Tracing method, device and equipment for clinical aid decision-making and medium

Abstract

The invention discloses a traceability method, device and equipment for clinical aid decision making and a medium, and relates to the technical field of data processing.The method comprises the steps that clinical data are acquired, and whether the clinical data meet rule triggering conditions or not is judged according to a constructed dynamic rule base; the dynamic rule base is associated with the static knowledge base; when the rule triggering condition is met, dynamic traceability information matched with the rule triggering condition is obtained from a dynamic rule base; the dynamic traceability information comprises a target rule entry identifier, a target rule prompt content and a target rule prompt basis content; according to the target rule entry identifier, obtaining static traceability information matched with the target rule entry identifier from a static knowledge base; the static traceability information comprises a target knowledge entry identifier and a target associated knowledge content; and receiving and responding to an operation instruction, and marking the target associated knowledge content. According to the scheme, the credibility problem caused by opaque rules is solved, and the accuracy of clinical decision making is improved.

Description

Technical Field

[0001] The present invention generally relates to the field of clinical medical technology, and specifically relates to a traceability method, device, equipment and medium for clinical auxiliary decision-making. Background Art

[0002] With the continuous development of medical technology and information processing technology, Clinical Decision Support System (CDSS), as a medical information technology application system based on human-computer interaction, has been increasingly used in the medical field because it can provide more and more auxiliary medical support.

[0003] At present, clinical decision-making support systems mainly focus on expanding the number of rules, and improving the coverage and practicality of the system by increasing the number of rules. However, the clinical decision-making support system formed in this solution lacks transparency, making it difficult for medical staff to evaluate its accuracy and reliability, reducing their trust in the system, affecting their judgment and decision-making process, and reducing work efficiency. Summary of the invention

[0004] In view of the above-mentioned defects or deficiencies in the prior art, it is desirable to provide a traceability method, device, equipment and medium for clinical decision-making assistance.

[0005] In a first aspect, the present invention provides a method for tracing the source of clinical auxiliary decision making, the method comprising:

[0006] Acquire clinical data, and determine whether the clinical data meets the rule triggering condition according to the constructed dynamic rule base; the dynamic rule base is associated with the static knowledge base;

[0007] When the rule triggering condition is met, dynamic tracing information matching the rule triggering condition is obtained from the dynamic rule library; the dynamic tracing information includes: target rule item identifier, target rule prompt content, and target rule prompt basis content;

[0008] According to the target rule item identifier, static tracing information matching the target rule item identifier is obtained from the static knowledge base; the static tracing information includes: a target knowledge item identifier and target associated knowledge content, the target associated knowledge content being the knowledge content with the highest degree of matching with the target knowledge item identifier;

[0009] The target-related knowledge content is marked by receiving and responding to the operation instruction.

[0010] In one embodiment, obtaining dynamic tracing information corresponding to the rule triggering condition from the dynamic rule library includes:

[0011] Acquire a rule entry identifier that matches the rule triggering condition from the dynamic rule library as the target rule entry identifier;

[0012] Acquire the rule prompt content matching the target rule entry identifier from the dynamic rule library as the target rule prompt content;

[0013] The rule prompt basis content corresponding to the target rule entry identifier is used as the target rule prompt basis content.

[0014] In one of the embodiments, acquiring static tracing information matching the target rule entry identifier from the static knowledge base according to the target rule entry identifier includes:

[0015] According to the target rule item identifier, acquiring a target knowledge item identifier matching the target rule item identifier from the static knowledge base;

[0016] The associated knowledge content corresponding to the target knowledge item identifier is used as the target associated knowledge content.

[0017] In one embodiment, before obtaining clinical data and determining whether the clinical data meets a rule triggering condition according to a constructed dynamic rule base, the method further includes:

[0018] Build static knowledge base and dynamic rule base;

[0019] A mapping relationship between the static knowledge base and the dynamic rule base is established.

[0020] In one embodiment, building a static knowledge base includes:

[0021] Acquiring clinical auxiliary data; the clinical auxiliary data includes at least one of the following: literature data, medical experience data, clinical data;

[0022] According to preset static conversion rules, the clinical auxiliary data is converted into static knowledge items; the static knowledge items include: knowledge item identifier and knowledge content;

[0023] The static knowledge base is constructed according to the knowledge item identifier and the knowledge content.

[0024] In one embodiment, building a dynamic rule base includes:

[0025] Get dynamic rule data;

[0026] According to a preset dynamic conversion rule, the dynamic rule data is converted into a dynamic rule entry; the dynamic rule entry includes: a rule entry identifier and a rule content;

[0027] For each dynamic rule entry, construct the rule triggering condition, rule prompt content, and rule prompt basis content;

[0028] The dynamic rule library is constructed according to the dynamic rule entries, the rule triggering conditions, the rule prompt contents and the rule prompt basis contents.

[0029] In one embodiment, establishing a mapping relationship between the static knowledge base and the dynamic rule base includes:

[0030] Obtaining the associated knowledge item identifier of each rule item identifier, and establishing a mapping relationship between the rule item identifier and the associated knowledge item identifier;

[0031] Determining, according to the associated knowledge item identifier, knowledge content that matches the associated knowledge item identifier;

[0032] The knowledge content with the highest matching degree is used as the associated knowledge content and is bound to the rule entry identifier.

[0033] In a second aspect, an embodiment of the present application provides a clinical decision-making assistance tracing device, the device comprising:

[0034] A judgment module, used to obtain clinical data and judge whether the clinical data meets the rule triggering condition according to the constructed dynamic rule base; the dynamic rule base is associated with the static knowledge base;

[0035] A dynamic acquisition module, used to acquire dynamic tracing information matching the rule triggering condition from the dynamic rule library when the rule triggering condition is met; the dynamic tracing information includes: target rule item identifier, target rule prompt content, and target rule prompt basis content;

[0036] A static acquisition module, used to acquire static tracing information matching the target rule entry identifier from the static knowledge base according to the target rule entry identifier; the static tracing information includes: a target knowledge entry identifier and a target-related knowledge content, wherein the target-related knowledge content is the knowledge content matching the target knowledge entry identifier and having the highest matching degree;

[0037] The marking module is used to receive and respond to the operation instruction to mark the target-related knowledge content.

[0038] In a third aspect, an embodiment of the present application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the clinical decision-making support traceability method provided in the above embodiment.

[0039] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the clinical decision-making support traceability method provided in the above-mentioned embodiment.

[0040] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0041] The traceability method, device, equipment and medium for clinical decision-making support provided in the embodiments of the present application include: obtaining clinical data, judging whether the clinical data meets the rule triggering condition according to the constructed dynamic rule base, and when the rule triggering condition is met, obtaining dynamic traceability information matching the rule triggering condition from the dynamic rule base, and the dynamic traceability information includes: target rule item identifier, target rule prompt content, target rule prompt basis content, and according to the target rule item identifier, obtaining static traceability information matching the target rule item identifier from the static knowledge base, and the static traceability information includes: target knowledge item identifier and target-related knowledge content, the target-related knowledge content is the knowledge content matching the target knowledge item and having the highest matching degree, receiving and responding to operation instructions, and marking the target-related knowledge content. Compared with the existing technology, this technical solution can determine in real time whether clinical data meets the rule triggering conditions through the dynamic rule base, so that medical personnel can quickly and accurately obtain the matching target rule item identifier, target rule prompt content, and target rule prompt basis content. Since the dynamic rule base is associated with the static knowledge base, the target knowledge item identifier and target-related knowledge content can be quickly obtained according to the target rule item identifier, which provides data guidance information for medical personnel to evaluate the accuracy and reliability of content and rules, and after receiving the operation instruction, the target-related knowledge content is marked, which is convenient for medical personnel to quickly locate the literature content closely related to the current rule. It can not only solve the trust problem caused by the opaque rules, but also improve the utilization efficiency of static medical literature knowledge and the accuracy and efficiency of clinical decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0043] Figure 1 This is an application environment diagram of the clinical decision-making support tracing method provided in one embodiment of the present application;

[0044] Figure 2 A schematic diagram of a flow chart of a clinical decision-making assistance tracing method provided in an embodiment of the present application;

[0045] Figure 3 A flowchart of a method for establishing a mapping relationship between a static knowledge base and a dynamic rule base provided in an embodiment of the present application;

[0046] Figure 4 A flowchart of a clinical decision-making assistance tracing method provided in one embodiment of the present application;

[0047] Figure 5 A schematic diagram of functional modules of a clinical decision-making support traceability device provided in one embodiment of the present application;

[0048] Figure 6 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0050] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0051] As mentioned in the background technology, the clinical decision support system in the related technology lacks transparency. Since the rule prompts lack clear basis and source, it is difficult for medical personnel to evaluate their accuracy and reliability, which reduces their trust in the system, affects their judgment and decision-making process, and leads to reduced work efficiency.

[0052] Based on the above-mentioned defects, the present application provides a traceability method for clinical decision-making assistance. Compared with the existing technology, the technical solution can determine in real time whether the clinical data meets the rule triggering conditions through the dynamic rule base, so that medical personnel can quickly and accurately obtain the matching target rule item identification, target rule prompt content, and target rule prompt basis content. Since the dynamic rule base is associated with the static knowledge base, the target knowledge item identification and target-related knowledge content can be quickly obtained according to the target rule item identification, which provides data guidance information for medical personnel to evaluate the accuracy and reliability of content and rules, and after receiving the operation instructions, the target-related knowledge content is marked, which is convenient for medical personnel to quickly locate the literature content closely related to the current rules. It can not only solve the trust problem caused by the opaque rules, but also improve the utilization efficiency of static medical literature knowledge and the accuracy and efficiency of clinical decision-making.

[0053] The clinical decision-making assistance tracing method provided in the embodiments of the present application can be applied to Figure 1 In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, or placed on the cloud or other servers. The terminal 102 can send the real-time monitored clinical data to the server 104. After obtaining the clinical data, the server 104 determines whether the clinical data meets the rule triggering conditions according to the constructed dynamic rule library. When the rule triggering conditions are met, the corresponding dynamic traceability information is obtained from the dynamic rule library, and the corresponding static traceability information is obtained from the static knowledge base. Then, after receiving the operation instruction on the terminal 102, the target association indication content is marked and displayed on the terminal 102. In addition, in some embodiments, the traceability method of clinical auxiliary decision-making can also be implemented separately by the server 104 or the terminal 102, such as the terminal 102 can directly process the traceability of the clinical auxiliary decision to be processed, or the server 104 can obtain clinical data from the data storage system and perform traceability processing on the clinical data.

[0054] Optionally, the above-mentioned dynamic rule base and static knowledge base can be constructed by the server 104 or the terminal 102 based on the analysis of clinical auxiliary data.

[0055] The terminal 102 may be, but is not limited to, various desktop computers, laptop computers, smart phones, tablet computers, IoT devices, and portable wearable devices. The IoT devices may be smart speakers, smart TVs, smart air conditioners, smart vehicle-mounted devices, etc. The portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers, or may be a cloud server.

[0056] In an exemplary embodiment, Figure 2 As shown, a clinical decision-making assistance tracing method is provided. The method is executed by a computer device, and can be executed by a computer device such as a terminal or a server alone, or by a terminal and a server together. In the embodiment of the present application, the method is applied to Figure 1 The server 104 in the example is used as an example to illustrate the method, which includes the following steps S201 to S204. Among them:

[0057] Step S201, obtaining clinical data, and judging whether the clinical data meets the rule triggering condition according to the constructed dynamic rule base; the dynamic rule base is associated with the static knowledge base.

[0058] It should be noted that the above clinical data is data that needs to be assisted by the clinical decision support system, and may include the patient's vital signs information, diagnosis and treatment parameter information, basic information, etc. The dynamic rule base and the static knowledge base can be pre-customized according to actual needs. The dynamic rule base includes multiple rule trigger conditions, each of which can correspond to a set of clinical data, and whether the rule trigger condition is met can be determined based on the clinical data.

[0059] The above clinical data may be obtained through a public data source, may be obtained by importing from an external system, or may be obtained through real-time monitoring. This embodiment does not limit the method for obtaining the clinical data.

[0060] In this embodiment, after obtaining the clinical data, the dynamic rule library can be called to determine whether the clinical data meets the rule triggering conditions in the dynamic rule library. Specifically, the corresponding condition parameters can be determined based on the clinical data, and the rule parameters of each rule triggering condition can be obtained. Then, the condition parameters are matched with the rule parameters. When the match is consistent, it indicates that the clinical data meets the rule triggering conditions; when the match is inconsistent, it indicates that the clinical data does not meet the rule triggering conditions.

[0061] Exemplarily, the above-mentioned rule triggering conditions may include the patient's vital signs information and diagnosis and treatment parameter information, such as: body temperature is higher than 38.5°C for three consecutive days, and the white blood cell count is above 10×109 / L, and at the same time, lung imaging examination shows that the area of ​​inflammatory lesions has expanded by 20% or more in the past week.

[0062] In this embodiment, after obtaining the clinical data, it is determined whether the clinical data meets the rule triggering conditions based on the constructed dynamic rule library, so as to assist medical personnel in conducting rule queries to further confirm the accuracy and reliability of the data in the clinical decision support system, thereby solving the trust problem caused by opaque rules.

[0063] In one embodiment, before obtaining clinical data and determining whether the clinical data meets the rule triggering condition according to the constructed dynamic rule base, refer to Figure 3 As shown, the above method also includes:

[0064] Step S301, constructing a static knowledge base and a dynamic rule base.

[0065] Step S302: Establish a mapping relationship between the static knowledge base and the dynamic rule base.

[0066] It should be noted that the above-mentioned static knowledge base can be constructed based on clinical auxiliary data, and the dynamic rule base can be constructed based on dynamic rule data. The static knowledge base can include basic medical knowledge and clinical medical knowledge, etc. Basic medical knowledge can include, for example, knowledge related to human anatomy, physiological knowledge, and pathological knowledge. Knowledge related to human anatomy can include: the structure, position, and physiological function of each organ; physiological knowledge can cover the various functional operations of the human body under normal physiological conditions; pathological knowledge can include: the causes, pathogenesis, and pathological changes of various diseases.

[0067] The dynamic rule base may include rule triggering conditions, rule prompt content, and rule prompt basis content. The rule triggering conditions are usually related to the patient's vital signs, laboratory test results, imaging test results, etc. The rule prompt content may refer to the guidance operations performed by medical staff after the rule is triggered, and the rule prompt basis content is the rule reference basis corresponding to the rule prompt content, that is, the theoretical basis or practical basis supporting the formulation of the rule.

[0068] As an implementable method, in the process of constructing a static knowledge base, clinical auxiliary data is obtained, and the clinical auxiliary data includes at least one item: literature data, medical experience data, and clinical data. According to preset static conversion rules, the clinical auxiliary data is converted into static knowledge items. The static knowledge items include: knowledge item identifiers and knowledge content. Then, based on the knowledge item identifiers and knowledge content, a static knowledge base is constructed.

[0069] It should be noted that the above clinical auxiliary data can be imported from an external device, obtained from a public data source, or obtained by reviewing the data. The present application embodiment does not impose any restrictions on the method of obtaining clinical auxiliary data. Clinical auxiliary data can include original data such as medical literature guidelines, expert consensus, clinical pathways, and medical textbooks. The preset static conversion rules are customized according to actual needs.

[0070] After obtaining clinical auxiliary data, the clinical auxiliary data can be converted into static knowledge items according to preset static conversion rules. Each static knowledge item includes: knowledge item identifier and knowledge content. The knowledge item identifier is used to uniquely represent the identification information of the knowledge item, for example, it can be a "knowledge ID", and the knowledge content includes specific literature information. Each knowledge item identifier uniquely corresponds to one or more associated knowledge contents.

[0071] Optionally, the above-mentioned static knowledge base can be configured with an index structure, which is used to associate the knowledge ID with the knowledge content. The index structure may include, for example, a hash tree, a B-tree, etc. After obtaining the target knowledge ID, the corresponding target knowledge content can be quickly found in the static knowledge base according to the index structure, allowing the system to quickly locate the target entry in constant time or logarithmic time, thereby significantly improving the retrieval speed of the rule basis.

[0072] In the embodiment of the present application, by constructing a static knowledge base, a database can be suggested for clinical medical personnel to consult, providing good data guidance information for the subsequent application of dynamic rules.

[0073] As another feasible method, in the process of constructing a dynamic rule library, dynamic rule data may be first obtained, and the dynamic rule data may be converted into dynamic rule entries according to preset dynamic conversion rules; the dynamic rule entries include: rule entry identification information and rule content; for each dynamic rule entry, rule triggering conditions, rule prompt content and rule prompt basis content are constructed; according to the dynamic rule entries, rule triggering conditions, rule prompt content and rule prompt basis content, a dynamic rule library is constructed.

[0074] It should be noted that the above dynamic rule data can be imported from an external device, obtained from a public data source, or obtained after consulting materials. The embodiment of the present application does not impose any restrictions on the acquisition method of dynamic rule data. Dynamic rule data refers to the data required by dynamic rules, for example, it can include multiple cases, medication guidelines, clinical operation guidelines, etc. The preset dynamic conversion rules are customized according to actual needs.

[0075] After acquiring the dynamic rule data, the dynamic rule data can be converted into dynamic rule entries according to the preset dynamic conversion rules. Each dynamic rule entry includes a rule entry identifier and specific rule content, and defines the rule triggering conditions, including specific patient population characteristics and clinical operation scenarios, to ensure that the rules can be triggered and activated at the appropriate time and situation, and define the rule prompt content, which can include specific information displayed to medical staff after the rule triggering conditions are triggered, and then define the basis content of the rule prompt, which can include literature materials, historical experience materials, etc. The above rule entry identifier can be a "rule ID", and the rule content includes specific rule information, so as to construct a dynamic rule library. Each rule entry identifier uniquely corresponds to one or more associated rule contents, rule triggering conditions, rule prompt content, and rule prompt basis content.

[0076] Among them, the triggering conditions of the above rules can be set as follows: when the patient's blood pressure is higher than 180 / 110mmHg and accompanied by symptoms such as headache and dizziness, the triggered rule content is: Hypertensive emergency treatment rules. The triggering thresholds in different disease scenarios clarify the specific values ​​of each indicator or the specific circumstances when the corresponding rules are activated. For example, when monitoring the blood sugar of diabetic patients, when the fasting blood sugar is higher than 13.9mmol / L and accompanied by positive ketone bodies, the diabetic ketoacidosis treatment rules are triggered.

[0077] The above-mentioned rule prompts are immediate guidance information after the rules are triggered, which are used to prompt medical staff what initial actions and subsequent diagnosis and treatment recommendations should be taken. For example, after the above-mentioned hypertension emergency treatment rules are triggered, the rule prompts can be "immediately give antihypertensive drug treatment, place the patient in a quiet environment, closely monitor blood pressure changes, etc." Diagnosis and treatment recommendations refer to providing medical staff with the direction of the next diagnosis and treatment activities that should be carried out after the initial treatment. For example, when dealing with diabetic ketoacidosis, subsequent recommendations may include "complete laboratory tests such as blood gas analysis as soon as possible, adjust fluid replacement and insulin treatment plans according to the results, etc."

[0078] The rule prompt basis content can be expressed in the form of a rule basis link or summary, and provide the theoretical support source behind each rule, pointing to relevant medical research papers, clinical guidelines and other literature in the form of links, or extracting the abstract content from these literature and storing it in the dynamic rule base. For example, the basis of the hypertensive emergency treatment rule may come from an authoritative research paper on the treatment of hypertensive objects. The link or abstract of the paper is stored so that medical staff can query and understand the scientific nature of the rule.

[0079] After constructing the static knowledge base and the dynamic rule base, a mapping relationship between the static knowledge base and the dynamic rule base is established. By obtaining the associated knowledge item identifier of each rule item identifier and establishing a mapping relationship between the rule item identifier and the associated knowledge item identifier, the knowledge content that matches the associated knowledge item identifier is determined based on the associated knowledge item identifier, and then the content with the highest matching degree is determined from the knowledge content as the associated knowledge content and bound to the rule item identifier.

[0080] Specifically, taking the rule entry identified as "rule ID" and the knowledge entry identified as "knowledge ID" as an example, a binding relationship is established between the knowledge ID in the static knowledge base and the rule ID in the dynamic rule base. For each rule ID, the associated knowledge ID corresponding to the rule ID can be determined, and each dynamic rule is associated with the knowledge ID in the relevant static knowledge base. According to the associated rule ID, the knowledge content matching the associated rule ID is determined, and then the content with the highest matching degree is determined from the knowledge content as the associated knowledge content, and the associated knowledge content is used as the strongly associated content and bound to the rule ID. Among them, the associated knowledge content can be the paragraph content that is most closely associated with the rule ID among all the knowledge content, for example, it can be in text form, it can also be in table form, or it can also be in graphic form. The text format can be, for example, summary content.

[0081] Optionally, the above-mentioned associated knowledge content can be determined by deeply analyzing all knowledge content using a preset algorithm. The preset algorithm may be, for example, a natural language processing (NLP) algorithm. By pre-binding the associated knowledge content to the knowledge ID, it can ensure that medical personnel can quickly obtain key information, thereby improving the accuracy and relevance of the rule basis.

[0082] In this embodiment, by constructing a static knowledge base and a dynamic rule base, and establishing a mapping relationship between the static knowledge base and the dynamic rule base, it is ensured that each rule can accurately find specific related knowledge content, and can accurately determine dynamic traceability information and static traceability information, avoiding confusion and mismatching that may be caused by many-to-many or one-to-many relationships, thereby improving the accuracy of recognition.

[0083] Step S202, when the rule triggering condition is met, dynamic tracing information matching the rule triggering condition is obtained from the dynamic rule library; the dynamic tracing information includes: target rule entry identifier, target rule prompt content, and target rule prompt basis content.

[0084] Step S203, according to the target rule item identifier, obtain static traceability information matching the target rule item identifier from the static knowledge base; the static traceability information includes: the target knowledge item identifier and the target associated knowledge content, the target associated knowledge content is the knowledge content with the highest degree of matching with the target knowledge item identifier.

[0085] Step S204: receiving and responding to the operation instruction to mark the associated knowledge content.

[0086] Specifically, see Figure 4 As shown, Figure 4The flowchart of the traceability method for clinical decision support provided in the embodiment of the present application includes a database construction phase and an application phase. For the construction phase, a static knowledge base and a dynamic rule base are first defined. Specifically, after the static knowledge base is defined, the data such as medical literature guides and medical experience materials are received and processed, and the data is converted into static knowledge entries according to static conversion rules. Each static knowledge entry includes: a knowledge ID and corresponding knowledge content. After defining the dynamic rule base, dynamic rule entries can be defined, and the rule ID and rule name can be obtained. Then, for each rule ID, the corresponding rule triggering conditions, rule prompt content, and rule prompt basis content are defined. A binding relationship between the static knowledge base and the dynamic rule base is established by establishing a binding relationship based on the knowledge ID and the rule ID, and then the knowledge content with the highest degree of association with the rule ID is obtained as the strongly associated content, and the strongly associated content is copied and stored.

[0087] After establishing a binding relationship between the static knowledge base and the dynamic rule base, the application phase can be executed. For the application phase, the clinical data is monitored in real time, and it is determined whether the clinical data meets the rule triggering conditions. When the rule triggering conditions are met, the target rule ID that matches the rule triggering conditions is obtained from the dynamic rule base, and then the target rule prompt content corresponding to the target rule ID is found, and the target rule ID is displayed on the clinical information system interface. The target rule prompt content can provide instant guidance data for medical staff; then, the target rule prompt basis content is obtained and displayed on the clinical information system interface based on the target rule ID. The target rule prompt basis content can be expressed in the form of a link or summary, so that medical staff can understand the theoretical support data corresponding to the rule.

[0088] Exemplarily, the target rule prompt content displayed on the clinical information system interface may include: "The patient's current pneumonia symptoms are worsening, and it is necessary to consider adjusting the treatment plan. It is recommended to re-evaluate the effectiveness of the antibiotics used, and consider conducting a drug sensitivity test to guide more accurate medication selection. At the same time, pay close attention to the patient's respiratory function and other vital signs. "The target rule prompt basis content can be, for example, the basis of the hypertensive emergency treatment rule, and the basis content can be from an authoritative research paper on the treatment of hypertensive crises. The link or abstract of the paper is stored so that medical staff can query and understand the scientific nature of the rule.

[0089] Furthermore, after obtaining the target rule ID, if medical personnel want to gain a deeper understanding of the static traceability information, they can call the static knowledge base and display it, and find the target knowledge ID that matches the target rule ID from the static knowledge base according to the target rule ID. When medical personnel open the static document knowledge and need to further obtain strongly associated content, they can perform a trigger operation on the interface, so that medical personnel receive and respond to the operation instructions, obtain the target-associated knowledge content related to the target rule, and mark the target-associated knowledge content after obtaining it. For example, the target-associated knowledge content can be highlighted, or it can be displayed in the form of an underline, or it can be displayed in bold.

[0090] The target-related knowledge content may be a paragraph that has the highest degree of matching with the current rule, or may be a keyword or key sentence that has the highest degree of matching with the current rule.

[0091] In this embodiment, when clinical data meets the rule triggering conditions in the dynamic rule base, the corresponding rules can be matched immediately and the target rule item identifier, target rule prompt content, and target rule prompt basis content can be displayed, so that medical personnel can quickly obtain guidance information without spending a lot of time reading materials. The rule prompt content in the dynamic rule base is combined with the literature knowledge in the static knowledge base, providing medical personnel with more accurate diagnosis and treatment directions, so as to formulate personalized diagnosis and treatment plans that are more in line with the patient's specific conditions. Through rule identification, medical personnel can easily obtain dynamic traceability information from the dynamic rule base and static traceability information from the static knowledge base, saving time and energy for knowledge acquisition and providing medical personnel with a more convenient way to acquire knowledge. When medical personnel open relevant literature, they can quickly focus on the key content closely related to the current rules, highlight key knowledge, and improve the efficiency and quality of medical decision-making.

[0092] The traceability method for clinical decision support provided in the embodiment of the present application includes: acquiring clinical data, judging whether the clinical data meets the rule triggering condition based on the constructed dynamic rule base, and when the rule triggering condition is met, acquiring dynamic traceability information matching the rule triggering condition from the dynamic rule base, the dynamic traceability information including: target rule item identifier, target rule prompt content, target rule prompt basis content, and according to the target rule item identifier, acquiring static traceability information matching the target rule item identifier from the static knowledge base, the static traceability information including: target knowledge item identifier and target-related knowledge content, the target-related knowledge content is the knowledge content matching the target knowledge item and having the highest matching degree, receiving and responding to operation instructions, and marking the target-related knowledge content. Compared with the existing technology, this technical solution can determine in real time whether clinical data meets the rule triggering conditions through the dynamic rule base, so that medical personnel can quickly and accurately obtain the matching target rule item identifier, target rule prompt content, and target rule prompt basis content. Since the dynamic rule base is associated with the static knowledge base, the target knowledge item identifier and target-related knowledge content can be quickly obtained according to the target rule item identifier, providing data guidance information for medical personnel to evaluate the accuracy and reliability of content and rules, and marking the target-related knowledge content after receiving the operation instruction, so that medical personnel can quickly locate the literature content closely related to the current rules. It can not only solve the trust problem caused by the opaque rules, but also improve the utilization efficiency of static medical literature knowledge and the accuracy and efficiency of clinical decision-making, which helps the behavior of rule managers and avoids the application of prompt content without rule basis in clinical practice, further improving the practicality and reliability of clinical decision-making support systems.

[0093] Based on the same inventive concept, the embodiment of the present application also provides a clinical decision-making tracing device for implementing the clinical decision-making tracing method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the embodiments of one or more clinical decision-making tracing devices provided below can refer to the limitations of the clinical decision-making tracing method above, and will not be repeated here.

[0094] In an exemplary embodiment, Figure 5 As shown, a clinical decision-making support traceability device is provided, and the clinical decision-making support traceability device includes:

[0095] The judgment module 510 is used to obtain clinical data and judge whether the clinical data meets the rule triggering condition according to the constructed dynamic rule base; the dynamic rule base is associated with the static knowledge base;

[0096] The dynamic acquisition module 520 is used to acquire dynamic tracing information matching the rule triggering condition from the dynamic rule library when the rule triggering condition is met; the dynamic tracing information includes: target rule item identifier, target rule prompt content, and target rule prompt basis content;

[0097] The static acquisition module 530 is used to acquire static traceability information matching the target rule entry identifier from the static knowledge base according to the target rule entry identifier; the static traceability information includes: the target knowledge entry identifier and the target associated knowledge content, the target associated knowledge content is the knowledge content matching the target knowledge entry identifier and having the highest matching degree;

[0098] The marking module 540 is used to receive and respond to the operation instruction to mark the target-related knowledge content.

[0099] As an optional implementation, the dynamic acquisition module 520 is specifically used to:

[0100] Obtaining a rule entry identifier that matches the rule triggering condition from the dynamic rule library as a target rule entry identifier;

[0101] Obtaining rule prompt content matching the target rule entry identifier from the dynamic rule library as the target rule prompt content;

[0102] The rule prompt basis content corresponding to the target rule entry identifier is used as the target rule prompt basis content.

[0103] As an optional implementation, the static acquisition module 530 is specifically used to:

[0104] According to the target rule entry identifier, a target knowledge entry identifier matching the target rule entry identifier is obtained from the static knowledge base;

[0105] The associated knowledge content corresponding to the target knowledge entry identifier is used as the target associated knowledge content.

[0106] As an optional implementation, the above device is also used for:

[0107] Build static knowledge base and dynamic rule base;

[0108] Establish a mapping relationship between the static knowledge base and the dynamic rule base.

[0109] As an optional implementation, the above device is also used for:

[0110] Obtain clinical auxiliary data; clinical auxiliary data includes at least one of the following: literature data, medical experience data, clinical data;

[0111] According to the preset static conversion rules, clinical auxiliary data are converted into static knowledge items; static knowledge items include: knowledge item identifier and knowledge content;

[0112] Construct a static knowledge base based on knowledge item identifiers and knowledge content.

[0113] As an optional implementation, the above device is also used for:

[0114] Get dynamic rule data;

[0115] According to the preset dynamic conversion rules, the dynamic rule data is converted into dynamic rule entries; the dynamic rule entries include: rule entry identifier and rule content;

[0116] For each dynamic rule entry, construct the rule triggering condition, rule prompt content, and rule prompt basis content;

[0117] A dynamic rule library is constructed based on dynamic rule entries, rule triggering conditions, rule prompt content and rule prompt basis content.

[0118] As an optional implementation, the above device is also used for:

[0119] Obtaining the associated knowledge item identifier of each rule item identifier, and establishing a mapping relationship between the rule item identifier and the associated knowledge item identifier;

[0120] According to the associated knowledge item identifier, determining the knowledge content matching the associated knowledge item identifier;

[0121] The knowledge content with the highest matching degree is used as the associated knowledge content and is bound to the rule entry identifier.

[0122] Among them, by implementing this implementation method, the technical solution can determine in real time whether the clinical data meets the rule triggering conditions through the dynamic rule library, so that medical personnel can quickly and accurately obtain the matching target rule item identification, target rule prompt content, and target rule prompt basis content. Since the dynamic rule library is associated with the static knowledge base, the target knowledge item identification and target-related knowledge content can be quickly obtained according to the target rule item identification, which provides data guidance information for medical personnel to evaluate the accuracy and reliability of content and rules, and after receiving the operation instructions, the target-related knowledge content is marked, which is convenient for medical personnel to quickly locate the literature content closely related to the current rules. It can not only solve the trust problem caused by the opaque rules, but also improve the utilization efficiency of static medical literature knowledge and the accuracy and efficiency of clinical decision-making.

[0123] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 6 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store video tag processing data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a traceability method for clinical auxiliary decision-making is implemented.

[0124] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0125] In an exemplary embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0126] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0127] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0128] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0129] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0130] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0131] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0132] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A clinical decision-making tracing method, characterized in that: The clinical decision-making assistance traceability method includes: Acquire clinical data, and determine whether the clinical data meets the rule triggering condition according to the constructed dynamic rule base; the dynamic rule base is associated with the static knowledge base; When the rule triggering condition is met, dynamic tracing information matching the rule triggering condition is obtained from the dynamic rule library; the dynamic tracing information includes: target rule item identifier, target rule prompt content, and target rule prompt basis content; According to the target rule item identifier, static tracing information matching the target rule item identifier is obtained from the static knowledge base; the static tracing information includes: a target knowledge item identifier and target associated knowledge content, the target associated knowledge content being the knowledge content with the highest degree of matching with the target knowledge item identifier; The target-related knowledge content is marked by receiving and responding to the operation instruction.

2. The clinical decision-making support tracing method according to claim 1, characterized in that: Acquiring dynamic tracing information corresponding to the rule triggering condition from the dynamic rule library includes: Acquire a rule entry identifier matching the rule triggering condition from the dynamic rule library as the target rule entry identifier; Acquire the rule prompt content matching the target rule entry identifier from the dynamic rule library as the target rule prompt content; The rule prompt basis content corresponding to the target rule entry identifier is used as the target rule prompt basis content.

3. The clinical decision-making support tracing method according to claim 1, characterized in that: According to the target rule entry identifier, acquiring static tracing information matching the target rule entry identifier from the static knowledge base includes: According to the target rule item identifier, acquiring a target knowledge item identifier matching the target rule item identifier from the static knowledge base; The associated knowledge content corresponding to the target knowledge item identifier is used as the target associated knowledge content.

4. The clinical decision-making support tracing method according to claim 1, characterized in that: Before obtaining clinical data and determining whether the clinical data meets a rule triggering condition according to the constructed dynamic rule base, the method further includes: Build static knowledge base and dynamic rule base; A mapping relationship between the static knowledge base and the dynamic rule base is established.

5. The clinical decision-making support tracing method according to claim 4, characterized in that: Build a static knowledge base, including: Acquiring clinical auxiliary data; the clinical auxiliary data includes at least one of the following: literature data, medical experience data, clinical data; According to preset static conversion rules, the clinical auxiliary data is converted into static knowledge items; the static knowledge items include: knowledge item identifier and knowledge content; The static knowledge base is constructed according to the knowledge item identifier and the knowledge content.

6. The clinical decision-making support tracing method according to claim 4, characterized in that: Build a dynamic rule base, including: Get dynamic rule data; According to a preset dynamic conversion rule, the dynamic rule data is converted into a dynamic rule entry; the dynamic rule entry includes: a rule entry identifier and a rule content; For each dynamic rule entry, construct the rule triggering condition, rule prompt content and rule prompt basis content; The dynamic rule library is constructed according to the dynamic rule entries, the rule triggering conditions, the rule prompt contents and the rule prompt basis contents.

7. The clinical decision-making support tracing method according to claim 4, characterized in that: Establishing a mapping relationship between the static knowledge base and the dynamic rule base includes: Obtaining the associated knowledge item identifier of each rule item identifier, and establishing a mapping relationship between the rule item identifier and the associated knowledge item identifier; Determining, according to the associated knowledge item identifier, knowledge content that matches the associated knowledge item identifier; The knowledge content with the highest matching degree is used as the associated knowledge content and is bound to the rule entry identifier.

8. A clinical decision-making aid tracing device, characterized in that: The clinical decision-making assistance traceability device includes: A judgment module, used to obtain clinical data and judge whether the clinical data meets the rule triggering condition according to the constructed dynamic rule base; the dynamic rule base is associated with the static knowledge base; A dynamic acquisition module, used to acquire dynamic tracing information matching the rule triggering condition from the dynamic rule library when the rule triggering condition is met; the dynamic tracing information includes: target rule item identifier, target rule prompt content, and target rule prompt basis content; A static acquisition module, used to acquire static tracing information matching the target rule entry identifier from the static knowledge base according to the target rule entry identifier; the static tracing information includes: a target knowledge entry identifier and a target-related knowledge content, wherein the target-related knowledge content is the knowledge content matching the target knowledge entry identifier and having the highest matching degree; The marking module is used to receive and respond to the operation instruction to mark the target-related knowledge content.

9. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the clinical decision-making support method described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for tracing the clinical auxiliary decision-making described in any one of claims 1 to 7 is implemented.

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