Interrogation priority calculation method based on traditional Chinese medicine disease relationship knowledge graph

Abstract

The invention provides an inquiry priority calculation method based on a traditional Chinese medicine symptom relation knowledge graph, and the method comprises the steps: constructing the traditional Chinese medicine symptom relation knowledge graph, employing the relevance between symptoms, between symptoms and diseases, and between pathogenicity and diseases, combining the symptom information of a user, automatically calculating and determining the inquiry priority, and improving the user experience. The key information is quickly obtained by preferentially inquiring the symptom with the highest priority, so that the efficiency and accuracy of traditional Chinese medicine diagnosis are improved, the inquiry times of patients are reduced, the patient experience is improved, more timely and effective medical services are provided for the patients, inquiry omission caused by insufficient experience of doctors or subjective factors can be avoided, and the diagnosis efficiency is improved. Unmanned intelligent inquiry is realized, standardization and normalization of the inquiry process are ensured, and the quality of medical services is improved.

Description

Technical Field

[0001] The present invention relates to the field of TCM intelligent technology, and in particular to a method for calculating consultation priority based on a TCM disease relationship knowledge graph. Background Art

[0002] In today's medical diagnosis field, TCM, as the traditional medical system of the Chinese nation, still plays an important role in medical care with its unique theoretical system and rich clinical experience. In the TCM diagnosis process, doctors need to determine the order of consultation based on the patient's initial symptom description to ensure that the patient receives the correct diagnosis.

[0003] At present, traditional Chinese medicine diagnosis mainly relies on the personal experience and professional knowledge of doctors, lacking systematic and standardized diagnostic tools, which to some extent affects the accuracy and efficiency of diagnosis. In addition, with the increasing number of patients, doctors need to process a large amount of symptom information when making a diagnosis, which not only increases the workload of doctors, but also may lead to some patients in urgent need of treatment not being able to receive timely diagnosis and treatment.

[0004] Therefore, how to use modern scientific and technological means to improve the efficiency and accuracy of TCM diagnosis has become an urgent problem to be solved. Summary of the invention

[0005] In order to solve the problems existing in the prior art, the present invention provides a method for calculating the consultation priority based on a knowledge graph of TCM disease relationships, comprising the following steps:

[0006] Pre-construct a TCM disease relationship knowledge graph with symptoms, pathological properties and diseases as entities, and the relationships between symptoms, between symptoms and diseases, and between pathological properties and diseases as inter-entity relationships, where the inter-entity relationships are represented by weight values;

[0007] Initialize the priority values ​​of all symptoms to 0, initialize the weight values ​​between all symptoms and diseases, the weight values ​​between symptoms, and the weight values ​​between pathologies and diseases, and set the search path length between symptoms and diseases and the search path length between symptoms;

[0008] Receive the symptom information currently input by the user, and judge the disease nature of the user, wherein the symptom information includes symptoms selected by the user and symptoms not selected by the user;

[0009] Calculate the priority of each symptom based on the TCM disease relationship knowledge graph and the received user symptom information and disease nature;

[0010] Make the highest priority symptoms the ones your doctor asks about first during your next visit.

[0011] Optionally, the step of calculating the priority of each symptom based on the TCM disease relationship knowledge graph and the received user symptom information and disease nature includes:

[0012] Obtain the weight values ​​between the received user symptoms and the disease and the weight values ​​between the user's pathology and the disease from the TCM disease relationship knowledge graph, and calculate the probability value of each disease based on the weight values ​​between the received user symptoms and the disease and the weight values ​​between the user's pathology and the disease;

[0013] The priority of each symptom is calculated based on the probability value of each disease, the initial weight values ​​between the symptom and the disease, and the initial weight values ​​between the symptoms.

[0014] Furthermore, the method of obtaining the weight values ​​between the received user symptoms and the diseases and the weight values ​​between the user's pathological nature and the diseases from the TCM disease relationship knowledge graph, and calculating the probability value of each disease according to the weight values ​​between the received user symptoms and the diseases and the weight values ​​between the user's pathological nature and the diseases, includes:

[0015] Get all user selected symptoms And these symptoms belong to the disease i;

[0016] Get all symptoms that the user has not selected And these symptoms belong to the disease i;

[0017] Get symptoms and the weight between disease i Sum these weight values;

[0018] Get symptoms and the weight between disease i Sum these weight values;

[0019] Get the weight values ​​w of all symptoms of disease i ds , these weight values ​​are summed up;

[0020] Get the weight value between the user's disease type and disease i Sum these weight values;

[0021] Get the weight value w of all pathologies dn , calculate the sum of these weight values;

[0022] Calculate the probability value P of disease i i , the specific formula is as follows:

[0023]

[0024] Among them, λ 1 and λ 2 It is the pre-set specific gravity value.

[0025] Furthermore, the priority of each symptom is calculated according to the probability value of each disease, the initial weight value between the symptom and the disease, and the initial weight value between the symptoms, including:

[0026] Traverse the symptoms k except the symptoms selected or not selected by the user, and obtain the initial weight values ​​between these symptoms k and disease i, and the initial weight values ​​between symptoms k and other symptoms;

[0027] Calculate the latest weight value between symptom k and disease i, and update the initial weight value to the latest weight value. The specific formula is as follows:

[0028]

[0029] in, is the initial weight value between symptom k and disease i, P i is the probability value of disease i;

[0030] Calculate the latest weight value between symptom k and other symptoms, and update the initial weight value to the latest weight value. The specific formula is as follows:

[0031]

[0032] Among them, μ is a variable coefficient, which can be set as needed. is the initial weight value between symptom k and other symptoms;

[0033] Calculate the priority of symptom k j , the specific formula is as follows:

[0034]

[0035] in, and are the search path lengths between symptoms and diseases and the search path lengths between symptoms, respectively. The lengths can be set according to actual conditions. l is the number of diseases adjacent to symptom k. is the latest weight value between the disease adjacent to symptom k and symptom k, m is the number of symptoms selected by users adjacent to symptom k, The latest weight value between the symptom selected by the user adjacent to symptom k and symptom k.

[0036] After adopting the above technical solution, the present invention has the following beneficial effects:

[0037] 1. Improve the accuracy of medical consultation

[0038] The present invention is based on the knowledge graph of TCM disease relationships and utilizes the correlation between symptoms, between symptoms and diseases, and between pathology and diseases. It can accurately calculate the priority of other symptoms, and quickly obtain key information by prioritizing the highest priority symptoms, thereby improving the accuracy of diagnosis.

[0039] 2. Reduce the number of consultation rounds

[0040] The present invention can effectively reduce the number of consultation rounds through scientific calculation of symptom priority. Traditional intelligent Chinese medicine consultation methods often adopt the method of filling out a questionnaire. Users need to fill in dozens or even hundreds of questions to complete the collection of symptoms. The present solution utilizes the symptom priority calculation method to lock in key symptoms in a shorter time, reduce the number of patient consultations, and improve patient experience.

[0041] 3. Standardized consultation process

[0042] The present invention provides a standardized symptom inquiry process, which, combined with a priority calculation method, can avoid omissions in inquiries due to doctors' lack of experience or subjective factors, ensure the standardization and normalization of the consultation process, and improve the quality of medical services.

[0043] 4. Dynamically adjust weights

[0044] In the present invention, the weight values ​​between symptoms and diseases, and between symptoms are dynamically calculated, and can be adjusted in real time according to the current symptom selection status and the probability value of the disease, which can more flexibly reflect the changes in the actual condition and improve the sensitivity and applicability of the consultation.

[0045] 5. Comprehensive consideration of symptoms and disease

[0046] In the calculation of disease probability, the present invention not only considers the relationship between symptoms and disease, but also considers the impact of the disease nature on disease probability. Combining these two aspects of information can more comprehensively evaluate the possibility of disease and improve the comprehensiveness and accuracy of diagnosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0048] Figure 1 A flowchart of a method for calculating consultation priority based on a knowledge graph of TCM disease relationships is provided for an embodiment of the present disclosure. DETAILED DESCRIPTION

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

[0050] In recent years, knowledge graphs, as an emerging artificial intelligence technology, have shown broad application prospects in the medical field and other fields. Knowledge graphs can systematically organize and express knowledge, and can effectively help doctors quickly find relevant diagnostic evidence in massive amounts of medical information. However, in the field of traditional Chinese medicine, research on symptom analysis and diagnosis using knowledge graphs is still in its early stages, and a mature application system has not yet been formed.

[0051] Based on the above background, the inventor proposed a method for calculating consultation priority based on a knowledge graph of TCM disease relationships. By constructing a knowledge graph of TCM disease relationships and combining the user's symptom information, the priority of consultation is automatically calculated and determined, thereby improving the efficiency and accuracy of TCM diagnosis and providing patients with more timely and effective medical services. At the same time, this method can reduce the workload of doctors and provide strong support for the standardization and systematization of TCM diagnosis.

[0052] refer to Figure 1 The present disclosure provides a method for calculating the priority of consultation based on a knowledge graph of TCM disease relationships, comprising the following steps:

[0053] 1. Construct a knowledge graph of TCM disease relationships

[0054] Pre-construct a TCM disease relationship knowledge graph with symptoms, pathological properties and diseases as entities, and the relationships between symptoms, between symptoms and diseases, and between pathological properties and diseases as inter-entity relationships, where the inter-entity relationships are represented by weight values;

[0055] 2. Initialization phase

[0056] Initialize the priority values ​​of all symptoms to 0. This is done to uniformly process them during the calculation process and avoid interference from the initial values.

[0057] Initialize the weights between all symptoms and diseases, between symptoms, and between pathological characteristics and diseases. These weights are derived from the attribute values ​​set based on clinical data when constructing the TCM disease relationship knowledge graph. For example, the initial weight of general symptoms is set to 1, and some symptoms are more important in specific diseases, and their weights can be set to 5.

[0058] Set the search path length between symptoms and diseases and the search path length between symptoms to ensure that the association calculation between symptoms and diseases is within a reasonable range;

[0059] 3. User symptom input

[0060] Receive the symptom information currently input by the user, and judge the user's disease type, wherein the symptom information includes symptoms selected by the user and symptoms not selected by the user, for example, the user selects the "low back pain" symptom and does not select the "headache" symptom, and the disease type includes deficiency, excess, cold, heat, and normal state;

[0061] 4. Disease Probability Calculation

[0062] Get all user selected symptoms And these symptoms belong to the disease i;

[0063] Get all symptoms that the user has not selected And these symptoms belong to the disease i;

[0064] Get symptoms and the weight between disease i Sum these weight values. If the initial weight value is between 0 and 1, including 0, then set the weight value to 1.

[0065] Get symptoms and the weight between disease i Sum these weight values. If the initial weight value is between 0 and 1, excluding 0, the weight value is set to 0.

[0066] Get the weight values ​​w of all symptoms of disease i ds , these weight values ​​are summed up;

[0067] Get the weight value between the user's disease type and disease i Sum these weight values;

[0068] Get the weight value w of all pathologies dn , calculate the sum of these weight values;

[0069] Calculate the probability value P of disease i i , the specific formula is as follows:

[0070]

[0071] Among them, λ 1 and λ 2 is the preset specific gravity value, λ 1 The value is 0.9, 2 The value is 0.1;

[0072] 5. Symptom priority calculation

[0073] Traverse the symptoms k except those selected or not selected by the user, and obtain the initial weight values ​​between these symptoms k and disease i, and the initial weight values ​​between symptoms k and other symptoms, for example, the symptom of "weak waist and knees" and its related weight values;

[0074] Calculate the latest weight value between symptom k and disease i, and update the initial weight value to the latest weight value. The specific formula is as follows:

[0075]

[0076] in, is the initial weight value between symptom k and disease i, P i is the probability value of disease i. For example, the probability value of the disease "kidney yin deficiency" calculated in the previous step is 0.8, the probability value of "diabetes" is 0.5, and the initial weight value between the symptom of "weak waist and knees" and the diseases "kidney yin deficiency" and "diabetes" is 1. Then, through the above calculation, the latest weight value between the symptom of "weak waist and knees" and the disease "kidney yin deficiency" is 0.512, which is significantly higher than the latest weight value of 0.125 between the symptom of "weak waist and knees" and the disease "diabetes";

[0077] Calculate the latest weight value between symptom k and other symptoms, and update the initial weight value to the latest weight value. The specific formula is as follows:

[0078]

[0079] Among them, μ is a variable coefficient, which can be set as needed. is the initial weight value between symptom k and other symptoms;

[0080] Calculate the priority of symptom k j , the specific formula is as follows:

[0081]

[0082] in, and are the search path lengths between symptoms and diseases and the search path lengths between symptoms, respectively. The lengths can be set according to actual conditions. l is the number of diseases adjacent to symptom k. is the latest weight value between the disease adjacent to symptom k and symptom k, m is the number of symptoms selected by users adjacent to symptom k, The latest weight value between the symptom selected by the user adjacent to symptom k and symptom k;

[0083] Through the above steps, the priority of all symptoms can be calculated, and the symptom with the highest priority will be used as the symptom that the doctor will ask about first during the next consultation. For example, if the calculation result shows that the symptom of "weak waist and knees" has the highest priority, the symptom that will be asked about first next time will be "weak waist and knees".

[0084] Through the above implementation mode, the present invention effectively uses the TCM disease relationship knowledge graph to calculate the priority of symptoms. For example, when the user inputs the symptom of "low back pain", through the above steps, the symptom with the highest priority may be "weak waist and knees", then the doctor is advised to give priority to asking about the "weak waist and knees" symptom during the next consultation.

[0085] Although the present invention has been disclosed as above by way of embodiments, it is not intended to limit the present invention. Any person skilled in the art may make some changes and modifications without departing from the spirit and scope of the present invention. Therefore, the protection scope of the present invention shall be determined by the claims.

Claims

1. A method for calculating consultation priority based on a knowledge graph of TCM disease relationships, characterized in that: The following steps are involved: Pre-construct a TCM disease relationship knowledge graph with symptoms, pathological properties and diseases as entities, and the relationships between symptoms, between symptoms and diseases, and between pathological properties and diseases as inter-entity relationships, where the inter-entity relationships are represented by weight values; Initialize the priority values ​​of all symptoms to 0, initialize the weight values ​​between all symptoms and diseases, the weight values ​​between symptoms, and the weight values ​​between pathologies and diseases, and set the search path length between symptoms and diseases and the search path length between symptoms; Receive the symptom information currently input by the user, and judge the disease nature of the user, wherein the symptom information includes symptoms selected by the user and symptoms not selected by the user; Calculate the priority of each symptom based on the TCM disease relationship knowledge graph and the received user symptom information and disease nature; Make the highest priority symptoms the ones your doctor asks about first during your next visit.

2. The method for calculating consultation priority based on the TCM disease relationship knowledge graph according to claim 1 is characterized in that: The step of calculating the priority of each symptom based on the TCM disease relationship knowledge graph and the received user symptom information and disease nature includes: Obtain the weight values ​​between the received user symptoms and the disease and the weight values ​​between the user's pathology and the disease from the TCM disease relationship knowledge graph, and calculate the probability value of each disease based on the weight values ​​between the received user symptoms and the disease and the weight values ​​between the user's pathology and the disease; The priority of each symptom is calculated based on the probability value of each disease, the initial weight values ​​between the symptom and the disease, and the initial weight values ​​between the symptoms.

3. The method for calculating consultation priority based on the TCM disease relationship knowledge graph according to claim 2 is characterized in that: The method of obtaining the weight values ​​between the received user symptoms and the disease and the weight values ​​between the user's pathology and the disease from the TCM disease relationship knowledge graph, and calculating the probability value of each disease according to the weight values ​​between the received user symptoms and the disease and the weight values ​​between the user's pathology and the disease, includes: Get all user selected symptoms And these symptoms belong to the disease i; Get all symptoms that the user has not selected And these symptoms belong to the disease i; Get symptoms and the weight between disease i Sum these weight values; Get symptoms and the weight between disease i Sum these weight values; Get the weight value w of all symptoms of disease i ds , these weight values ​​are summed up; Get the weight value between the user's disease type and disease i Sum these weight values; Get the weight value w of all pathologies dn , calculate the sum of these weight values; Calculate the probability value P of disease i i , the specific formula is as follows: Among them, λ1 and λ2 are preset specific gravity values.

4. The method for calculating consultation priority based on the TCM disease relationship knowledge graph according to claim 3 is characterized in that: The step of calculating the priority of each symptom according to the probability value of each disease, the initial weight value between the symptom and the disease, and the initial weight value between the symptoms includes: Traverse the symptoms k except the symptoms selected or not selected by the user, and obtain the initial weight values ​​between these symptoms k and disease i, and the initial weight values ​​between symptoms k and other symptoms; Calculate the latest weight value between symptom k and disease i, and update the initial weight value to the latest weight value. The specific formula is as follows: in, is the initial weight value between symptom k and disease i, P i is the probability value of disease i; Calculate the latest weight value between symptom k and other symptoms, and update the initial weight value to the latest weight value. The specific formula is as follows: Among them, μ is a variable coefficient, which can be set as needed. is the initial weight value between symptom k and other symptoms; Calculate the priority of symptom k j , the specific formula is as follows: in, and are the search path lengths between symptoms and diseases and the search path lengths between symptoms, respectively. The lengths can be set according to actual conditions. l is the number of diseases adjacent to symptom k. is the latest weight value between the disease adjacent to symptom k and symptom k, m is the number of symptoms selected by users adjacent to symptom k, The latest weight value between the symptom selected by the user adjacent to symptom k and symptom k.

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