CN110148440B - A medical information query method - Google Patents

Abstract

The embodiment of the invention relates to a medical information query method, which comprises the following steps: acquiring medical data; establishing a platform data warehouse; performing data extraction, conversion and loading on medical data in a platform data warehouse to obtain a business intelligent database; establishing a multi-dimensional data analysis model according to a first service dimension and a first index dimension of the medical platform, and establishing a multi-dimensional data mining model according to a second service dimension and a second index dimension of the medical platform; training, testing and evaluating the multidimensional data model for multiple times; receiving query condition information of medical information input by a user; analyzing the query condition to obtain keyword information; calling a corresponding data model according to the matched model information; calling corresponding medical data; inputting the medical data into a data model, and performing data analysis on the medical data by the data model so as to obtain an output result; and displaying the output result according to a preset expression form.

Description

A medical information query method

technical field

The invention relates to the field of data processing, in particular to a medical information query method.

Background technique

In recent years, with the continuous development of medical technology and the rapid growth of medical data, analysis and decision support based on medical big data have also become popular. Traditional hospital medical services are mainly supported and operated by various management systems such as HIS, LIS, and PACS. At the same time, these management systems can perform simple query and statistics on the hospital's medical service data. However, as the scale of the hospital continues to increase and the amount of medical data continues to increase, these simple business query and analysis can no longer meet the needs of the hospital for its own management and development.

Contents of the invention

The purpose of the present invention is to provide a kind of medical information inquiry method for the defective of prior art, can realize the multi-dimensional inquiry of medical data of user, realize analysis and prediction function, concretely realize the multi-dimensional analysis of different angles to medical data, help For health management and medical workers to analyze problems from different perspectives, and to find out new valuable information from existing data, such as prediction, description, clustering, classification, analysis of influencing factors, and analysis of related factors etc., to realize the multi-attribute comprehensive analysis and prediction of medical data.

In view of this, an embodiment of the present invention provides a medical information query method, including:

Obtain the medical data in the hospital information system database, laboratory information system database, image archiving and communication system database of multiple medical platforms respectively;

Obtain corresponding preset rules according to the type of database, perform data integration on corresponding medical data according to the preset rules, and establish a platform data warehouse according to the integrated medical data;

Perform data extraction, conversion and loading on the medical data in the platform data warehouse to obtain a business intelligence database;

Establishing a multi-dimensional data analysis model according to the first business dimension and the first index dimension of the medical platform, and establishing a multi-dimensional data mining model according to the second business dimension and the second index dimension of the medical platform;

Perform multiple training, testing and evaluation on the multi-dimensional data model; the data model includes the data analysis model and the data mining model;

Establishing an association relationship between the data model and model information, generating an analysis model list according to the model information of the data analysis model, generating a mining model list according to the model information of the data mining model, and storing it;

Receive the query condition information of the medical information input by the user;

Analyzing the query conditions to obtain keyword information; wherein the keyword information includes model type information, query item information and corresponding query range information;

Acquiring a corresponding model list according to the model type information;

Obtain matching model information from the acquired model list according to the query item information;

Call the corresponding data model according to the matching model information;

calling the corresponding medical data in the business intelligence database according to the query item information and the corresponding query range information;

inputting the medical data into the data model, and the data model performs data analysis on the medical data to obtain an output result;

The output result is displayed according to a preset expression form.

Preferably, the model information includes model name information, function description information, input items and output items.

Preferably, after obtaining the matching model information in the obtained model list according to the query item information, the method further includes:

generating a model selection list according to the matched model information;

receiving model information selected by the user according to the model selection list.

Preferably, after extracting, converting and loading the medical data in the platform data warehouse to obtain the business intelligence database, the method further includes:

The medical management decision-making, medical diagnosis and scientific research needs are analyzed to obtain the first business dimension, the first index dimension, the second business dimension, and the second index dimension.

Preferably, the multiple training, testing and evaluation of the multi-dimensional data model specifically includes:

Obtain corresponding medical data in the business intelligence database according to the first business dimension and the first index dimension;

performing multiple trainings, tests and evaluations on the data analysis model according to the medical data;

The multi-dimensional data mining model is trained, tested and evaluated multiple times.

Further preferably, the multiple training, testing and evaluation of the multi-dimensional data mining model specifically includes:

Setting input items and output items according to the data mining model; the input items include a first time period, and the output items include a second time period;

Obtain corresponding medical data from the business intelligence database according to the input item and the first time period;

Inputting the acquired medical data into the data mining model, the data mining model processes the medical data to obtain predicted output data; the predicted output data includes a confidence interval;

Obtaining corresponding medical data according to the output items and the second time period, and performing summary analysis to obtain actual output data;

Comparing the predicted output data with the actual output data to obtain error data;

The data mining model is evaluated according to multiple errors obtained from multiple training and testing.

Further preferably, the multiple training, testing and evaluation of the multi-dimensional data mining model specifically includes:

Set input items and output items according to the data mining model; the input items include patient information, inspection information, diagnosis information and medical expense information; the output items include a list of disease influencing factors, a disease analysis list, and a list of disease characteristics or a list of cost-influencing factors, which includes project information and corresponding probability information;

Obtain corresponding medical data from the business intelligence database according to the input items and the preset quantity;

input the acquired medical data into the data mining model, and the data mining model outputs the output data according to the output items after processing the medical data;

increasing the preset number, performing multiple training and testing on the data mining model to obtain multiple output data;

The plurality of output data are comparatively analyzed, and the data mining model is evaluated according to the analysis results.

Preferably, the method also includes:

updating the medical data in the hospital information system database, laboratory information system database and image archiving and communication system database of the medical platform;

The data analysis model and the data mining model are trained, tested and evaluated according to the updated medical data.

Preferably, the preset presentation form is one or more of a line chart, a bar chart, a fan chart, a scatter chart, a dashboard, a radar chart and a table.

A medical information query method provided by the embodiment of the present invention can realize multi-dimensional query of medical data by users, realize analysis and prediction functions, and specifically realize multi-dimensional analysis of medical data from different angles, which is helpful for health management and medical workers Analyze problems from different perspectives, and be able to find new valuable information from existing data, such as prediction, description, clustering, classification, analysis of influencing factors, analysis of related factors, etc., to realize the analysis of medical data Multi-attribute comprehensive analysis and prediction.

Description of drawings

Fig. 1 is a flowchart of a medical information query method provided by an embodiment of the present invention.

Detailed ways

The technical solutions of the present invention will be described in further detail below with reference to the accompanying drawings and embodiments.

The medical information query method provided by the embodiment of the present invention is applied to the medical platform system, and can realize the query and analysis of the medical information of the medical platform of multiple hospitals. Fig. 1 is a flow chart of a medical information query method provided by an embodiment of the present invention, as shown in Fig. 1 , including:

Step 101, obtaining the medical data in the hospital information system database, laboratory information system database, image archiving and communication system database of multiple medical platforms respectively;

The hospital information system database (Hospital Information System, HIS) is an information management system covering all the business and the whole process of the hospital. It uses computers and communication equipment to provide the departments of the hospital with the collection of patient diagnosis and treatment information and administrative management information. A platform capable of storing, processing, extracting and exchanging data and meeting the functional requirements of authorized users.

The Laboratory Information Management System (LIS) database generates the test barcode label of the corresponding patient through the test application submitted by the outpatient doctor and the inpatient workstation, and corresponds the basic information of the patient to the test instrument while generating the test sheet; After the test instrument generates the result, the system will automatically correspond the test data with the patient information through the data interface and result approval according to the corresponding relationship, so as to realize the network system of electronic test information and automatic test information management.

Communication system database (Picture Archiving and Communication Systems, PACS), the system applied in the imaging department of the hospital, the main task is to store all kinds of medical images (including MRI, CT, ultrasound, various X-ray machines, various infrared The images produced by equipment such as instruments, microscopes, etc.) are stored digitally in large quantities through various interfaces (analog, DICOM, network).

Specifically, the medical data in the hospital information system database, laboratory information system database, and image archiving and communication system database of each hospital are respectively obtained through the data interface of each hospital in the area. It should be noted that the medical data acquisition database includes But not limited to the above three databases of hospital information system database, laboratory information system database, image archiving and communication system database. Those skilled in the art can set the area and the number of hospitals according to needs.

Step 102, obtain corresponding preset rules according to the type of the database, perform data integration on the corresponding medical data according to the preset rules, and establish a platform data warehouse according to the integrated medical data;

Each type of database corresponds to different preset rules, which include requirements for data storage format, structure and type, etc., that is, for hospital information system databases, laboratory information system databases, image archiving and communication The medical data in the system database adopts corresponding preset rules for data integration, and a platform data warehouse is established based on the integrated medical data.

Step 103, extracting, converting and loading the medical data in the platform data warehouse to obtain a business intelligence database;

Data extraction refers to extracting the data required by the target data source system from the platform data warehouse; data conversion refers to converting the data obtained from the source data source into the form required by the target data source according to business requirements, and correcting errors and inconsistent data. Cleaning and processing; data loading refers to loading the transformed data to the target data source.

The purpose of this process is to be responsible for extracting data from distributed and heterogeneous data sources such as relational data and flat data files to the temporary intermediate layer for cleaning, conversion, integration, and finally loading to the data warehouse or data mart, that is, business In the intelligent database, it becomes the basis of online analytical processing and data mining.

After that, the medical management decision-making, medical diagnosis and scientific research needs are analyzed to obtain the first business dimension, the first index dimension, the second business dimension, and the second index dimension.

Wherein, the first business dimension and the first index dimension are used for data analysis, and the second business dimension and the second index dimension are used for data mining. The first business dimension and the second business dimension can be the same or different, and those skilled in the art can Set the first business dimension, the first index dimension, the second business dimension, and the second index dimension as required.

The first business dimension refers to the topic of decision analysis, which can specifically include medical quantity and quality operation management decisions, human resource decisions, financial decisions, material management decisions, service decisions, real-time clinical decisions, clinical pathways, and scientific research decisions. The first indicator dimension is Refers to the analysis indicators based on the first business dimension. For example, the analysis indicators corresponding to the decision-making of medical quantity and quality operation management can be the theme of hospital admission, surgery, and anesthesia; the analysis indicators of human resource decision-making can be the theme of performance, business information, and basic information. ; The analysis indicators of financial decision-making can be the theme of medical income and medical cost; the analysis indicators of material management decision-making can be the theme of medical consumables, medical equipment, and logistics supplies; the analysis indicators of service decision-making can be the theme of hospital admission prediction, average hospitalization Daily prediction topics; the analysis indicators of real-time clinical decision-making can be real-time clinical decision-making, clinical diagnosis and treatment topics, and medical quality control topics; the analysis indicators of clinical pathways can be clinical pathways, general indicator monitoring The key implementation path monitoring theme of diseases; the analysis indicators of scientific research decision-making can be the theme of evidence-based medicine and the theme of clinical scientific research.

The second business dimension can specifically include service decision-making, medical diagnosis and scientific research, and financial management. The second index refers to the analysis index based on the second business dimension. For example, the second index corresponding to the service decision-making class can be registration situation prediction, Prediction of visits, visits, diagnosis, discharge, discharge day, critical rate, invasive surgery, non-invasive surgery, bed utilization, bed turnover days, visits Time analysis; the second index corresponding to medical diagnosis and scientific research can be disease etiology analysis, disease prediction analysis, patient pattern recognition, concurrent disease association analysis, intelligent prescription recommendation, drug combination analysis, epidemic monitoring analysis, anesthesia analysis, drug Abnormal reaction analysis, disease evolution analysis; the second index of medical diagnosis and scientific research can be disease etiology analysis, disease prediction analysis, patient pattern recognition, concurrent disease association analysis, intelligent prescription recommendation, drug combination analysis, epidemic monitoring analysis, Anesthesia analysis, abnormal drug reaction analysis, and disease evolution analysis; the second indicator of financial management can be cost early warning analysis, department cost forecast analysis, project cost forecast analysis, disease cost forecast analysis, hospital-level cost forecast analysis, and department income forecast Analysis, project income forecast analysis, disease income forecast analysis, hospital-level income forecast analysis, medical insurance income forecast analysis, abnormal medical expense analysis, and hospitalization expense influencing factor analysis.

It should be noted that those skilled in the art can set the first business dimension, the first index dimension, the second business dimension, and the second index dimension as required.

Step 104, establishing a multidimensional data analysis model according to the first business dimension and the first index dimension of the medical platform, and establishing a multidimensional data mining model according to the second business dimension and the second index dimension of the medical platform;

Specifically, a dimensional data analysis model can be established for each first index dimension, so as to establish a multi-dimensional OLAP data analysis model based on the first business dimension and the first index dimension of the medical platform. The main function of OLAP data analysis is to analyze data Summary statistics and calculations are different from common static reports. Dynamic reports based on OLAP technology realize multi-angle data analysis and query, which is convenient for health management and medical workers and other medical data managers to query and analyze from different perspectives. question.

A dimensional data mining model can be established for each second index dimension, so as to establish a multi-dimensional data mining model based on the second business dimension and the second index dimension of the medical platform. The main function of the data mining model is to target specific analysis topics, using Advanced data mining and statistical techniques can find new and valuable information from existing data, such as prediction, description, clustering, classification, analysis of influencing factors, analysis of related factors, etc. Data mining provides a multi-attribute comprehensive analysis method.

Step 105, performing multiple training, testing and evaluation on the multi-dimensional data model;

The data model here includes two types of data models, the data analysis model and the data mining model. For the convenience of description, the data analysis model and the data mining model are collectively referred to as the data model.

After the data analysis model is established, it is necessary to use a large amount of data to train the multi-dimensional model, that is, obtain the corresponding medical data in the business intelligence database according to the first business dimension and the first index dimension; The analysis model is trained, tested and evaluated multiple times, and the model is continuously optimized according to the results of training, testing and evaluation, and then multiple optimized multi-dimensional data analysis models are obtained.

After the data mining model is established, it is necessary to use a large amount of data to conduct multiple training, testing and evaluation of the multi-dimensional data mining model.

Data mining models have set input items and output items. According to different input items and output items, different data mining models have different training and testing methods. The following two training, testing and evaluation methods are introduced in detail:

The first is to set input items and output items according to the data mining model; the input items include the first time period, and the output items include the second time period. In a specific example, according to the second index dimension corresponding to the service decision class of the second business dimension, the data mining model established for the registration situation prediction is the registration situation prediction model, and the function is to predict the registration situation of each department in the future Y period, then set The specified input item is the number of registered persons in each department in the historical X period, X is the first time period, the output item is the number of registered persons in each department in the future Y period, and Y is the second time period; according to the input items and the first time period in The corresponding medical data is obtained from the business intelligence database; the obtained medical data is input into the data mining model, and the data mining model processes the medical data to obtain prediction output data, and the prediction output data also includes a confidence interval.

After obtaining the predicted output data, obtain the corresponding medical data according to the output items and the second time period, and perform summary analysis to obtain the actual output data; compare the predicted output data with the actual output data to obtain error data; Multiple errors obtained from training and testing are used to evaluate and optimize the data mining model.

This method is suitable for service decision-making data mining models, which are shown in Table 1 below.

Table 1 Service decision-making data mining model

The second is to set input items and output items according to the data mining model; input items include patient information, examination information, diagnosis information and medical expense information; output items include a list of disease influencing factors, a list of disease analysis, a list of disease characteristics or A list of cost-influencing factors, including project information and corresponding probability information; obtain corresponding medical data from the business intelligence database according to the input project and preset quantity; input the obtained medical data into the data mining model, and the data mining model will After the medical data is processed, the output data is output according to the output items; the preset number is increased, and the data mining model is trained and tested multiple times to obtain multiple output data; multiple output data are compared and analyzed, and data mining is performed according to the analysis results. The model is evaluated. This method is suitable for medical diagnosis and scientific research data mining models, and the details of the medical diagnosis and scientific research data mining models are shown in Table 2 below.

Table 2 Data Mining Models for Medical Diagnosis and Scientific Research

In addition, for financial management data mining models, some use the first method, and some use the second method. The details of financial management data mining models are shown in Table 3.

Table 3 Data Mining Models for Medical Diagnosis and Scientific Research

Step 106, establishing the association relationship between the data model and the model information, generating an analysis model list according to the model information of the data analysis model, generating a mining model list according to the model information of the data mining model, and storing it;

Wherein, the model information includes model name information, function description information, input items and output items.

The data analysis model and the data mining model respectively generate their respective model lists, that is, generate the analysis model list according to the model information of the data analysis model, and generate the mining model list according to the model information of the data mining model, and store them.

Step 107, receiving the query condition information of the medical information input by the user, analyzing the query condition, and obtaining keyword information;

The users here can be municipal health bureaus and business management departments, municipal (district) health bureaus, community health service centers, managers of various hospitals, and different users have different operating authority. permissions are set.

The user can enter the query conditions of the medical information to be queried through the user terminal, and send it to the medical platform. The medical platform obtains the corresponding authority information of the user according to the user information, and checks whether the authority matches the query conditions. Perform analysis to obtain keyword information. Keyword information includes model type information, query item information, and corresponding query range information. The model type information here includes data analysis models and data mining models.

The query item information refers to the content to be queried, and the query range information refers to the range of the query content, which may be a time range, a region range, and the like. In a specific example, the query conditions entered by the user can be "statistics of all hospital equipment in region A" and "predict the number of registered patients of hospital A in X period in the future", then "statistics of all hospital equipment in region A" can be determined The data model corresponding to "is a data analysis model, and the query item is the statistics of hospital equipment, and the query range is all hospitals in area A; the data model corresponding to "forecasting the number of registered patients of hospital A in X period in the future" is a data mining model, and the query item is registration The number of people, the query scope is the future X period of Hospital A.

Step 108, obtain a corresponding model list according to the model type information, and obtain matching model information in the obtained model list according to the query item information;

Specifically, the corresponding model list is obtained according to the model type information, and the information corresponding to the model name, function description, input item and output item in the model list is matched according to the query item information, so as to obtain the matched model information.

Step 109, call the corresponding data model according to the matching model information;

When there is only one matched model, call the corresponding data model according to the matched model information; when there are multiple matched models, generate a model selection list according to the matched model information, and receive the user's selection list based on the model The selected model information calls the corresponding data model according to the model information selected by the user.

Step 110, invoking the corresponding medical data in the business intelligence database according to the query item information and the corresponding query range information;

Step 111, input medical data into the data model, and the data model performs data analysis on the medical data to obtain output results;

After using a large amount of data to train and optimize the model to obtain multiple multi-dimensional data analysis and mining models, regional managers can select data analysis and mining models as needed, and select the data analysis and mining to be analyzed and mined in the business intelligence database Scope, select medical data according to the data scope, and input the selected data analysis and mining model, so as to obtain the data output result.

Step 112, display the output result according to the preset expression form.

Multidimensional data analysis results and multidimensional data mining results can be displayed through one or more of trend line charts, bar charts, sector charts, scatter charts, dashboards, radar charts and tables, which are intuitive and easy to understand.

As time goes by, the medical platform will generate new medical data, thereby updating the medical data in the hospital information system database, laboratory information system database and image archiving and communication system database of the medical platform, and then according to the updated medical data. The data analysis model and data mining model are trained, tested and evaluated, thereby improving and optimizing the data analysis model and data mining model, and obtaining more accurate data.

A medical information query method provided by the embodiment of the present invention can realize multi-dimensional query of medical data by users, realize analysis and prediction functions, and specifically realize multi-dimensional analysis of medical data from different angles, which is helpful for health management and medical workers Analyze problems from different perspectives, and be able to find new valuable information from existing data, such as prediction, description, clustering, classification, analysis of influencing factors, analysis of related factors, etc., to realize the analysis of medical data Multi-attribute comprehensive analysis and prediction.

Professionals should further realize that the units and algorithm steps described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the relationship between hardware and software Interchangeability. In the above description, the composition and steps of each example have been generally described according to their functions. Whether these functions are executed by hardware or software depends on the specific application and design constraints of the technical solution. Skilled artisans may use different methods to implement the described functions for each specific application, but such implementation should not be regarded as exceeding the scope of the present invention.

The steps of the methods or algorithms described in connection with the embodiments disclosed herein may be implemented by hardware, software modules executed by a processor, or a combination of both. The software module can be placed in random access memory (RA medical information query method), internal memory, read-only memory (RO medical information query method), electrically programmable RO medical information query method, electrically erasable programmable RO medical information query method, register , hard disk, removable disk, CD-RO medical information query method, or any other form of storage medium known in the technical field.

The specific embodiments described above have further described the purpose, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above descriptions are only specific embodiments of the present invention and are not intended to limit the scope of the present invention. Protection scope, within the spirit and principles of the present invention, any modification, equivalent replacement, improvement, etc., shall be included in the protection scope of the present invention.

Claims (9)

1. A medical information query method, the method comprising:

respectively acquiring medical data in hospital information system databases, laboratory information system databases and image archiving and communication system databases of a plurality of medical platforms;

acquiring corresponding preset rules according to the types of the databases, integrating data of corresponding medical data according to the preset rules, and establishing a platform data warehouse according to the integrated medical data;

performing data extraction, conversion and loading on the medical data in the platform data warehouse to obtain a business intelligent database;

establishing a multi-dimensional data analysis model according to a first service dimension and a first index dimension of the medical platform, and establishing a multi-dimensional data mining model according to a second service dimension and a second index dimension of the medical platform;

training, testing and evaluating the multidimensional data model for multiple times; the data model comprises the data analysis model and the data mining model;

establishing an association relation between the data model and the model information, generating an analysis model list according to the model information of the data analysis model, generating an excavation model list according to the model information of the data excavation model, and storing the excavation model list;

receiving query condition information of medical information input by a user;

analyzing the query condition to obtain keyword information; the keyword information comprises model type information, query item information and corresponding query range information;

obtaining a corresponding model list according to the model type information;

obtaining matched model information from the obtained model list according to the query item information;

calling a corresponding data model according to the matched model information;

calling corresponding medical data in a business intelligent database according to the query item information and the corresponding query range information;

inputting the medical data into the data model, and performing data analysis on the medical data by the data model so as to obtain an output result;

and displaying the output result according to a preset expression form.

2. The medical information query method according to claim 1, wherein the model information includes model name information, function description information, input items, and output items.

3. The medical information query method of claim 1, wherein after the matching model information is acquired in the acquired model list according to the query term information, the method further comprises:

generating a model selection list according to the matched model information;

and receiving model information selected by the user according to the model selection list.

4. The medical information query method of claim 1, wherein after said data extraction, conversion and loading of medical data in said platform data warehouse to obtain a business intelligence database, said method further comprises:

and analyzing medical management decision, medical diagnosis and scientific research requirements to obtain the first service dimension, the first index dimension, the second service dimension and the second index dimension.

5. The medical information query method according to claim 1, wherein the training, testing and evaluating the multi-dimensional data model multiple times specifically comprises:

acquiring corresponding medical data from the business intelligent database according to the first business dimension and the first index dimension;

training, testing and evaluating the data analysis model for a plurality of times according to the medical data;

and training, testing and evaluating the multi-dimensional data mining model for multiple times.

6. The medical information query method of claim 5, wherein the performing multiple training, testing and evaluating the multi-dimensional data mining model specifically comprises:

setting input items and output items according to the data mining model; the input item includes a first time period and the output item includes a second time period;

acquiring corresponding medical data from the business intelligence database according to the input item and the first time period;

inputting the acquired medical data into the data mining model, and processing the medical data by the data mining model to obtain predicted output data; the predicted output data includes a confidence interval;

acquiring corresponding medical data according to the output items and the second time period, and performing total analysis to obtain actual output data;

comparing the predicted output data with the actual output data to obtain error data;

and evaluating the data mining model according to a plurality of errors obtained by multiple training and testing.

7. The medical information query method of claim 5, wherein the performing multiple training, testing and evaluating the multi-dimensional data mining model specifically comprises:

setting input items and output items according to the data mining model; the input items include patient information, examination information, diagnosis information, and medical fee information; the output items comprise a disease influence factor list, a disease analysis list, a disease characteristic list or a cost influence factor list, and the list comprises item information and corresponding probability information;

acquiring corresponding medical data from the business intelligent database according to the input items and the preset quantity;

inputting the acquired medical data into the data mining model, and outputting output data according to the output item after the data mining model processes the medical data;

the preset number is increased, and the data mining model is trained and tested for multiple times to obtain multiple output data;

and comparing and analyzing the plurality of output data, and evaluating the data mining model according to the analysis result.

8. The medical information query method of claim 1, wherein the method further comprises:

updating medical data in a hospital information system database, a laboratory information system database and an image archiving and communication system database of the medical platform;

and training, testing and evaluating the data analysis model and the data mining model according to the updated medical data.

9. The medical information query method of claim 1, wherein the preset representation is one or more of a line graph, a bar graph, a fan graph, a scatter graph, a dashboard, a radar graph, and a table.

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