Data visualization method and system based on rich text

Abstract

The invention relates to the technical field of data visualization, in particular to a data visualization method and system based on rich texts. The method comprises the following steps: acquiring medical rich text information, analyzing the medical rich text information, and determining visual friendly data; acquiring data receiving object information, analyzing the data receiving object information, and determining receiving object feature information; analyzing the visual friendly data according to the feature information of the receiving object, and determining visual data; carrying out interactive visual presentation on the visual data, and obtaining an interactive operation of the data receiving object on the visual data; and dynamically updating the visual data according to the interaction operation. According to the method and the device, different requirements of different data receiving objects on the visual data can be met, information disputes generated among different data objects are reduced, and positive influences are provided for orderly development of medical work.

Description

Technical Field

[0001] This application relates to the technical field of data visualization, and in particular, to a data visualization method and system based on rich text. Background Art

[0002] With the rapid development of information technology, the informatization process of various industries is accelerating, the application field of data visualization is constantly expanding, and data visualization technology can help users quickly understand the core meaning expressed by massive data, significantly improving the readability of data.

[0003] However, when existing data visualization methods are applied to the medical field, it is difficult to meet the different visualization data requirements brought about by the differences in data needs of different data recipients, often resulting in differences in the understanding of data among different data recipients, and further leading to information disputes among different data recipients, which has a negative impact on the orderly development of medical work. Summary of the Invention

[0004] This application provides a data visualization method and system based on rich text to solve the above technical problems.

[0005] In a first aspect, this application provides a data visualization method based on rich text, the method comprising:

[0006] Obtain medical rich text information, analyze the medical rich text information, and determine visualization-friendly data;

[0007] Obtain data recipient information, analyze the data recipient information, and determine recipient characteristic information;

[0008] According to the recipient characteristic information, analyze the visualization-friendly data, and determine visualization data;

[0009] Perform interactive visualization presentation on the visualization data, and obtain interactive operations of the data recipient on the visualization data;

[0010] According to the interactive operations, dynamically update the visualization data.

[0011] This application analyzes medical rich text information, formats and processes the medical rich text information to obtain visualization-friendly data that is convenient for data visualization processing. At the same time, by analyzing the data recipient information, recipient characteristic information reflecting the data recipient's needs for visualization data is obtained. Based on the recipient characteristic information, the visualization-friendly data is analyzed and screened to obtain visualization data that meets the visualization data needs of the data recipient, and the visualization data is presented interactively. According to the interactive operations of the data object on the visualization data, the visualization data is dynamically updated to meet the different needs of different data recipients for visualization data, reduce information disputes generated between different data objects, and have a positive impact on the orderly development of medical work.

[0012] Optionally, the analyzing the medical rich text information to determine visualization-friendly data includes:

[0013] Performing standardized denoising processing on the medical rich text information to determine clean rich text information;

[0014] Analyzing the clean rich text information to determine key entity information and entity relationship information;

[0015] Constructing structured information according to the key entity information and the entity relationship information;

[0016] Determining the structured information as the visualization-friendly data.

[0017] Through the above technical solution, performing standardized denoising processing on the medical rich text information to determine clean rich text information, extracting key entity information and entity relationship information from the clean rich text information, constructing structured information according to the key entity information and the entity relationship information, and determining the structured information as visualization-friendly data, reducing the subsequent processing cost of data visualization and enhancing the comprehensibility of visualization data.

[0018] Optionally, the analyzing the data recipient information to determine recipient characteristic information includes:

[0019] Determining the object type according to the data recipient information;

[0020] Determining the decision-making requirement level and the emotional requirement level according to the object type;

[0021] Based on the object type, analyzing the data recipient information to determine the understanding level information;

[0022] Analyzing the understanding level information and the decision-making requirement level to determine the object information requirement;

[0023] Analyze the understanding level information and the emotional need level, and determine the object view requirements;

[0024] Determine the object information requirements and the object view requirements as the receiving object characteristic information.

[0025] Through the above technical solution, according to the data receiving object information, determine the object type, and for different object types, determine different decision requirement levels and emotional need levels. And by analyzing the data receiving object information, determine the understanding level information reflecting the understanding degree of the data receiving object for medical data. By analyzing the understanding level information and the decision requirement level, determine the object information requirements. At the same time, by analyzing the understanding level information and the emotional need level, determine the object view requirements, and determine the object information requirements and the object view requirements as the receiving object characteristic information. On the basis of determining the object information requirements according to the decision requirements and the understanding level of the data receiving object, considering the special situation of medical data, incorporate the emotional needs of the data receiving object into the process of visualizing medical data, so that the subsequent visualized medical data can weigh the feelings of the data receiving object while transmitting the data.

[0026] Optionally, the object type includes patients and accompanying persons. The determination of the decision requirement level and the emotional need level according to the object type includes:

[0027] If the object type is the patient, obtain the medical treatment information of the patient;

[0028] According to the medical treatment information, determine the age information, medical history information and cognitive information of the patient;

[0029] Analyze the age information, the medical history information and the cognitive information, and determine the decision-making ability score of the patient;

[0030] According to the decision-making ability score, judge whether the patient has the decision-making ability;

[0031] If the patient has the decision-making ability, determine the decision requirement level according to the medical treatment information;

[0032] According to the decision-making ability score, determine the emotional need level;

[0033] If the patient does not have the decision-making ability, adjust the object type to the accompanying person;

[0034] If the object type is the accompanying person, obtain the basic information and relationship information of the accompanying person;

[0035] Analyze the basic information and the medical treatment information, and determine the decision requirement level;

[0036] Analyze the relationship information to determine the level of emotional needs.

[0037] Through the above technical solution, when the object type is a patient, based on the medical visit information, determine the patient's age information, medical history information, and cognitive information, and on the basis of the above information, evaluate the patient's decision-making ability. If the patient has decision-making ability, then according to the medical visit information and the decision-making ability score, determine the patient's decision-making need level and emotional need level respectively. If the patient does not have decision-making ability, then adjust the object type to the accompanying person, and according to the basic information of the accompanying person, the relationship information, and the patient's medical visit information, determine the decision-making need level and emotional need level of the accompanying person respectively, improving the accuracy and comprehensiveness of the evaluation of the patient's decision-making need level and emotional need level, and fully considering the impact of the patient's decision-making ability on the emotional need level and the conversion of the object type, improving the pertinence of the subsequent visualized medical data.

[0038] Optionally, the object type further includes medical staff. The determining of the decision-making need level and the emotional need level according to the object type includes:

[0039] If the object type is the medical staff, then determine the emotional need level according to the preset emotional needs of the medical staff;

[0040] Obtain the work information of the medical staff, determine the specific work responsibilities of the medical staff, and determine the decision-making need level according to the specific work responsibilities.

[0041] Through the above technical solution, when the object type is medical staff, by analyzing the emotional need characteristics of the medical staff, determine the emotional need level of the medical staff, and according to the work information of the medical staff, determine the specific work responsibilities of the medical staff, and determine the decision-making need level of the medical staff according to the specific work responsibilities, making the determination of the decision-making need level and the emotional need level more reasonable.

[0042] Optionally, the analyzing of the age information, the medical history information, and the cognitive information to determine the patient's decision-making ability score includes:

[0043] According to the medical history information, determine the treatment time span of the patient;

[0044] According to the cognitive information, determine the cognitive ability score of the patient;

[0045] According to the age information, the treatment time span, and the cognitive ability score, refer to the following formula to determine the decision-making ability score:

[0046]

[0047] where D is the decision-making ability score, w Ais the preset age relationship coefficient, S A is the age information, k A is the preset age influence index, a is the preset optimal decision-making age, w M is the preset treatment time relationship coefficient, k M is the preset treatment time influence coefficient, S M is the treatment time span, w C is the preset cognitive relationship coefficient, S C is the cognitive ability score;

[0048] Determining whether the patient has decision-making ability according to the decision-making ability score includes:

[0049] Comparing the decision-making ability score with a preset decision-making ability interval. If the decision-making ability score is within the preset decision-making ability interval, it is determined that the patient has decision-making ability; otherwise, it is determined that the patient does not have decision-making ability.

[0050] Through the above technical solution, by using mathematical analysis means, on the basis of the patient's age information, treatment time span and cognitive ability score, a mathematical formula is designed to quantitatively calculate the patient's decision-making ability score. By comparing the decision-making ability score with a preset decision-making ability interval, it is judged whether the patient has decision-making ability in the current state, making the judgment of the patient's decision-making ability more accurate and comprehensive.

[0051] Optionally, determining the decision-making requirement level according to the medical treatment information includes:

[0052] Evaluating the urgency of the patient's condition, the availability of the support system and the information complexity according to the medical treatment information;

[0053] According to the urgency of the condition, the availability of the support system and the information complexity, referring to the following formula, determine the decision-making requirement score:

[0054] M = α·S + β·(1 - C) + γ·P;

[0055] Wherein, M is the decision-making requirement score, α is the preset urgency influence weight, S is the urgency of the condition, β is the preset support influence weight, C is the availability of the support system, γ is the preset complexity influence weight, and P is the information complexity;

[0056] Determine the decision-making requirement level according to the decision-making requirement score.

[0057] Through the above technical solution, by means of mathematical analysis, a mathematical formula is designed based on the urgency of the patient's condition, the availability of the support system, and the information complexity to quantify the patient's decision-making need score. The patient's decision-making need level is reflected through the decision-making need score, making the analysis of the patient's decision-making need level more accurate and comprehensive.

[0058] Optionally, determining the emotional need level according to the decision-making ability score includes:

[0059] According to the decision-making ability score, referring to the following formula, determine the emotional need score of the patient:

[0060]

[0061] where E is the emotional need score, a is a preset proportional constant, D is the decision-making ability score, and b is a preset offset;

[0062] Determine the emotional need level according to the emotional need score.

[0063] Through the above technical solution, by means of mathematical analysis, a mathematical formula is designed based on the patient's decision-making ability score to quantify the patient's emotional need score. The patient's emotional need level is reflected through the emotional need score, making the analysis of the patient's emotional need level more accurate and comprehensive.

[0064] Optionally, analyzing the visualization-friendly data and determining the visualization data according to the received object characteristic information includes:

[0065] Filter the visualization-friendly data according to the object information requirement to determine the highly matched friendly data;

[0066] Retrieve the preset visualization view data set according to the object view requirement, and determine the highly matched view according to the retrieval result;

[0067] Perform a reading-friendly combination of the highly matched friendly data and the highly matched view to construct the visualization data.

[0068] Through the above technical solution, starting from two directions of the object information requirement and the object view requirement simultaneously, analyze the visualization-friendly data and the preset visualization view data set to respectively obtain the highly matched friendly data and the highly matched view. By performing a reading-friendly combination of the highly matched friendly data and the highly matched view to construct the visualization data, while highly meeting the object information requirement and the object view requirement, enhance the reading friendliness of the visualization data and improve the transmission effect of the visualization data.

[0069] Second aspect, the present application provides a data visualization system based on rich text, and the system includes:

[0070] A rich text analysis module, configured to obtain medical rich text information, analyze the medical rich text information, and determine visualization-friendly data;

[0071] A feature information extraction module, configured to obtain data recipient information, analyze the data recipient information, and determine recipient feature information;

[0072] A data analysis module, configured to analyze the visualization-friendly data according to the recipient feature information, and determine visualization data;

[0073] A visualization module, configured to perform interactive visualization presentation on the visualization data, and obtain interactive operations of the data recipient on the visualization data;

[0074] An update module, configured to dynamically update the visualization data according to the interactive operations.

[0075] Optionally, the rich text analysis module is specifically configured to:

[0076] Perform standardized denoising processing on the medical rich text information to determine clean rich text information;

[0077] Analyze the clean rich text information to determine key entity information and entity relationship information;

[0078] Construct structured information according to the key entity information and the entity relationship information;

[0079] Determine the structured information as the visualization-friendly data.

[0080] Optionally, the feature information extraction module is specifically configured to:

[0081] Determine the object type according to the data recipient information;

[0082] Determine the decision requirement level and the emotional requirement level according to the object type;

[0083] Based on the object type, analyze the data recipient information to determine the understanding level information;

[0084] Analyze the understanding level information and the decision requirement level to determine the object information requirement;

[0085] Analyze the understanding level information and the emotional requirement level to determine the object view requirement;

[0086] Determine the object information requirement and the object view requirement as the recipient feature information.

[0087] Optionally, when determining the decision requirement level and the emotional requirement level according to the object type, the feature information extraction module is specifically configured to:

[0088] If the object type is the patient, obtain the medical treatment information of the patient;

[0089] According to the medical treatment information, determine the age information, medical history information, and cognitive information of the patient;

[0090] Analyze the age information, the medical history information, and the cognitive information to determine the decision-making ability score of the patient;

[0091] According to the decision-making ability score, determine whether the patient has the decision-making ability;

[0092] If the patient has the decision-making ability, determine the decision requirement level according to the medical treatment information;

[0093] Determine the emotional requirement level according to the decision-making ability score;

[0094] If the patient does not have the decision-making ability, adjust the object type to the accompanying person;

[0095] If the object type is the accompanying person, obtain the basic information and relationship information of the accompanying person;

[0096] Analyze the basic information and the medical treatment information to determine the decision requirement level;

[0097] Analyze the relationship information to determine the emotional requirement level.

[0098] Optionally, when determining the decision requirement level and the emotional requirement level according to the object type, the feature information extraction module is specifically configured to:

[0099] If the object type is the medical staff, determine the emotional requirement level according to the preset emotional requirements of the medical staff;

[0100] Obtain the work information of the medical staff, determine the specific work responsibilities of the medical staff, and determine the decision requirement level according to the specific work responsibilities.

[0101] Optionally, when analyzing the age information, the medical history information, and the cognitive information to determine the decision-making ability score of the patient, the feature information extraction module is specifically configured to:

[0102] According to the medical history information, determine the treatment time span of the patient;

[0103] Determine the cognitive ability score of the patient according to the cognitive information;

[0104] According to the age information, treatment time span, and cognitive ability score, refer to the following formula to determine the decision-making ability score:

[0105]

[0106] where D is the decision-making ability score, w A is the preset age relationship coefficient, S A is the age information, k A is the preset age influence index, a is the preset optimal decision-making age, w M is the preset treatment time relationship coefficient, k M is the preset treatment time influence coefficient, S M is the treatment time span, w C is the preset cognitive relationship coefficient, S C is the cognitive ability score;

[0107] The judgment of whether the patient has decision-making ability according to the decision-making ability score includes:

[0108] Compare the decision-making ability score with the preset decision-making ability interval. If the decision-making ability score is within the preset decision-making ability interval, it is determined that the patient has decision-making ability; otherwise, it is determined that the patient does not have decision-making ability.

[0109] Optionally, when determining the decision-making requirement level according to the medical visit information, the feature information extraction module is specifically used for:

[0110] Evaluate the disease urgency, support system availability, and information complexity of the patient according to the medical visit information;

[0111] According to the disease urgency, the support system availability, and the information complexity, refer to the following formula to determine the decision-making requirement score:

[0112] M = α·S + β·(1 - C) + γ·P;

[0113] where M is the decision-making requirement score, α is the preset urgency influence weight, S is the disease urgency, β is the preset support influence weight, C is the support system availability, γ is the preset complexity influence weight, and P is the information complexity;

[0114] Determine the decision-making requirement level according to the decision-making requirement score.

[0115] Optionally, when determining the emotional requirement level according to the decision-making ability score, the feature information extraction module is specifically used for:

[0116] According to the decision-making ability score, with reference to the following formula, determine the emotional need score of the patient:

[0117]

[0118] Where E is the emotional need score, a is a preset proportional constant, D is the decision-making ability score, and b is a preset offset;

[0119] Determine the emotional need level according to the emotional need score.

[0120] Optionally, the data analysis module is specifically configured to:

[0121] Filter the visualization-friendly data according to the object information requirement to determine the high-matching friendly data;

[0122] Retrieve a preset visualization view data set according to the object view requirement, and determine a high-matching view according to the retrieval result;

[0123] Perform a reading-friendly combination of the high-matching friendly data and the high-matching view to construct the visualization data. Description of the Drawings

[0124] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained according to these drawings without creative efforts.

[0125] Figure 1 It is a schematic diagram of an application scenario provided by an embodiment of the present application;

[0126] Figure 2 It is a flowchart of a method for visualizing data based on rich text provided by an embodiment of the present application;

[0127] Figure 3 It is a schematic structural diagram of a system for visualizing data based on rich text provided by an embodiment of the present application. Detailed Embodiments

[0128] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of this application. Apparently, the described embodiments are some but not all of the embodiments of this application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of this application without creative efforts shall fall within the scope of protection of this application.

[0129] In addition, the term "and / or" in this document is merely a description of the association relationship between associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this document generally represents an "or" relationship between the associated objects before and after, unless otherwise specified.

[0130] The embodiments of this application will be further described in detail below with reference to the accompanying drawings of the specification.

[0131] When existing data visualization methods are applied to the medical field, it is difficult to meet the different visualization data requirements brought about by the differences in data requirements of different data recipients, often resulting in differences in the understanding of data among different data recipients, and further leading to information disputes among different data recipients, which has a negative impact on the orderly development of medical work.

[0132] Based on this, this application provides a data visualization method and system based on rich text, analyzes medical rich text information, formats and processes the medical rich text information to obtain visualization-friendly data that is convenient for data visualization processing. At the same time, by analyzing the information of data recipients, the recipient characteristic information reflecting the data recipients' visualization data requirements is obtained. Based on the recipient characteristic information, the visualization-friendly data is analyzed and screened to obtain visualization data that meets the visualization data requirements of data recipients, and the visualization data is presented interactively. According to the interactive operations of data objects on the visualization data, the visualization data is dynamically updated to meet the different requirements of different data recipients for visualization data, reduce information disputes generated among different data objects, and provide a positive impact on the orderly development of medical work.

[0133] Figure 1 This is a schematic diagram of an application scenario provided by this application. In the process of visualizing medical data, the method provided by this application is applied to analyze medical rich text information and data recipient information, provide the corresponding visualization data to the data recipients, and update the visualization data according to the interactive operations of the data recipients.

[0134] Specifically, the method provided in this application is applied to a server that communicates with a medical system. The server obtains and analyzes the medical rich text information provided by the medical system, formats and processes the medical rich text information to obtain visualization-friendly data that is convenient for data visualization processing. At the same time, by obtaining and analyzing the data recipient information provided by the medical system, recipient characteristic information reflecting the visualization data requirements of the data recipient is obtained. Based on the recipient characteristic information, the visualization-friendly data is analyzed and screened to obtain visualization data that meets the visualization data requirements of the data recipient, and the visualization data is presented interactively. According to the interactive operations of the data object on the visualization data, the visualization data is dynamically updated to meet the different needs of different data recipients for visualization data, reduce information disputes generated between different data objects, and have a positive impact on the orderly development of medical work.

[0135] For specific implementation methods, reference can be made to the following embodiments.

[0136] Figure 2 The following is a flowchart of a rich text-based data visualization method provided by an embodiment of this application. The method of this embodiment can be applied to the server in the above scenario. As Figure 2 shown, the method includes:

[0137] S201. Obtain medical rich text information, analyze the medical rich text information, and determine visualization-friendly data.

[0138] The medical rich text information can be various medical information related to patients presented in rich text format. The medical rich text information not only contains basic text content but also contains various formatting elements. The medical rich text information can be provided by the medical system.

[0139] The visualization-friendly data can be data suitable for visualization display obtained after formatting the medical rich text information.

[0140] Specifically, with the development of the medical informatization process, rich text format is usually used in modern medical systems to record various medical information, such as electronic medical records. Doctors can describe the condition and treatment process in detail in the electronic medical records in various forms such as text, images, tables, and annotations. Since the medical rich text information contains various different types of data, it is difficult to ensure the presentation effect of the visualization data if direct visualization operations are performed on the basis of the medical rich text information. Therefore, during the visualization of the medical rich text information, it is necessary to first perform formatting preprocessing on the medical rich text information to obtain visualization-friendly data that is convenient for visualization.

[0141] S202. Obtain data recipient information, analyze the data recipient information, and determine recipient characteristic information.

[0142] The data receiving object information can be a series of information related to the medical visualization data display object, and the data receiving object information can be provided by the medical system.

[0143] The receiving object characteristic information can be relevant information that can reflect the specific requirements of the data receiving object for the medical visualization data.

[0144] Specifically, the object types targeted by medical visualization data are complex, such as doctors, patients, and family members. Different types of data receiving objects have different requirements for medical visualization data. For example, doctors need to conduct a global analysis of the patient's overall medical treatment information to accurately judge the patient's condition, while patients do not need and it is difficult to comprehensively understand the medical professional data involved in their own medical treatment process. By analyzing the data receiving object information, the differentiated requirements of different types of data receiving objects for medical information are obtained, and the receiving object information is obtained to facilitate subsequent data visualization for different types of data receiving objects.

[0145] S203. Analyze the visualization-friendly data according to the receiving object characteristic information to determine the visualization data.

[0146] The visualization data can be the data in the visualization-friendly data that highly conforms to the medical information requirements reflected by the current receiving object characteristic information.

[0147] Specifically, after obtaining the receiving object characteristic information, according to the medical information requirements of the current data receiving object reflected in the receiving object characteristic information, the visualization-friendly data is screened, and the data in the visualization-friendly data that highly matches the above medical information requirements is extracted to determine the visualization data, providing a data basis for subsequent accurate visualization data presentation.

[0148] S204. Perform interactive visualization presentation on the visualization data and obtain the interactive operations of the data receiving object on the visualization data.

[0149] The interactive visualization presentation can be considered as a process of displaying the visualization data in a graphical and interactive form.

[0150] The interactive operation can be an operation performed by the data receiving object on the cross-section of the visualization data, such as clicking, zooming, etc.

[0151] Specifically, after determining the visualization data corresponding to different types of data receiving objects, through data visualization technologies such as D3.js (Data-Driven Documents), according to the visualization data, perform interactive visualization presentation on the visualization data, and then use the event listening mechanism in the data visualization technology to obtain the interactive operations of the data receiving object on the visualization data.

[0152] S205. Dynamically update the visualization data according to the interactive operation.

[0153] Specifically, after obtaining the interactive operation through the event listening mechanism in the data visualization technology, the event handling function corresponding to the interactive operation is executed through the event handling mechanism in the data visualization technology to dynamically update the part of the visualization data corresponding to the interactive operation.

[0154] Through the method provided in this embodiment, the medical rich text information is analyzed, formatted and processed to obtain visualization-friendly data that is convenient for data visualization processing. At the same time, by analyzing the data recipient information, the recipient characteristic information reflecting the data recipient's needs for the visualization data is obtained. Based on the recipient characteristic information, the visualization-friendly data is analyzed and screened to obtain the visualization data that meets the visualization data needs of the data recipient, and the visualization data is presented interactively. According to the interactive operation of the data object on the visualization data, the visualization data is dynamically updated to meet the different needs of different data recipients for the visualization data, reduce the information disputes generated between different data objects, and have a positive impact on the orderly development of medical work.

[0155] In some embodiments, perform standardized denoising processing on the medical rich text information to determine the clean rich text information; analyze the clean rich text information to determine the key entity information and entity relationship information; construct structured information according to the key entity information and entity relationship information; and determine the structured information as the visualization-friendly data.

[0156] The standardized denoising processing can be considered as a process of removing the redundant formats in the medical rich text information that are meaningless to the data visualization process.

[0157] The clean text information can be the information containing the main content of subsequent data visualization obtained after performing standardized denoising processing on the medical rich text information.

[0158] The key entity information can be the main key information that needs to be displayed in the subsequent data visualization process in the medical rich text information.

[0159] The entity relationship information can be other secondary information related to and pointed to by the key entity information.

[0160] The structured information can be the specific format information with clear attributes and relationships.

[0161] Specifically, through a text cleaning tool, such as BeautifulSoup, the rich text information is subjected to standardized denoising processing to remove redundant tags and elements in the rich text information, obtaining clean rich text information. Then, through natural language processing tools, such as the named entity recognition technology and dependency syntax analysis technology in LTP (Language Technology Platform), the clean text is processed to obtain key entity information and entity key information. Furthermore, through a data processing tool, such as OpenRefine, the clean text is structured, thereby constructing structured information and determining the structured information as visualization-friendly data. The structured information is easy to integrate with other data visualization tools, which helps improve the subsequent data visualization efficiency.

[0162] Through the method provided in this embodiment, the medical rich text information is subjected to standardized denoising processing to determine clean rich text information, and key entity information and entity relationship information are extracted from the clean rich text information. According to the key entity information and entity relationship information, structured information is constructed, and the structured information is determined as visualization-friendly data, reducing the processing cost of subsequent data visualization and enhancing the comprehensibility of the visualized data.

[0163] In some embodiments, according to the data receiving object information, the object type is determined; according to the object type, the decision-making requirement level and the emotional requirement level are determined; based on the object type, the data receiving object information is analyzed to determine the understanding level information; the understanding level information and the decision-making requirement level are analyzed to determine the object information requirement; the understanding level information and the emotional requirement level are analyzed to determine the object view requirement; the object information requirement and the object view requirement are determined as the receiving object characteristic information.

[0164] The object type can be the specific type of the data receiving object, such as patients, family members, medical staff, etc.

[0165] The decision-making requirement level can be the requirement level for the current data receiving object to make medical decisions based on medical data.

[0166] The emotional requirement level can be the requirement level in terms of emotion when the current data receiving object faces medical data.

[0167] The understanding level information can be the information reflecting the understanding degree of the current data receiving object for medical data.

[0168] The object information requirement can be the information reflecting the visualized data required by the current data receiving object.

[0169] The object view requirement can be the information reflecting the visualized view required by the current data receiving object.

[0170] Specifically, according to the information of the data receiving object, determine the object type corresponding to the current data receiving object. Since different types of data receiving objects have different decision-making levels when receiving visual medical data. For example, doctors need detailed and accurate medical data to make the best clinical decisions, while patients need easy-to-understand and intuitive visual information to understand their conditions in order to make decisions on treatment plans. Also, different types of data receiving objects have different emotional need levels when receiving visual medical data. For example, doctors need to maintain an appropriate distance from patients emotionally to maintain professionalism and avoid excessive emotional burden, while patients often feel fear, anxiety and helplessness when facing diseases and need more emotional support to relieve these negative emotions. Therefore, according to different object types, determine their corresponding decision-making need levels and emotional need levels.

[0171] Furthermore, divide the visual medical data into two parts: information and view. Among them, the information is the medical data that needs to be directly displayed, and the view is the visual element that helps the data receiving object understand the medical data. By combining the information and the view, the transmission effect of the visual medical data can be enhanced. And the degree of understanding of the visual medical data by the data receiving object is directly affected by the understanding level of the data receiving object. For example, doctors have a large amount of professional medical knowledge reserves, and their understanding level of medical data is significantly higher than that of ordinary patients. Therefore, in visual medical data, the professionalism and detail of the information received by doctors should be higher than that of ordinary patients. To help the data receiving object better understand the visual medical data, during the process of determining the object information needs and object view needs, the understanding level information corresponding to the data receiving object needs to be taken into consideration. At the same time, the object information needs are directly affected by the decision-making need levels. Different decision-making need levels require different information. And the object view needs are directly affected by the emotional need levels. When the emotional need level is high, the corresponding view needs need to meet the emotional needs of the data receiving object.

[0172] Through the method provided in this embodiment, according to the data receiving object information, the object type is determined. For different object types, different decision requirement levels and emotional requirement levels are determined. By analyzing the data receiving object information, the understanding level information reflecting the understanding degree of the data receiving object for medical data is determined. By analyzing the understanding level information and the decision requirement level, the object information requirement is determined. At the same time, by analyzing the understanding level information and the emotional requirement level, the object view requirement is determined. And the object information requirement and the object view requirement are determined as the receiving object characteristic information. On the basis of determining the object information requirement according to the decision requirement and the understanding level of the data receiving object, considering the special situation of medical data, the emotional requirement of the data receiving object is incorporated into the process of visualizing medical data, so that the subsequent visualized medical data can balance the feelings of the data receiving object while transmitting the data.

[0173] In some embodiments, if the object type is a patient, the medical record information of the patient is obtained; according to the medical record information, the age information, medical history information and cognitive information of the patient are determined; the age information, medical history information and cognitive information are analyzed to determine the decision-making ability score of the patient; according to the decision-making ability score, it is judged whether the patient has the decision-making ability; if the patient has the decision-making ability, according to the medical record information, the decision requirement level is determined; according to the decision-making ability score, the emotional requirement level is determined; if the patient does not have the decision-making ability, the object type is adjusted to the accompanying person; if the object type is the accompanying person, the basic information and relationship information of the accompanying person are obtained; the basic information and the medical record information are analyzed to determine the decision requirement level; the relationship information is analyzed to determine the emotional requirement level.

[0174] The object types include patients and accompanying persons.

[0175] The medical record information can be all relevant medical record information corresponding to the current condition of the patient, and the medical record information can be provided by the medical system. The age information can be the age value of the current patient. The medical history information can be the past disease history information of the current patient. The cognitive information can be the information reflecting the current cognitive level of the patient, such as education level, etc. The decision-making ability score can be a mathematical quantitative value used to express whether the current patient can make a correct treatment decision. The decision-making ability can be the ability of the patient to make a correct treatment decision.

[0176] The basic information can be the basic information of the accompanying person, such as age, cognitive level, etc.

[0177] The relationship information can be the information used to express the relationship between the accompanying person and the patient, such as direct relative.

[0178] Specifically, when the object type is a patient, based on the medical visit information, the age information, medical history information, and cognitive information of the patient are extracted. Due to factors such as the patient's illness or age, when the patient makes a treatment decision based on medical data, the patient's decision-making ability is in doubt. It is necessary to use mathematical analysis methods to evaluate the patient's decision-making ability based on the patient's age information, medical history information, and cognitive information, and obtain a decision-making ability score that reflects the decision-making ability. When the patient has the decision-making ability, based on the medical visit information, through mathematical analysis methods, the patient's decision-making need level is determined, and through mathematical analysis methods, based on the decision-making ability score, the patient's emotional need level is determined. If the patient does not have the decision-making ability, then a companion is required to make the treatment decision. At this time, the object type is adjusted to the companion. Based on the basic information of the companion and the patient's medical visit information, the decision-making need level of the companion is determined, and through the relationship information reflecting the relationship between the companion and the patient, the emotional need level of the companion is determined. The closer the relationship between the companion and the patient, the higher the emotional need level.

[0179] Through the method of this embodiment, when the object type is a patient, based on the medical visit information, the age information, medical history information, and cognitive information of the patient are determined, and based on the above information, the patient's decision-making ability is evaluated. If the patient has the decision-making ability, then based on the medical visit information and the decision-making ability score, the patient's decision-making need level and emotional need level are respectively determined. If the patient does not have the decision-making ability, then the object type is adjusted to the companion, and based on the basic information of the companion, the relationship information, and the patient's medical visit information, the decision-making need level and emotional need level of the companion are respectively determined, improving the accuracy and comprehensiveness of the evaluation of the patient's decision-making need level and emotional need level, and fully considering the impact of the patient's decision-making ability on the emotional need level and the conversion of the object type, improving the pertinence of the subsequent visualized medical data.

[0180] In some embodiments, if the object type is a medical staff member, then based on the preset emotional need level of the medical staff, the emotional need level is determined; the work information of the medical staff member is obtained, the specific work responsibilities of the medical staff member are determined, and based on the specific work responsibilities, the decision-making need level is determined.

[0181] Medical staff members can be professional personnel responsible for providing medical services and nursing care, such as attending physicians, department doctors, nurses, etc. The preset emotional need level of the medical staff can be the average emotional need level of the preset medical staff members, and the preset emotional need level of the medical staff can be obtained by analyzing historical data.

[0182] The work information can be information reflecting the current medical work direction of the medical staff member, and the work information can be provided by the medical system. The specific work responsibilities can be the specific medical work currently responsible for by the medical staff member.

[0183] Specifically, when the object type is medical staff, as analyzed in the foregoing embodiments, the emotional need characteristics of medical staff require maintaining an appropriate distance from patients emotionally to maintain professionalism and avoid excessive emotional burden. By presetting the emotional need level of medical staff, the emotional need level of medical staff is set to conform to the above emotional need characteristics. At the same time, the decision-making need level of medical staff is determined by the specific work currently responsible for by the medical staff. Different specific work responsibilities result in different decisions and corresponding differences in decision-making need levels. According to the work information of the medical staff, the specific work currently responsible for by the medical staff is determined, and based on the specific work, the corresponding data in the medical decision-making dataset for storing the work of medical staff and the corresponding decision-making need levels is retrieved to determine the decision-making need level of the current medical staff.

[0184] Through the method provided in this embodiment, when the object type is medical staff, by analyzing the emotional need characteristics of medical staff, the emotional need level of medical staff is determined, and according to the work information of medical staff, the specific work currently responsible for by the medical staff is determined, and based on the specific work, the decision-making need level of medical staff is determined, making the decision-making need level and emotional need level more reasonable.

[0185] In some embodiments, according to the medical history information, the treatment time span of the patient is determined; according to the cognitive information, the cognitive ability score of the patient is determined; according to the age information, treatment time span, and cognitive ability score, referring to formula (1), the decision-making ability score is determined:

[0186]

[0187] where D is the decision-making ability score, w A is the preset age relationship coefficient, S A is the age information, k A is the preset age influence index, a is the preset optimal decision-making age, w M is the preset treatment time relationship coefficient, k M is the preset treatment time influence coefficient, S M is the treatment time span, w C is the preset cognitive relationship coefficient, S C is the cognitive ability score; the decision-making ability score is compared with the preset decision-making ability interval. If the decision-making ability score is within the preset decision-making ability interval, it is determined that the patient has decision-making ability; otherwise, it is determined that the patient does not have decision-making ability.

[0188] The treatment time span can be the total duration of the treatment that the patient has experienced so far, and the treatment time can be obtained from the treatment process in the patient's medical history information.

[0189] The cognitive ability score can be a numerical score that measures the cognitive ability of a patient obtained from cognitive information, and the cognitive ability score can be obtained by analyzing historical data based on the cognitive information.

[0190] The preset age relationship coefficient can be a coefficient that reflects the degree of influence of the patient's age on the decision-making ability score, and the preset age relationship coefficient can be obtained by analyzing historical data.

[0191] The preset optimal decision-making age can be the average age corresponding to the best decision-making ability obtained by analyzing historical data, and the preset optimal decision-making age can be obtained by analyzing historical data.

[0192] The preset treatment time relationship coefficient can be a coefficient that expresses the relationship between the treatment time span and the decision-making ability score, and the preset treatment time relationship coefficient can be obtained by analyzing historical data.

[0193] The preset treatment time influence coefficient can be a coefficient that reflects the degree of influence of the treatment time span on the decision-making ability score, and the preset treatment time influence coefficient can be obtained by analyzing historical data.

[0194] The preset cognitive relationship coefficient can be a coefficient that reflects the relationship between the patient's cognitive ability and the decision-making ability score, and the preset cognitive relationship coefficient can be obtained by analyzing historical data.

[0195] The preset decision-making ability interval can be the numerical interval in which the decision-making ability score should be when the patient can make a treatment decision, and the preset decision-making ability interval can be obtained by analyzing historical data.

[0196] Specifically, through the in formula (1) to describe the non-linear and smooth transition influence of the patient's age on their decision-making ability, and then through w M ·ln(k M ·S M +1), the treatment time span is compressed into a smaller range, so that the contribution trend of the longer time span gradually decreases to reflect the decelerating growth effect of the patient's treatment time span on the decision-making ability. As the cognitive ability score increases, through the influence of the cognitive ability score on the decision-making ability is gradually weakened, reasonably reflecting the influence of the cognitive ability on the decision-making ability, so that formula (1) comprehensively reflects the influence of age information, treatment time span and cognitive ability score on the patient's decision-making ability.

[0197] Through the method provided in this embodiment, by means of mathematical analysis, based on the patient's age information, treatment time span, and cognitive ability score, a mathematical formula is designed to quantitatively calculate the patient's decision-making ability score. By comparing the decision-making ability score with a preset decision-making ability interval, it is determined whether the patient has decision-making ability in the current state, making the judgment of the patient's decision-making ability more accurate and comprehensive.

[0198] In some embodiments, according to the medical treatment information, the urgency of the patient's condition, the availability of the support system, and the information complexity are evaluated; according to the urgency of the condition, the availability of the support system, and the information complexity, referring to formula (2), the decision-making requirement score is determined:

[0199] M = α·S + β·(1 - C) + γ·P (2)

[0200] Where M is the decision-making requirement score, α is the preset urgency influence weight, S is the urgency of the condition, β is the preset support influence weight, C is the availability of the support system, γ is the preset complexity influence weight, and P is the information complexity; according to the decision-making requirement score, the decision-making requirement level is determined.

[0201] The urgency of the condition can be data used to reflect the urgency of the patient's current condition that requires treatment, and the urgency of the condition can be evaluated by professional doctors for the patient's condition.

[0202] The availability of the support system can be data used to reflect the social and family support systems that the patient can rely on. The information complexity can be data used to reflect the complexity of the treatment decision corresponding to the patient's current condition.

[0203] The preset urgency influence weight can be the influence weight of the urgency of the condition on the decision-making requirement score, and the preset urgency influence weight can be obtained by analyzing historical data.

[0204] The preset support influence weight can be the influence weight of the availability of the support system on the decision-making requirement score, and the preset support influence weight can be obtained by analyzing historical data.

[0205] The preset complexity influence weight can be the influence weight of the information complexity on the decision-making requirement score, and the preset complexity influence weight can be obtained by analyzing historical data.

[0206] Specifically, during the process of making treatment decisions, patients are influenced by many factors. The most direct one is the urgency of the disease. The higher the disease urgency, the higher the level of the patient's decision-making demand relying on visual medical data. The information complexity of the treatment decision also directly affects the level of the patient's decision-making demand. The higher the above-mentioned information complexity, the higher the level of the patient's decision-making demand relying on visual medical data. Further, the level of the patient's decision-making demand is not only affected by the patient's own disease, but also affected by society and family. The less social and family support the patient can rely on, the higher the level of the patient's decision-making demand relying on visual medical data.

[0207] Therefore, the positive correlation between the patient's disease urgency, information complexity and decision-making demand score is described by α·S and γ·P in formula (2) respectively, and the negative correlation between the availability of the support system and the decision-making demand score is described by β·(1 - C).

[0208] Through the method provided in this embodiment, by using mathematical analysis means, based on the patient's disease urgency, the availability of the support system and information complexity, a mathematical formula is designed to quantify the patient's decision-making demand score, and the level of the patient's decision-making demand is reflected through the decision-making demand score, making the analysis of the level of the patient's decision-making demand more accurate and comprehensive.

[0209] In some embodiments, according to the decision-making ability score, referring to formula (3), the emotional demand score of the patient is determined:

[0210]

[0211] Among them, E is the emotional demand score, a is a preset proportional constant, D is the decision-making ability score, and b is a preset offset; according to the emotional demand score, the emotional demand level is determined.

[0212] The preset proportional constant can be a preset constant used to express the proportional relationship between the decision-making ability score and the emotional demand score, and the preset proportional constant can be obtained by analyzing historical data.

[0213] The preset offset can be a constant term used to reduce the error of the emotional demand score, and the preset offset can be obtained by analyzing historical data.

[0214] Specifically, the emotional demand of the patient can be indirectly reflected by the patient's decision-making ability. For example, if the patient is anxious about the current disease, it is difficult to concentrate on understanding the relevant medical data, resulting in a decline in decision-making ability. At this time, the level of the patient's emotional demand in analyzing visual medical data is relatively high. By using mathematical analysis means, based on the patient's decision-making ability score, the patient's emotional demand score is quantified through formula (3), and the current emotional demand level of the patient is reflected through the emotional demand score.

[0215] Through the method provided in this embodiment, by using mathematical analysis means, on the basis of the patient's decision-making ability score, a mathematical formula is designed to quantify the patient's emotional need score, and the emotional need level of the patient is reflected through the emotional need score, making the analysis of the patient's emotional need level more accurate and comprehensive.

[0216] In some embodiments, according to the object information requirements, the visualization-friendly data is screened to determine the high-matching friendly data; according to the object view requirements, the preset visualization view data set is retrieved, and according to the retrieval result, the high-matching view is determined; the high-matching friendly data and the high-matching view are combined in a reading-friendly manner to construct the visualization data.

[0217] The high-matching friendly data can be the data in the visualization-friendly data that highly matches the object information requirements. The preset visualization view data set can be a data set used to store several view elements for displaying medical data, and the preset visualization view data set can be obtained by analyzing a large number of existing data visualization view elements. The high-matching view can be the view element data in the visualization-friendly data that highly matches the object view requirements. The reading-friendly combination can be a process of combining the high-matching friendly data and the high-matching view for the purpose of facilitating the reading of the data receiving object.

[0218] Specifically, after determining the object information requirements, through the retrieval algorithm, the visualization-friendly data is retrieved to implement the screening of the visualization-friendly data, and the high-matching friendly data that highly matches the object information requirements is determined. At the same time, according to the object view requirements, through the retrieval algorithm, the preset visualization view data set is retrieved to determine the high-matching view that highly matches the object view requirements. Furthermore, through a data visualization tool, such as D3.js, the high-matching friendly data and the high-matching view are combined in a reading-friendly manner to construct the visualization data.

[0219] Through the method provided in this embodiment, starting from two directions of the object information requirements and the object view requirements simultaneously, the visualization-friendly data and the preset visualization view data set are analyzed to respectively obtain the high-matching friendly data and the high-matching view. By combining the high-matching friendly data and the high-matching view in a reading-friendly manner, the visualization data is constructed, making the visualization data highly meet the object information requirements and the object view requirements while enhancing the reading friendliness of the visualization data and improving the transmission effect of the visualization data.

[0220] Figure 3 The structural schematic diagram of a data visualization system based on rich text provided in an embodiment of the present application is as Figure 3As shown in the figure, the rich text-based data visualization system 300 of this embodiment includes: a rich text analysis module 301, a feature information extraction module 302, a data analysis module 303, a visualization module 304, and an update module 305.

[0221] The rich text analysis module 301 is used to obtain medical rich text information, analyze the medical rich text information, and determine visualization-friendly data;

[0222] The feature information extraction module 302 is used to obtain data recipient information, analyze the data recipient information, and determine recipient feature information;

[0223] The data analysis module 303 is used to analyze the visualization-friendly data according to the recipient feature information and determine visualization data;

[0224] The visualization module 304 is used to perform interactive visualization presentation on the visualization data and obtain the interactive operations of the data recipient on the visualization data;

[0225] The update module 305 is used to dynamically update the visualization data according to the interactive operations.

[0226] Optionally, the rich text analysis module 301 is specifically used for:

[0227] Perform standardized denoising processing on the medical rich text information to determine clean rich text information;

[0228] Analyze the clean rich text information to determine key entity information and entity relationship information;

[0229] Construct structured information according to the key entity information and the entity relationship information;

[0230] Determine the structured information as the visualization-friendly data.

[0231] Optionally, the feature information extraction module 302 is specifically used for:

[0232] Determine the object type according to the data recipient information;

[0233] Determine the decision-making requirement level and the emotional requirement level according to the object type;

[0234] Based on the object type, analyze the data recipient information to determine the understanding level information;

[0235] Analyze the understanding level information and the decision-making requirement level to determine the object information requirement;

[0236] Analyze the understanding level information and the emotional need level, and determine the object view requirements;

[0237] Determine the object information requirements and the object view requirements as the received object characteristic information.

[0238] Optionally, when determining the decision requirement level and the emotional need level according to the object type, the feature information extraction module 302 is specifically configured to:

[0239] If the object type is the patient, obtain the medical treatment information of the patient;

[0240] According to the medical treatment information, determine the age information, medical history information and cognitive information of the patient;

[0241] Analyze the age information, the medical history information and the cognitive information, and determine the decision-making ability score of the patient;

[0242] According to the decision-making ability score, judge whether the patient has the decision-making ability;

[0243] If the patient has the decision-making ability, determine the decision requirement level according to the medical treatment information;

[0244] According to the decision-making ability score, determine the emotional need level;

[0245] If the patient does not have the decision-making ability, adjust the object type to the accompanying person;

[0246] If the object type is the accompanying person, obtain the basic information and relationship information of the accompanying person;

[0247] Analyze the basic information and the medical treatment information, and determine the decision requirement level;

[0248] Analyze the relationship information, and determine the emotional need level.

[0249] Optionally, when determining the decision requirement level and the emotional need level according to the object type, the feature information extraction module 302 is specifically configured to:

[0250] If the object type is the medical staff, determine the emotional need level according to the preset medical staff emotional needs;

[0251] Obtain the work information of the medical staff, determine the specific work responsible by the medical staff, and determine the decision requirement level according to the specific work.

[0252] Optionally, when analyzing the age information, the medical history information, and the cognitive information to determine the decision-making ability score of the patient, the feature information extraction module 302 is specifically configured to:

[0253] Determine the treatment time span of the patient according to the medical history information;

[0254] Determine the cognitive ability score of the patient according to the cognitive information;

[0255] Determine the decision-making ability score according to the age information, the treatment time span, and the cognitive ability score, with reference to the following formula:

[0256]

[0257] where D is the decision-making ability score, w A is a preset age relationship coefficient, S A is the age information, k A is a preset age influence index, a is the preset optimal decision-making age, w M is a preset treatment time relationship coefficient, k M is a preset treatment time influence coefficient, S M is the treatment time span, w C is a preset cognitive relationship coefficient, S C is the cognitive ability score;

[0258] Judging whether the patient has decision-making ability according to the decision-making ability score includes:

[0259] Compare the decision-making ability score with a preset decision-making ability interval. If the decision-making ability score is within the preset decision-making ability interval, it is determined that the patient has decision-making ability; otherwise, it is determined that the patient does not have decision-making ability.

[0260] Optionally, when determining the decision-making requirement level according to the visit information, the feature information extraction module 302 is specifically configured to:

[0261] Evaluate the disease urgency, support system availability, and information complexity of the patient according to the visit information;

[0262] Determine the decision-making requirement score with reference to the following formula according to the disease urgency, the support system availability, and the information complexity:

[0263] M = α·S + β·(1 - C) + γ·P;

[0264] Among them, M is the decision-making requirement score, α is the preset urgency influence weight, S is the disease urgency, β is the preset support influence weight, C is the availability of the support system, γ is the preset complexity influence weight, and P is the information complexity;

[0265] Determine the decision-making requirement level according to the decision-making requirement score.

[0266] Optionally, when determining the emotional requirement level according to the decision-making ability score, the feature information extraction module 302 is specifically configured to:

[0267] Determine the emotional requirement score of the patient according to the decision-making ability score with reference to the following formula:

[0268]

[0269] Among them, E is the emotional requirement score, a is the preset proportional constant, D is the decision-making ability score, and b is the preset offset;

[0270] Determine the emotional requirement level according to the emotional requirement score.

[0271] Optionally, the data analysis module 303 is specifically configured to:

[0272] Screen the visualization-friendly data according to the object information requirement to determine the high-matching friendly data;

[0273] Retrieve the preset visualization view data set according to the object view requirement, and determine the high-matching view according to the retrieval result;

[0274] Perform a reading-friendly combination of the high-matching friendly data and the high-matching view to construct the visualization data.

[0275] The system of this embodiment can be used to execute the method of any of the above embodiments, and its implementation principle and technical effects are similar, so they will not be elaborated here.

Claims

1. A rich text-based data visualization method, characterized in that, Including: Obtain medical rich text information, analyze the medical rich text information, and determine visualization-friendly data; Obtain data recipient information, analyze the data recipient information, and determine recipient characteristic information; According to the recipient characteristic information, analyze the visualization-friendly data, and determine visualization data; Perform interactive visualization presentation on the visualization data, and obtain the interactive operations of the data recipient on the visualization data; According to the interactive operations, dynamically update the visualization data.

2. The method according to claim 1, wherein The analyzing the medical rich text information and determining visualization-friendly data includes: Perform standardized denoising processing on the medical rich text information to determine clean rich text information; Analyze the clean rich text information to determine key entity information and entity relationship information; Construct structured information according to the key entity information and the entity relationship information; Determine the structured information as the visualization-friendly data.

3. The method according to claim 2, wherein The analyzing the data recipient information and determining recipient characteristic information includes: Determine the object type according to the data recipient information; Determine the decision requirement level and the emotional requirement level according to the object type; Based on the object type, analyze the data recipient information to determine the understanding level information; Analyze the understanding level information and the decision requirement level to determine the object information requirement; Analyze the understanding level information and the emotional requirement level to determine the object view requirement; Determine the object information requirement and the object view requirement as the recipient characteristic information.

4. The method according to claim 3, characterized in that The object type includes patients and accompanying persons. The determining the decision requirement level and the emotional requirement level according to the object type includes: If the object type is the patient, obtain the patient's medical treatment information; According to the medical treatment information, determine the patient's age information, medical history information, and cognitive information; Analyze the age information, the medical history information, and the cognitive information to determine the patient's decision-making ability score; According to the decision-making ability score, determine whether the patient has decision-making ability; If the patient has decision-making ability, determine the decision requirement level according to the medical treatment information; Determine the emotional requirement level according to the decision-making ability score; If the patient does not have decision-making ability, adjust the object type to the accompanying person; If the object type is the accompanying person, obtain the basic information and relationship information of the accompanying person; Analyze the basic information and the medical treatment information to determine the decision requirement level; Analyze the relationship information to determine the emotional requirement level.

5. The method according to claim 3, wherein The object type also includes medical staff. The determining the decision requirement level and the emotional requirement level according to the object type includes: If the object type is the medical staff, determine the emotional requirement level according to the preset medical staff emotional requirements; Obtain the work information of the medical staff, determine the specific work responsibilities of the medical staff, and determine the decision requirement level according to the specific work responsibilities.

6. The method according to claim 4, characterized in that Analyzing the age information, the medical history information, and the cognitive information to determine the decision-making ability score of the patient, including: Determining the treatment time span of the patient according to the medical history information; Determining the cognitive ability score of the patient according to the cognitive information; Determining the decision-making ability score according to the age information, the treatment time span, and the cognitive ability score, with reference to the following formula: Among them, D is the decision-making ability score, w A is the preset age relationship coefficient, S A is the age information, k A is the preset age influence index, a is the preset optimal decision-making age, w M is the preset treatment time relationship coefficient, k M is the preset treatment time influence coefficient, S M is the treatment time span, w C is the preset cognitive relationship coefficient, S C is the cognitive ability score; Judging whether the patient has decision-making ability according to the decision-making ability score, including: Comparing the decision-making ability score with a preset decision-making ability interval. If the decision-making ability score is within the preset decision-making ability interval, it is determined that the patient has decision-making ability; otherwise, it is determined that the patient does not have decision-making ability.

7. The method according to claim 4, wherein Determining the decision-making requirement level according to the medical visit information, including: Evaluating the disease urgency, the availability of the support system, and the information complexity of the patient according to the medical visit information; Determining the decision-making requirement score according to the disease urgency, the availability of the support system, and the information complexity, with reference to the following formula: M = α·S + β·(1 - C) + γ·P; Where M is the decision-making requirement score, α is the preset urgency impact weight, S is the disease urgency, β is the preset support impact weight, C is the availability of the support system, γ is the preset complexity impact weight, and P is the information complexity; Determining the decision-making requirement level according to the decision-making requirement score.

8. The method according to claim 6, wherein Determining the emotional requirement level according to the decision-making ability score, including: Determining the emotional requirement score of the patient according to the decision-making ability score, with reference to the following formula: Where E is the emotional requirement score, a is a preset proportional constant, D is the decision-making ability score, and b is a preset offset; Determining the emotional requirement level according to the emotional requirement score.

9. The method according to claim 3, wherein Analyzing the visualization-friendly data according to the recipient characteristic information to determine the visualization data, including: Filtering the visualization-friendly data according to the object information requirements to determine the high-matching-friendly data; Retrieving a preset visualization view data set according to the object view requirements, and determining a high-matching view according to the retrieval result; Combining the high-matching-friendly data and the high-matching view in a reading-friendly manner to construct the visualization data.

10. A rich text-based data visualization system, characterized in that Including: A rich text analysis module for obtaining medical rich text information, analyzing the medical rich text information, and determining visualization-friendly data; A characteristic information extraction module for obtaining data recipient information, analyzing the data recipient information, and determining recipient characteristic information; A data analysis module for analyzing the visualization-friendly data according to the recipient characteristic information and determining visualization data; A visualization module for interactively visualizing the visualization data and obtaining interactive operations of the data recipient on the visualization data; An update module for dynamically updating the visualization data according to the interactive operations.

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