical record data, and help improve the accuracy and efficiency of medical record quality data monitoring. However, how to specifically apply these technologies in a comprehensive manner to make medical record quality data monitoring more efficient and accurate, and help improve the quality of medical services and patient safety, is a technical problem to be solved. In order to solve the problems in the prior art, the present invention performs quantitative calculation of medical record quality types based on graphs with the support of a knowledge base, thereby greatly improving the efficiency of medical record quality monitoring. Summary of the invention

[0004] In order to solve the above problems in the prior art, the present invention proposes a medical record data quality monitoring method and system based on a knowledge base, the method comprising:

[0005] Step S1: Divide the medical record data into N medical record components , ; Use medical record components as nodes to build a medical record graph ; For any two different components , if the component are independent of each other, then set ; If the component If they are related to each other, set ;

[0006] Step S2: assigning values ​​to nodes in the medical history graph based on the knowledge base, which is called a node vector; specifically, the steps include:

[0007] Step S21: Set a node vector for each node as an attribute representation; obtain an unprocessed node in the medical history graph and initialize the node vector of the node ,in: is the kth element in the node vector; k=1~K; k is the medical record quality type number, K is the total number of medical record quality types;

[0008] Step S22: Take the component corresponding to the unprocessed node as the current component, and compare it with the medical record sample in the knowledge base. If there is a medical record in the knowledge base that has another component of the same type as the current component, calculate the similarity between the current component and the other component; if the similarity between the two is greater than the similarity threshold, obtain the medical record quality type k of the medical record, and set the value of element k in the node vector ; Otherwise, continue processing the next medical record; repeat this step until all medical records have been processed;

[0009] Step S23: If there are still unprocessed nodes, return to step S21; otherwise, proceed to the next step;

[0010] Step S24: normalize the node vector; set the node vector ;

[0011] Step S3: based on the node vector and the medical record graph, obtain the medical record quality type corresponding to the medical record; specifically: propagate the node vector in the medical record graph to obtain the medical record quality type corresponding to the medical record; specifically includes the following steps:

[0012] Step S3A1: setting the quality type of the medical record to be processed to k, referred to as current type k;

[0013] Step S3A2: All nodes in the medical history graph are sorted according to the node vector corresponding to the current type k. The values ​​are arranged in ascending order to obtain the propagation queue;

[0014] Step S3A3: Get an unprocessed node from the propagation queue, called the first node ; Propagate the node vector with the first node as the center;

[0015] Step S3A4: If there is another node in the medical history graph, it is called the second node ; The length of the shortest path between the second node and the first node is ; Then update the node vector of the second node based on the following formula (1): Value; where: is the propagation attenuation coefficient;

[0016] (1);

[0017] Step S3A5: If there are still unprocessed nodes in the propagation queue, return to step S3A3; otherwise, proceed to the next step;

[0018] Step S3A6: If there is an unprocessed medical record quality type, set , return to step S3A1;

[0019] Step S3A7: Renormalize the node vector of each node and set the node vector ; Calculate graph vector ; Normalize the graph vector and set the graph vector ;Will The medical record quality type corresponding to the type k value is taken as the determined medical record quality type.

[0020] Further, each medical record component relates to a different portion of the medical record.

[0021] Furthermore, the initial values ​​of node vectors are all 0 before being assigned.

[0022] Furthermore, the similarity is semantic similarity.

[0023] Furthermore, the similarity threshold is a preset value.

[0024] Furthermore, the medical record quality types of the medical record samples in the knowledge base are updated according to the standardization of medical record writing, accuracy of diagnosis and treatment, completeness of medical record content, timeliness of medical records, legal compliance, data quality, quality of medical record archiving and / or TCM characteristics evaluation.

[0025] Furthermore, .

[0026] Furthermore, the medical records are traditional Chinese medicine or western medicine records.

[0027] A medical record data quality monitoring system based on a knowledge base, wherein the medical record data quality monitoring system based on a knowledge base is used to implement the above-mentioned medical record data quality monitoring method based on a knowledge base.

[0028] A medical record data quality monitoring platform based on a knowledge base, wherein the medical record data quality monitoring platform based on a knowledge base is used to implement the above-mentioned medical record data quality monitoring method based on a knowledge base.

[0029] A medical record data quality monitoring system based on a knowledge base, wherein the medical record data quality monitoring system based on a knowledge base is used to implement the above-mentioned medical record data quality monitoring method based on a knowledge base.

[0030] A medical record data quality monitoring device based on a knowledge base, the medical record data quality monitoring device based on a knowledge base is used to implement the above-mentioned medical record data quality monitoring method based on a knowledge base.

[0031] A medical record data quality monitoring module based on a knowledge base, wherein the medical record data quality monitoring module based on a knowledge base is used to implement the above-mentioned medical record data quality monitoring method based on a knowledge base.

[0032] A medical record data quality monitoring server based on a knowledge base, wherein the medical record data quality monitoring server based on a knowledge base is used to implement the above-mentioned medical record data quality monitoring method based on a knowledge base.

[0033] The beneficial effects of the present invention include:

[0034] (1) By transforming medical record data components into a graph representation of medical records, the content relevance of medical record data is mapped to the relevance of nodes in the graph. When assigning node values, the dimension of medical record quality type is expanded. The flow relationship between different components of medical record quality data is expressed by the propagation of node vectors in the medical record graph. Therefore, quantitative calculation of medical record quality types based on the graph can be performed with the support of the knowledge base, which greatly improves the efficiency of medical record quality monitoring.

[0035] (2) The node vector is updated based on the independent cascade model, which basically assumes that each node makes an independent decision when deciding whether to propagate information. This makes the model easy to implement and operate, and can effectively solve the reasoning and analysis of complex data relationships in medical record data. It provides a quantitative method for understanding and predicting the effect of medical record quality type propagation, and more scientifically uses the big data information in the knowledge base to guide the progress of medical record quality monitoring.

[0036] (3) By expressing the medical history graph and node vectors, the neural network model is connected. By crossing and mutating the node vectors based on the relationship between the nodes in the graph, the descent speed of parameter optimization can be greatly improved, thereby improving the stability and robustness of the model. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present application, but do not constitute an improper limitation of the present invention. In the drawings:

[0038] Figure 1 Schematic diagram of the medical record data quality monitoring method based on the knowledge base provided by the present invention. DETAILED DESCRIPTION

[0039] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments, wherein the illustrative embodiments and descriptions are only used to explain the present invention but are not intended to limit the present invention.

[0040] The present invention proposes a method and system for monitoring the quality of medical record data based on a knowledge base, as shown in the attached Figure 1 As shown, the method comprises the following steps:

[0041] Step S1: Divide the medical record data into N medical record components , ; Use medical record components as nodes to build a medical record graph ; For any two different components , if the component are independent of each other, then set ; If the component If they are related to each other, set ; You can know that, For Node and nodes The edge between

[0042] Preferably: each medical record component involves different parts of the medical record; the data content in the medical record component is related or unrelated; when there is a relationship between two medical record components, an edge is set between the two; of course, the number of edges in the medical record graph can also be reduced by using the strength of the relationship to reduce the complexity of the calculation; when the relationship between the two is weak, no corresponding edge is set;

[0043] Preferably: the medical record components are divided according to the organizational structure of the medical record data, the medical record file structure, and the correlation of the medical record data areas; after the division, a type is set for each component according to the division method;

[0044] Step S2: assigning values ​​to nodes in the medical history graph based on the knowledge base, which is called a node vector; specifically, the steps include:

[0045] Step S21: Set a node vector for each node as an attribute representation; obtain an unprocessed node in the medical history graph and initialize the node vector of the node ,in: is the kth element in the node vector; k=1~K; k is the medical record quality type number, K is the total number of medical record quality types, that is, the node vector is corresponded to the medical record quality type; that is, when assigning nodes, the dimension based on the medical record quality type is expanded;

[0046] Step S22: Take the component corresponding to the node as the current component, and compare it with the medical record sample in the knowledge base. If there is a medical record in the knowledge base that has another component of the same type as the current component, calculate the similarity between the current component and the other component; if the similarity between the two is greater than the similarity threshold, obtain the medical record quality type k of the medical record, and set the value of element k in the node vector ; Otherwise, continue processing the next medical record; repeat this step until all medical records have been processed; where: is the kth element in the node vector, indicating the attribute value of the node in medical record quality type k;

[0047] Preferably: the initial value of the node vector before assignment is 0;

[0048] Preferably, the comparing it with the medical record samples in the knowledge base is specifically: comparing it with the medical record samples of the same type summarized in the knowledge base; for example: for the same disease, belonging to the same department, using the same template, etc.;

[0049] Preferably: the similarity is semantic similarity; the similarity threshold is a preset value;

[0050] Step S23: If there are still unprocessed nodes, return to step S21; otherwise, proceed to the next step;

[0051] Step S24: normalize the node vector; set the node vector ;

[0052] Step S3: obtaining the medical record quality type corresponding to the medical record based on the node vector and the medical record graph; specifically: obtaining the medical record quality type corresponding to the medical record by propagating the node vector in the medical record graph; or obtaining the medical record quality type by a neural network model;

[0053] The step of propagating the node vector in the medical record graph to determine the medical record quality type corresponding to the medical record specifically includes the following steps:

[0054] Step S3A1: setting the quality type of the medical record to be processed to k, referred to as current type k;

[0055] Preferably: set the initial value of k to 1;

[0056] Step S3A2: All nodes in the medical history graph are sorted according to the node vector corresponding to the current type k. The values ​​are arranged in ascending order to obtain the propagation queue;

[0057] Step S3A3: Get an unprocessed node from the propagation queue, called the first node ; Propagate the node vector with the first node as the center;

[0058] Step S3A4: If there is another node in the medical history graph, it is called the second node ; The length of the shortest path between the second node and the first node is ; Update the node vector of the second node based on the following formula (1): Value; where: is the propagation attenuation coefficient;

[0059] (1);

[0060] Preferred: ;

[0061] Alternative: If the length is Then based on formula (1), update the node vector of the second node value, otherwise, no update is performed; is the longest path of propagation and is the default value;

[0062] Preferred: Setting ; It can be set according to the computational complexity and the size of the medical record;

[0063] Alternative: From length Starting from the second node corresponding to the largest shortest path to the second node corresponding to the smallest shortest path, the node vector of the second node is updated based on formula (1) value; the update mode adopted here is the iterative update mode; obviously there are no two identical nodes on the above shortest path; and each node is updated only once;

[0064] Step S3A5: If there are still unprocessed nodes in the propagation queue, return to step S3A3; otherwise, proceed to the next step;

[0065] Alternatively, the steps S3A2 to S3A5 are specifically to update the node vector based on the independent cascade model; specifically including the following steps:

[0066] Step S3A41: Determine the propagation probability between every two nodes ; Specifically: compare the first node (and its corresponding component) and the second node (and its corresponding component) with the medical record samples in the knowledge base respectively. If there is a medical record in the knowledge base that has another node (and its corresponding component) of the same type as the first node (and its corresponding component) or the second node (and its corresponding component), then calculate the similarity between the first node (and its corresponding component) or the second node (and its corresponding component) and the other node (and its corresponding component); if the similarity between the two is equal (or close), then set ; Otherwise, continue processing the next medical record sample; repeat this step until all medical records are processed; set ;in: is the count value; is the number of medical record samples;

[0067] Preferred: Set the count value The initial value of is 0;

[0068] Step S3A42: Obtain a set of two unprocessed nodes with a connection relationship from the medical history graph, respectively referred to as the first node and the second node ; Perform bidirectional propagation on the first node and the second node; repeat this step until all two nodes with connection relationship are processed;

[0069] The bidirectional transmission to the first node and the second node is specifically: If it is greater than or equal to the preset propagation probability, then set ,set up ;

[0070] Alternatively, the bidirectional propagation of the first node and the second node is specifically performed as follows: normalizing the propagation probability, and obtaining the normalized value of the propagation probability based on the following formula (2); updating the node vectors of the first node and the second node based on the following formulas (3) and (4);

[0071] (2);

[0072] (3);

[0073] (4);

[0074] Step S3A6: If there is an unprocessed medical record quality type, set , return to step S3A1;

[0075] Step S3A7: The medical record quality type corresponding to the type k value is taken as the determined medical record quality type; specifically: the node vector of each node is renormalized, and the node vector is set ; Calculate graph vector ; Normalize the graph vector and set the graph vector ;Will The medical record quality type corresponding to the type k value is taken as the determined medical record quality type;

[0076] Alternatively, the step S3A7 specifically includes the following steps:

[0077] Step S3A71: Calculate the information entropy of each medical record quality type ; Specifically, the information entropy is calculated using the following formulas (5) to (7): ;in: For the condition Execute under the condition Operation; is an intermediate variable;

[0078] (5);

[0079] (6);

[0080] (7);

[0081] Step S3A72: Renormalize the node vector of each node and set the node vector ; Calculate graph vector ; Normalize the graph vector and set the graph vector ;Will The medical record quality type corresponding to the k value is taken as the determined medical record quality type;

[0082] Preferably: the medical record quality type includes K types, wherein: the larger the K value, the better the medical record quality; the medical record quality type is obtained from the perspectives of medical record writing standardization, diagnosis and treatment accuracy, completeness of medical record content, timeliness of medical records, legal compliance, data quality, medical record filing quality, TCM characteristic evaluation, etc., and each medical record quality type reflects the comprehensive evaluation results of these perspectives;

[0083] Alternatively: the medical record quality type includes K types, wherein: the smaller the K value, the better the medical record quality;

[0084] Replaceable: The K types of medical record quality types correspond to the standardization of medical record writing, accuracy of diagnosis and treatment, completeness of medical record content, timeliness of medical record, legal compliance, data quality, quality of medical record filing, and evaluation of TCM characteristics. Each medical record quality type reflects the independent evaluation results of these angles. The graph vector Each element in Each indicates an independent evaluation result from an independent perspective;

[0085] Preferred: Data quality includes whether the data in the medical record is accurate, including diagnosis codes, surgical codes, medication use records, etc.;

[0086] The obtaining of the medical record quality type through the neural network model specifically comprises: constructing a neural network model as a medical record data quality prediction model, enriching the knowledge base based on a genetic algorithm, and inputting the node input vector corresponding to the medical record into the prediction model to obtain the medical record quality type of the medical record;

[0087] The specific steps include:

[0088] Step S3B1: Construct a prediction model based on BP neural network; construct samples based on medical records in the knowledge base; the input of the prediction model is the input vector composed of the node vectors of all nodes; the output of the prediction model is the quality type of medical records; the number of nodes in the input layer and hidden layer of the prediction model is ; The number of output nodes is K; that is, the first ( Nodes and Pathology Nodes corresponds to the kth element of ;

[0089] Preferred: The node vectors are concatenated in the order of the node numbers to form The input vector of the element;

[0090] Step S3B2: Determine the mutation probability and set the mutation probability of each node to ;

[0091] Preferred; Setting =-0.01~0.1;

[0092] Alternative: Set mutation probability ; Where: is the baseline mutation probability, Is a node degree; set the crossover probability to a preset value, for example: 0.5 to 0.9; initialize weights and biases;

[0093] Preferred: Randomly initialize the weights (W) and biases (b) of all connections in the network; for each sample, calculate the output value of each layer, and for each node in the hidden layer and the output layer, use the activation function sigmoid to calculate the output y=f(Wx+b); where: y is the activation function; x is the input value; since the number of nodes in the input layer is equal to the number of nodes in the hidden layer (convolutional layer), the corresponding activation function is set to ;

[0094] Preferably: the connection layer is a fully connected setting;

[0095] Step S3B3: Set the constraint condition as follows: when there is a connection edge between two nodes in the medical history graph, the two nodes can cross-mutate, otherwise, they cannot cross-mutate;

[0096] Step S3B4: Node , whose node vector is , perform mutation operation on its kth element to obtain the mutation node , whose node vector is ; Specifically: the node vector element value of the mutated node is calculated using the following formula (8); where: The node vector is , is the propagation coefficient, yes A random number between and , whose node vectors are , perform a crossover operation on its kth element to obtain a crossover node , whose node vector is ; Specifically: the node vector element value of the mutated node is calculated using the following formula (9); where: node The node vector is , yes A random number between

[0097] (8);

[0098] (9);

[0099] Step S3B5: Set the fitness function to mean square error function; repeat the above steps S3B1 to S3B4 until the fitness function is satisfied; at this point, the genetic algorithm has completed the optimization of the weights and bias parameters of the BP neural network; based on the connection relationship between nodes, the propagation is introduced into the node crossover and mutation process, which can greatly improve the descent speed of parameter optimization and quickly improve the stability and robustness of the model;

[0100] Step S3B6: Preprocess the node vector of the current medical record and set ; The node vectors are concatenated and input into the prediction model to obtain the medical record quality type corresponding to the current medical record;

[0101] Further: by randomly selecting medical records as current medical records and obtaining their corresponding medical record quality types, the quality of medical record data can be monitored;

[0102] Based on the same inventive concept, the present invention also provides a medical record data quality monitoring system based on a knowledge base, and the system is used to implement the above-mentioned medical record data quality monitoring method based on a knowledge base;

[0103] Based on the same inventive concept, the present invention also provides a medical record data quality monitoring server based on a knowledge base, and the system is used to implement the above-mentioned medical record data quality monitoring method based on a knowledge base;

[0104] Based on the same inventive concept, the present invention also provides a knowledge base-based medical record data quality monitoring client, the system is used to implement the above-mentioned knowledge base-based medical record data quality monitoring method;

[0105] A computer program (also referred to as a program, software, software application, script, or code) can be written in any form of a programming language, including an assembled or interpreted language, a declarative or procedural language, and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, object, or other unit suitable for use in a computing environment. A computer program may, but need not, correspond to a file in a file system. A program can be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program, or in multiple collaborative files (e.g., files storing one or more modules, subroutines, or code portions). A computer program can be deployed to execute on one computer or on multiple computers located at one site or distributed across multiple sites and interconnected by a communication network.

[0106] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0107] The present invention is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0108] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0109] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0110] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for monitoring the quality of medical record data based on a knowledge base, characterized in that: The method comprises: Step S1: Divide the medical record data into N medical record components , ; Use medical record components as nodes to build a medical record graph ; For any two different components , if the component are independent of each other, then set ; If the component If they are related to each other, set ; Step S2: Assign values ​​to nodes in the medical history graph based on the knowledge base to represent attributes, which is called node vector ; It is the kth element in the node vector, indicating the attribute value of the node in the medical record quality type k; k=1~K; k is the medical record quality type number, K is the total number of medical record quality types; Step S3: Propagate the node vector in the medical record graph to obtain the medical record quality type corresponding to the medical record; for any first node , if there is another node in the medical history graph, it is called the second node ; The length of the shortest path between the two is ; then set ;in: is the propagation attenuation coefficient; after the propagation is completed, The medical record quality type corresponding to the type k value is taken as the determined medical record quality type.

2. The method for monitoring medical record data quality based on a knowledge base according to claim 1, characterized in that: Each medical record component refers to a different part of the medical record.

3. The method for monitoring medical record data quality based on a knowledge base according to claim 2, characterized in that: The initial value of the node vector is 0 before it is assigned.

4. The method for monitoring medical record data quality based on a knowledge base according to claim 3, characterized in that: The data contents in different medical record components are either related or unrelated.

5. The method for monitoring medical record data quality based on a knowledge base according to claim 4, characterized in that: Divide the medical record components according to the organizational structure of the medical record data.

6. The method for monitoring medical record data quality based on a knowledge base according to claim 5, characterized in that: Update the medical record quality type of the medical record samples in the knowledge base based on the standardization of medical record writing, accuracy of diagnosis and treatment, completeness of medical record content, timeliness of medical records, legal compliance, data quality, quality of medical record archiving and / or evaluation of TCM characteristics.

7. The method for monitoring medical record data quality based on a knowledge base according to claim 6, characterized in that: 。 8. The method for monitoring medical record data quality based on a knowledge base according to claim 7, characterized in that: The medical records are traditional Chinese medicine or western medicine records.

9. A medical record data quality monitoring system based on a knowledge base, characterized in that: The medical record data quality monitoring system based on the knowledge base is used to implement the medical record data quality monitoring method based on the knowledge base described in any one of claims 1 to 8.

10. A medical record data quality monitoring platform based on a knowledge base, characterized in that: The medical record data quality monitoring platform based on the knowledge base is used to implement the medical record data quality monitoring method based on the knowledge base described in any one of claims 1 to 8.

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