A method for constructing a knowledge graph database based on chronic disease diet management

Abstract

The present invention provides a method for constructing a knowledge graph database based on chronic disease diet management, which relates to the field of health management. The method includes: determining various types of chronic disease types; obtaining historical patient information of the k-th type of historical patients according to the k-th type of chronic disease type; determining a dietary health relationship function of the k-th type of chronic disease type according to the historical patient information of the k-th type of historical patients; determining various types of food names; obtaining the nutritional components of the i-th food according to the i-th type of food name; determining a food health score according to the nutritional components of the i-th food and the dietary health relationship function of the k-th type of chronic disease type; generating a knowledge graph database according to the food health score, food name, and chronic disease type. According to the present invention, the accuracy of generating the knowledge graph database can be improved.

Description

Technical Field

[0001] The present invention relates to the field of health management, and particularly to a method for constructing a knowledge graph database based on chronic disease diet management. Background Art

[0002] In the related art, CN109145119A discloses a knowledge graph construction device and a construction method in the field of health management. The knowledge graph construction device includes: a data processing unit connected to each other, configured to obtain a plurality of entities and generate relationships between the entities, where the plurality of entities include any combination of exercise entities, diet entities, sleep entities, and mental entities; a display unit configured to construct and display a knowledge graph according to the entities and the relationships between the entities, where the knowledge graph includes a plurality of nodes, and the knowledge graph is used to display the current node among the plurality of nodes, the remaining nodes related to the current node, and the relationships between the current node and the remaining nodes. The plurality of nodes respectively represent the displayed entities, and the connections between the plurality of nodes represent the relationships between the displayed entities. The above technical solution of this solution has the beneficial effects of rich content, wide coverage of the knowledge system, and high scalability.

[0003] CN114925216A discloses a health management method based on a knowledge graph. The method includes: obtaining a target keyword indicating the health management needs of a user; querying, in the knowledge graph, a target entity corresponding to the target keyword and other entities having relationships with the target entity; and outputting and displaying the queried target entity, the other entities, and the relationships between the target entity and the other entities as search results to the user.

[0004] Based on the above related technologies, the scalability of the knowledge graph can be improved. However, the related technologies do not analyze the specific relationships between entities, that is, they cannot analyze the specific relationships between entities and cannot establish a knowledge graph database based on the specific relationships between entities and the entities.

[0005] The information disclosed in the background art part of the present application is only intended to deepen the understanding of the general background art of the present application, and should not be regarded as an admission or any form of suggestion that this information constitutes the prior art known to those skilled in the art. Summary of the Invention

[0006] The present invention provides a method for constructing a knowledge graph database based on chronic disease diet management, which can solve the technical problems that the related technologies cannot analyze the specific relationships between entities and cannot establish a knowledge graph database based on the specific relationships between entities and the entities.

[0007] According to a first aspect of the present invention, there is provided a method for constructing a knowledge graph database based on chronic disease diet management, including:

[0008] Determine multiple types of chronic disease types;

[0009] According to the k-th type of chronic disease type, obtain the historical patient information of the k-th type of historical patients, where the historical patient information includes: patient diet information, patient health data, and the number of disease occurrences of the patient;

[0010] According to the historical patient information of the k-th type of historical patients, determine the diet-health relationship function of the k-th type of chronic disease type;

[0011] Determine multiple types of food names;

[0012] According to the i-th type of food name, obtain the nutritional components of the i-th food;

[0013] According to the nutritional components of the i-th food and the diet-health relationship function of the k-th type of chronic disease type, determine the food health score;

[0014] According to the food health score, the food name, and the chronic disease type, generate a knowledge graph database.

[0015] According to the second aspect of the present invention, there is provided a knowledge graph database construction system based on chronic disease diet management, including:

[0016] A chronic disease type module for determining multiple types of chronic disease types;

[0017] A patient information module for obtaining the historical patient information of the k-th type of historical patients according to the k-th type of chronic disease type, where the historical patient information includes: patient diet information, patient health data, and the number of disease occurrences of the patient;

[0018] A relationship function module for determining the diet-health relationship function of the k-th type of chronic disease type according to the historical patient information of the k-th type of historical patients;

[0019] A food name module for determining multiple types of food names;

[0020] A food information module for obtaining the nutritional components of the i-th food according to the i-th type of food name;

[0021] A food health module for determining the food health score according to the nutritional components of the i-th food and the diet-health relationship function of the k-th type of chronic disease type;

[0022] A database generation module for generating a knowledge graph database according to the food health score, the food name, and the chronic disease type.

[0023] Technical effects: According to the present invention, the relationship between the patient's dietary information and the patient's health data can be accurately analyzed, and based on this relationship, the impact of various foods on the patient's health can be determined. Further, based on the impact of various foods on the patient's health, the food names, and the types of chronic diseases, a knowledge graph database is generated, improving the accuracy of generating the knowledge graph database. When determining the patient's health score, the patient's health score can be determined according to the patient's blood glucose data, the standard deviation of the systolic blood pressure change rate, the standard deviation of the diastolic blood pressure change rate, the preset standard systolic blood pressure data, the preset standard blood glucose data, the patient's systolic blood pressure data, the patient's systolic blood pressure change rate, the patient's diastolic blood pressure change rate, and the number of patient disease occurrences. During the calculation process, the patient's blood pressure health status within the historical period can be determined according to the patient's systolic blood pressure status, the patient's systolic blood pressure change status, and the patient's diastolic blood pressure change status. Further, the patient's overall health status can be determined according to the patient's blood pressure health status, the patient's blood glucose health status, and the number of patient disease occurrences, improving the comprehensiveness and accuracy of the patient's health score. When determining the dietary health relationship function, the dietary health relationship function of the k-th type of chronic disease is determined according to the amount of food consumed by the patient, the nutritional components of the food consumed by the patient, and the patient's health score, accurately describing the impact relationship of the amount of food consumed by the patient and the nutritional components of the food consumed by the patient on the optimization degree of the patient's blood glucose health status and the optimization degree of the patient's blood pressure health status, improving the accuracy of the dietary health relationship function. When determining the food health score, the food health score of the i-th food regarding the k-th type of chronic disease can be determined according to the fat content, protein content, carbohydrate content, and vitamin content of the i-th food, and the dietary health relationship function of the k-th type of chronic disease, improving the accuracy of the food health score.

[0024] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and do not limit the present invention. According to the following detailed description of the exemplary embodiments with reference to the accompanying drawings, other features and aspects of the present invention will be clearer. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other embodiments can also be obtained based on these drawings;

[0026] Figure 1 Exemplarily shows a schematic flowchart of a method for constructing a knowledge graph database based on chronic disease diet management according to an embodiment of the present invention;

[0027] Figure 2A block diagram of a knowledge graph database construction system for chronic disease diet management according to an embodiment of the present invention is exemplarily shown. Detailed implementation manners

[0028] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are only a part rather than all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0029] The technical solutions of the present invention will be described in detail below with specific embodiments. These specific embodiments may be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments.

[0030] Figure 1 A flowchart of a method for constructing a knowledge graph database for chronic disease diet management according to an embodiment of the present invention is exemplarily shown. The method includes:

[0031] Step S101, determining multiple types of chronic disease types;

[0032] Step S102, according to the kth type of chronic disease type, obtaining historical patient information of the kth type of historical patients, where the historical patient information includes: patient diet information, patient health data, and patient disease occurrence times;

[0033] Step S103, according to the historical patient information of the kth type of historical patients, determining a diet-health relationship function for the kth type of chronic disease type;

[0034] Step S104, determining multiple types of food names;

[0035] Step S105, according to the ith type of food name, obtaining the nutritional components of the ith food;

[0036] Step S106, according to the nutritional components of the ith food and the diet-health relationship function of the kth type of chronic disease type, determining a food health score;

[0037] Step S107, generating a knowledge graph database according to the food health score, the food name, and the chronic disease type.

[0038] The method for constructing a knowledge graph database based on chronic disease diet management according to an embodiment of the present invention can accurately analyze the relationship between a patient's diet information and the patient's health data, and determine the impact of various foods on the patient's health based on this relationship. Further, based on the impact of various foods on the patient's health, the food names, and the types of chronic diseases, a knowledge graph database is generated, improving the accuracy of the generation of the knowledge graph database.

[0039] According to an embodiment of the present invention, in step S101, multiple types of chronic disease types are determined.

[0040] For example, according to a professional chronic disease management database and knowledge base, multiple types of chronic disease types are determined, such as hypertension and diabetes.

[0041] According to an embodiment of the present invention, in step S102, according to the k-th type of chronic disease type, historical patient information of the k-th type of historical patients is obtained, where the historical patient information includes: patient diet information, patient health data, and the number of patient disease occurrences.

[0042] For example, the k-th type of historical patients are patients suffering from the k-th type of chronic disease type, and the patient diet information, patient health data, and the number of patient disease occurrences (the number of occurrences of chronic disease complications) of patients suffering from the k-th type of chronic disease type in a historical time period are obtained through authoritative health institutions and medical institutions.

[0043] According to an embodiment of the present invention, in step S103, according to the historical patient information of the k-th type of historical patients, a diet-health relationship function of the k-th type of chronic disease type is determined.

[0044] According to an embodiment of the present invention, step S103 includes:

[0045] According to the patient diet information, determine the amount of food consumed by the patient and the nutritional components of the food consumed by the patient, where the nutritional components of the food consumed by the patient include: protein content, carbohydrate content, vitamin content, and fat content;

[0046] According to the patient health data, determine the patient's blood pressure data and the patient's blood glucose data;

[0047] According to the patient's blood pressure data, the patient's blood glucose data, and the number of patient disease occurrences, determine the patient's health score;

[0048] According to the amount of food consumed by the patient, the nutritional components of the food consumed by the patient, and the patient's health score, determine the diet-health relationship function of the k-th type of chronic disease type.

[0049] For example, based on the patient's dietary information, determine the amounts of various foods consumed by the chronic disease patient within a historical time period, that is, the amounts of foods consumed by the patient, and determine the nutritional components of various foods consumed by the chronic disease patient; obtain the patient's blood pressure data and patient's blood glucose data within the historical time period; evaluate the patient's health status within the historical period based on the patient's blood pressure data, the patient's blood glucose data, and the number of times the patient has fallen ill, and determine the patient's health score; the patient's health status is to a certain extent related to the patient's dietary status. For example, when the vitamin content of the consumed foods is relatively high, the patient's blood pressure and blood glucose conditions are also better. Based on the correlation of the above data, determine the dietary health relationship function between the patient's health score, the amounts of foods consumed by the patient, and the nutritional components of the foods consumed by the patient.

[0050] According to an embodiment of the present invention, determining the patient's health score based on the patient's blood pressure data, the patient's blood glucose data, and the number of times the patient has fallen ill includes:

[0051] Determine the preset standard high blood pressure data and the preset standard blood glucose data according to the kth type of chronic disease type;

[0052] Determine the patient's high blood pressure data and the patient's low blood pressure data according to the patient's blood pressure data;

[0053] Fit according to the patient's high blood pressure data and the moments in the historical time period to obtain the high blood pressure data function of the patient's high blood pressure data in the historical time period;

[0054] Determine the derivative function of the high blood pressure data according to the high blood pressure data function;

[0055] Determine the patient's high blood pressure change rate at multiple moments in the historical time period according to the derivative function of the high blood pressure data;

[0056] Determine the standard deviation of the high blood pressure change rate according to the patient's high blood pressure change rate at multiple moments in the historical time period;

[0057] Fit according to the patient's low blood pressure data and the moments in the historical time period to obtain the low blood pressure data function of the patient's low blood pressure data in the historical time period;

[0058] Determine the derivative function of the low blood pressure data according to the low blood pressure data function;

[0059] Determine the patient's low blood pressure change rate at multiple moments in the historical time period according to the derivative function of the low blood pressure data;

[0060] Determine the standard deviation of the low blood pressure change rate according to the patient's low blood pressure change rate at multiple moments in the historical time period;

[0061] Determine the patient health score based on the patient blood glucose data, the standard deviation of the high blood pressure change rate, the standard deviation of the low blood pressure change rate, the preset standard high blood pressure data, the preset standard blood glucose data, the patient high blood pressure data, the patient high blood pressure change rate, the patient low blood pressure change rate, and the number of patient disease occurrences.

[0062] For example, according to the k-th type of chronic disease type, determine the preset standard high blood pressure data and the preset standard blood glucose data. For example, when the k-th type of chronic disease type is diabetes, the preset standard blood glucose data is 7 mmol / L; when the k-th type of chronic disease type is not diabetes, the preset standard blood glucose data is 4 mmol / L; when the k-th type of chronic disease type is not hypertension, the preset standard high blood pressure data is 120 mmHg; when the k-th type of chronic disease type is hypertension, the preset standard high blood pressure data is 140 mmHg. Determine the patient high blood pressure data and the patient low blood pressure data of the patient in the historical time period. Fit the patient high blood pressure data and the moments in the historical time period to obtain a high blood pressure data function for describing the law of change of the patient high blood pressure data over time in the historical time period. Take the derivative of the high blood pressure data function to determine the high blood pressure derivative function. Substitute the moments in the historical time period into the high blood pressure derivative function to determine the patient high blood pressure change rates at multiple moments in the historical time period. Calculate the standard deviation of the high blood pressure change rate of the patient high blood pressure change rates based on the patient high blood pressure change rates at multiple moments in the historical time period. Fit the patient low blood pressure data and the moments in the historical time period to obtain a low blood pressure data function for describing the law of change of the patient low blood pressure data over time in the historical time period. Take the derivative of the low blood pressure data function to determine the low blood pressure derivative function. Substitute the moments in the historical time period into the low blood pressure derivative function to determine the patient low blood pressure change rates at multiple moments in the historical time period. Calculate the standard deviation of the low blood pressure change rate of the patient low blood pressure change rates based on the patient low blood pressure change rates at multiple moments in the historical time period. Evaluate the patient's health status based on the patient blood glucose data, the standard deviation of the high blood pressure change rate, the standard deviation of the low blood pressure change rate, the preset standard high blood pressure data, the preset standard blood glucose data, the patient high blood pressure data, the patient high blood pressure change rate, the patient low blood pressure change rate, and the number of patient disease occurrences, and determine the patient health score.

[0063] According to an embodiment of the present invention, determining the patient health score based on the patient blood glucose data, the standard deviation of the high blood pressure change rate, the standard deviation of the low blood pressure change rate, the preset standard high blood pressure data, the preset standard blood glucose data, the patient high blood pressure data, the patient high blood pressure change rate, the patient low blood pressure change rate, and the number of patient disease occurrences includes: determining the patient health score of the e-th historical patient of the k-th type of chronic disease type in the j-th historical time period according to formula (1) ,

[0064] (1) Among them, and are preset weight values, is a preset multiple, if is a conditional function, max is a function to take the maximum value, is the preset standard high-pressure data, is the high-pressure data of the e-th historical patient at the r-th moment in the j-th historical time period, is the blood glucose data of the e-th historical patient at the r-th moment in the j-th historical time period, is the preset standard blood glucose data, is the r-th moment of the historical time period, is the high-pressure change rate of the e-th historical patient at the r-th moment in the j-th historical time period, is the standard deviation of the high-pressure change rate of the e-th historical patient in the j-th historical time period, is the low-pressure change rate of the e-th historical patient at the r-th moment in the j-th historical time period, is the standard deviation of the low-pressure change rate of the e-th historical patient in the j-th historical time period, is the number of disease occurrences of the e-th historical patient in the j-th historical time period, R is the number of moments in the historical time period, r ≤ R, and both r and R are positive integers.

[0065] According to an embodiment of the present invention, is to take the maximum value of the high-pressure data of the e-th historical patient at the R moments in the j-th historical time period. The above processing of taking the maximum value can be used to determine whether the e-th historical patient has a condition of excessive blood pressure in the j-th historical time period. is the relative difference between the preset standard high-pressure data and the maximum value of the high-pressure data of the e-th historical patient in the j-th historical time period. The larger this ratio is, the smaller the maximum value of the high-pressure data of the e-th historical patient in the j-th historical time period is relatively, the smaller the possibility that the patient has a condition of excessive blood pressure, and the better the blood pressure health condition of the patient.

[0066] According to an embodiment of the present invention, in formula (1), the following two situations can be represented in the form of a conditional function. When the condition of is satisfied, it means that the high-pressure change rate of the e-th historical patient at the r-th moment in the j-th historical time period is centered on the average high-pressure change rate of patients and is at twice the preset multiple (for example, the preset multiple Within the interval where the standard deviation of (3) is the interval length, the value of the conditional function is 1, indicating that the deviation between the patient's high blood pressure change rate and the mean value at the r-th moment in the j-th historical time period of the e-th historical patient is small, so the patient's high blood pressure change is relatively stable at the r-th moment. Otherwise, the deviation between the patient's high blood pressure change rate and the mean value at the r-th moment in the j-th historical time period of the e-th historical patient is large, so the patient's high blood pressure change is relatively abnormal. is the ratio of the number of moments when the patient's high blood pressure change is relatively stable to the number of moments in the historical time period. The larger this ratio is, the relatively more stable the high blood pressure change of the e-th historical patient in the j-th historical time period is, and the better the patient's blood pressure health condition is.

[0067] According to an embodiment of the present invention, in formula (1), the following two situations can be represented in the form of a conditional function. When the condition is met, it indicates that the patient's low blood pressure change rate at the r-th moment in the j-th historical time period of the e-th historical patient is within the interval centered on the average patient low blood pressure change rate and with a standard deviation of twice the preset multiple (e.g., the preset multiple is 3). The value of the conditional function is 1, indicating that the deviation between the patient's low blood pressure change rate and the mean value at the r-th moment in the j-th historical time period of the e-th historical patient is small, so the patient's low blood pressure change is relatively stable at the r-th moment. Otherwise, the deviation between the patient's low blood pressure change rate and the mean value at the r-th moment in the j-th historical time period of the e-th historical patient is large, so the patient's low blood pressure change is relatively abnormal. is the ratio of the number of moments when the patient's low blood pressure change is relatively stable to the number of moments in the historical time period. The larger this ratio is, the relatively more stable the low blood pressure change of the e-th historical patient in the j-th historical time period is, and the better the patient's blood pressure health condition is.

[0068] According to an embodiment of the present invention, represents determining the patient's blood pressure health condition in the historical period based on the patient's high blood pressure condition, the patient's high blood pressure change condition, and the patient's low blood pressure change condition.

[0069] According to an embodiment of the present invention, is the relative difference between the patient's blood glucose data and the preset standard blood glucose data at the r-th moment in the j-th historical time period of the e-th historical patient. The smaller this ratio is, the closer the patient's blood glucose data at the r-th moment is to the preset standard blood glucose data, and the better the patient's blood glucose health condition is. is to calculate the average value according to the number of moments in the historical time period. The larger this average value is, the better the patient's blood glucose health condition in the historical time period is.

[0070] According to an embodiment of the present invention, Determine the overall health status of a patient based on the patient's blood pressure health status, blood sugar health status, and the number of disease occurrences of the patient. Among them, and are preset weights determined according to the type of chronic disease of the patient. For example, when the type of chronic disease is hypertension, , such as, , , indicating that the blood pressure health status has a greater impact on the overall health status of the patient. When the type of chronic disease is diabetes, , such as, , , indicating that the blood sugar health status has a greater impact on the overall health status of the patient.

[0071] In this way, based on the patient's blood sugar data, standard deviation of systolic blood pressure change rate, standard deviation of diastolic blood pressure change rate, preset standard systolic blood pressure data, preset standard blood sugar data, patient's systolic blood pressure data, patient's systolic blood pressure change rate, patient's diastolic blood pressure change rate, and the number of disease occurrences of the patient, the health score of the patient can be determined. During the calculation process, the blood pressure health status of the patient within the historical period can be determined according to the patient's systolic blood pressure status, patient's systolic blood pressure change status, and patient's diastolic blood pressure change status. Further, based on the patient's blood pressure health status, blood sugar health status, and the number of disease occurrences of the patient, the overall health status of the patient can be determined, improving the comprehensiveness and accuracy of the patient's health score.

[0072] According to an embodiment of the present invention, determine the dietary health relationship function of the k-th type of chronic disease based on the amount of food consumed by the patient, the nutritional components of the food consumed by the patient, and the health score of the patient, including: determining the first undetermined coefficient equation of the dietary health relationship function of the k-th type of chronic disease according to formula (2),

[0073] (2) Wherein, and are preset weights, is the health score of the e-th historical patient of the k-th type of chronic disease in the j-th historical time period, is the health score of the e-th historical patient of the k-th type of chronic disease in the (j - 1)-th historical time period, is the amount of food consumed by the patient of the q-th type of food in the j-th historical time period, is the protein content of the q-th type of food consumed by the patient, is the carbohydrate content of the q-th type of food consumed by the patient, is the vitamin content of the q-th type of food consumed by the patient, is the fat content of the q-th type of food consumed by the patient, , , , , , , , , , , and are the first undetermined coefficients of the first undetermined coefficient equation, Q is the number of types of foods consumed by the patient, q ≤ Q, and both q and Q are positive integers;

[0074] Solve the first undetermined coefficient according to the amount of food consumed by the patient, the diet composition of the patient, and the health score of the patient to obtain the solution value of the first undetermined coefficient;

[0075] Determine the diet-health relationship function of the k-th type of chronic disease according to the solution value of the first undetermined coefficient and the first undetermined coefficient equation.

[0076] According to an embodiment of the present invention, is the difference in the health scores of the e-th historical patient of the k-th type of chronic disease between the j-th historical time period and the j-1-th historical time period, indicating the degree of optimization of the health status of the e-th historical patient.

[0077] According to an embodiment of the present invention, represents the part that shows the influence of the carbohydrate content, vitamin content, and fat content ingested by the patient on the blood pressure health status of the patient's health status, represents the total amount of vitamins ingested by the patient in the j-th historical time period, represents the average vitamin content of all foods ingested by the patient in the j-th historical time period, represents the total amount of carbohydrates ingested by the patient in the j-th historical time period, represents the average carbohydrate content of all foods ingested by the patient in the j-th historical time period, represents the total amount of fat ingested by the patient in the j-th historical time period, represents the average fat content of all foods ingested by the patient in the j-th historical time period, represents that the average vitamin content of all foods ingested by the patient in the j-th historical time period has a positive correlation with the degree of optimization of the patient's blood pressure health status. For example, antioxidants such as vitamin C can protect the blood vessel wall, reduce vascular inflammation, help lower blood pressure, and maintain blood pressure stability, represents that the average carbohydrate content of all foods ingested by the patient in the j-th historical time period has a negative correlation with the degree of optimization of the patient's blood pressure health status. For example, a long-term high-carbohydrate diet may lead to weight gain, which in turn increases the risk of hypertension, It indicates that there is a negative correlation between the average fat content of all foods consumed by the patient in the j - th historical time period and the degree of optimization of the patient's blood pressure health condition. For example, saturated fatty acids and trans - fatty acids in dietary fat can cause blood pressure to increase.

[0078] According to an embodiment of the present invention, It represents the part that shows the influence of the carbohydrate content, vitamin content, and protein content consumed by the patient on the patient's blood - sugar health condition among the patient's health conditions. It represents the protein content consumed by the patient in the j - th historical time period. It represents the average protein content of all foods consumed by the patient in the j - th historical time period. It indicates that there is a positive correlation between the average vitamin content of all foods consumed by the patient in the j - th historical time period and the degree of optimization of the patient's blood - sugar health condition. For example, vitamin C can promote sugar metabolism and help maintain blood - sugar stability. It indicates that there is a negative correlation between the average protein content of all foods consumed by the patient in the j - th historical time period and the degree of optimization of the patient's blood - sugar health condition. For example, when excessive protein is ingested, it may cause blood - sugar to rise. It indicates that there is a negative correlation between the average carbohydrate content of all foods consumed by the patient in the j - th historical time period and the degree of optimization of the patient's blood - sugar health condition. For example, excessive carbohydrate intake can cause blood - sugar to rise rapidly. Based on the above relationships, the first undetermined - coefficient equation of the dietary - health relationship function of the k - th type of chronic - disease type can be obtained.

[0079] According to an embodiment of the present invention, fitting can be performed based on multiple parameters involved in the above - mentioned first undetermined - coefficient equation, that is, fitting is performed based on the amount of food consumed by the patient, the nutritional components of the food consumed by the patient, and the patient's health score, and the above - mentioned multiple first undetermined coefficients are solved. There are 12 first undetermined coefficients, namely, , , , , , , , , , , and , and the above - mentioned 12 first undetermined coefficients are solved according to the amount of food consumed by the patient, the nutritional components of the food consumed by the patient, and the patient's health score of at least 12 patients, and the solved values of the above - mentioned 12 first undetermined coefficients are obtained and substituted into the first undetermined - coefficient equation to determine the dietary - health relationship function of the k - th type of chronic - disease type.

[0080] In this way, according to the amount of food consumed by the patient, the nutritional components of the food consumed by the patient, and the health score of the patient, the dietary health relationship function of the k-th type of chronic disease is determined, which accurately describes the influence relationship between the amount of food consumed by the patient and the nutritional components of the food consumed by the patient on the optimization degree of the patient's blood glucose health status and the optimization degree of the patient's blood pressure health status, and improves the accuracy of the dietary health relationship function.

[0081] According to an embodiment of the present invention, in step S104, multiple types of food names are determined.

[0082] For example, multiple types of common food names are obtained, such as potatoes and tomatoes.

[0083] According to an embodiment of the present invention, in step S105, according to the name of the i-th type of food, the nutritional components of the i-th food are obtained.

[0084] For example, according to the food name, the nutritional components of the food are queried in a professional nutritional data website.

[0085] According to an embodiment of the present invention, in step S106, according to the nutritional components of the i-th food and the dietary health relationship function of the k-th type of chronic disease, the food health score is determined.

[0086] According to an embodiment of the present invention, step S106 includes:

[0087] According to the nutritional components of the i-th food, the fat content, protein content, carbohydrate content, and vitamin content of the i-th food are determined;

[0088] According to the fat content, protein content, carbohydrate content, and vitamin content of the i-th food, and the dietary health relationship function of the k-th type of chronic disease, the food health score of the i-th food with respect to the k-th type of chronic disease is determined.

[0089] For example, according to a professional nutritional data website, the fat content, protein content, carbohydrate content, and vitamin content of the i-th food are queried; the fat content, protein content, carbohydrate content, and vitamin content of the i-th food are substituted into the dietary health relationship function of the k-th type of chronic disease to evaluate the influence of consuming the i-th food on the health status of patients with the k-th type of chronic disease, and the food health score of the i-th food with respect to the k-th type of chronic disease is determined.

[0090] According to an embodiment of the present invention, based on the fat content, protein content, carbohydrate content, and vitamin content of the \(i\)th food, and the diet-health relationship function of the \(k\)th type of chronic disease, determining the food health score of the \(i\)th food with respect to the \(k\)th type of chronic disease includes: determining the food health score of the \(i\)th food with respect to the \(k\)th type of chronic disease according to formula (3). ,

[0091] (3)

[0092] wherein, and are preset weights, is the protein content of the \(i\)th food, is the carbohydrate content of the \(i\)th food, is the vitamin content of the \(i\)th food, is the fat content of the \(i\)th food, is the solution value of, is the solution value of, is the solution value of, is the solution value of, is the solution value of, is the solution value of, is the solution value of, is the solution value of, is the solution value of, is the solution value of, is the solution value of, is the solution value of.

[0093] According to an embodiment of the present invention, substituting the fat content, protein content, carbohydrate content, and vitamin content of the \(i\)th food into the diet-health relationship function of the \(k\)th type of chronic disease, formula (3) is obtained, thereby obtaining the food health score of the \(i\)th food with respect to the \(k\)th type of chronic disease.

[0094] In this way, according to the fat content, protein content, carbohydrate content, and vitamin content of the \(i\)th food, and the diet-health relationship function of the \(k\)th type of chronic disease, the food health score of the \(i\)th food with respect to the \(k\)th type of chronic disease can be determined, and the accuracy of the food health score can be improved.

[0095] According to an embodiment of the present invention, in step S107, a knowledge graph database is generated based on the food health score, the food name, and the chronic disease type.

[0096] According to an embodiment of the present invention, step S107 includes:

[0097] Determine the relationship between the food and the chronic disease type according to the food health score and the set food health score threshold;

[0098] Generate knowledge graph database entities based on the food name and the chronic disease type;

[0099] Generate a knowledge graph database based on the knowledge graph database entities and the relationship between the food and the chronic disease type.

[0100] For example, set the food health score threshold to , if the food health score of the i-th food for the k-th type of chronic disease is greater than , it means that consuming the i-th food is beneficial to the recovery of the k-th type of chronic disease, and the relationship between the i-th food and the k-th type of chronic disease is "beneficial". If the food health score of the i-th food for the k-th type of chronic disease is less than or equal to , it means that consuming the i-th food is not beneficial to the recovery of the k-th type of chronic disease, and the relationship between the i-th food and the k-th type of chronic disease is "not beneficial"; identify the food name and the chronic disease type as entities through named entity recognition technology; select the storage method of the knowledge graph (such as a graph database), import the relationship between the entities into the graph database, optimize the data in the knowledge graph, and regularly update the data in the knowledge graph to maintain its accuracy and integrity.

[0101] The method for constructing a knowledge graph database based on chronic disease diet management according to an embodiment of the present invention can accurately analyze the relationship between the patient's diet information and the patient's health data, and determine the impact of various foods on the patient's health based on this relationship. Further, based on the impact of various foods on the patient's health, the food names, and the types of chronic diseases, a knowledge graph database is generated, improving the accuracy of generating the knowledge graph database. When determining the patient's health score, the patient's health score can be determined according to the patient's blood glucose data, the standard deviation of the systolic blood pressure change rate, the standard deviation of the diastolic blood pressure change rate, the preset standard systolic blood pressure data, the preset standard blood glucose data, the patient's systolic blood pressure data, the patient's systolic blood pressure change rate, the patient's diastolic blood pressure change rate, and the number of patient disease occurrences. During the calculation process, the patient's blood pressure health status within the historical period can be determined according to the patient's systolic blood pressure status, the patient's systolic blood pressure change status, and the patient's diastolic blood pressure change status. Further, according to the patient's blood pressure health status, the patient's blood glucose health status, and the number of patient disease occurrences, the patient's overall health status is determined, improving the comprehensiveness and accuracy of the patient's health score. When determining the diet-health relationship function, according to the amount of food consumed by the patient, the nutritional components of the food consumed by the patient, and the patient's health score, the diet-health relationship function of the k-th type of chronic disease is determined, accurately describing the impact relationship of the amount of food consumed by the patient and the nutritional components of the food consumed by the patient on the optimization degree of the patient's blood glucose health status and the optimization degree of the patient's blood pressure health status, improving the accuracy of the diet-health relationship function. When determining the food health score, the food health score of the i-th food for the k-th type of chronic disease can be determined according to the fat content, protein content, carbohydrate content, and vitamin content of the i-th food, and the diet-health relationship function of the k-th type of chronic disease, improving the accuracy of the food health score.

[0102] Figure 2 Exemplarily shown is a block diagram of a knowledge graph database construction system based on chronic disease diet management according to an embodiment of the present invention. The system includes:

[0103] A chronic disease type module for determining multiple types of chronic disease types;

[0104] A patient information module for obtaining historical patient information of the k-th type of historical patients according to the k-th type of chronic disease type, where the historical patient information includes: patient diet information, patient health data, and the number of patient disease occurrences;

[0105] A relationship function module for determining a diet-health relationship function of the k-th type of chronic disease type according to the historical patient information of the k-th type of historical patients;

[0106] A food name module for determining multiple types of food names;

[0107] A food information module for obtaining the nutritional components of the i-th type of food according to the name of the i-th type of food;

[0108] A food health module for determining a food health score according to the nutritional components of the i-th type of food and the dietary health relationship function of the k-th type of chronic disease;

[0109] A database generation module for generating a knowledge graph database according to the food health score, the food name, and the chronic disease type.

[0110] The present invention may be a method, an apparatus, a system, and / or a computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions thereon for performing various aspects of the present invention.

[0111] Those skilled in the art should understand that the embodiments of the present invention described above and shown in the accompanying drawings are only examples and do not limit the present invention. The object of the present invention has been fully and effectively achieved. The functions and structural principles of the present invention have been shown and described in the embodiments, and the embodiments of the present invention may have any deformation or modification without departing from the principle.

Claims

1. A method for constructing a knowledge graph database based on chronic disease diet management, characterized in that, Including: Determine multiple types of chronic disease types; According to the k-th type of chronic disease type, obtain the historical patient information of the k-th type of historical patients, where the historical patient information includes: patient diet information, patient health data, and patient disease occurrence times; According to the historical patient information of the k-th type of historical patients, determine the diet-health relationship function of the k-th type of chronic disease type; including: According to the formula Determine the first undetermined coefficient equation of the dietary health relationship function for the k-th type of chronic disease, where α1 and α2 are preset weights, and PH k,e,j is the patient health score of the e-th historical patient of the k-th type of chronic disease in the j-th historical time period, and PH k,e,j-1 is the patient health score of the e-th historical patient of the k-th type of chronic disease in the (j - 1)-th historical time period, and CA q,j is the amount of food consumed by the q-th patient in the j-th historical time period, and Pr q is the protein content of the food consumed by the q-th patient, and Cw q is the carbohydrate content of the food consumed by the q-th patient, and Vit q is the vitamin content of the food consumed by the q-th patient, and Fa q is the fat content of the food consumed by the q-th patient, and β1, β2, β3, β4, β5, β6, β7, β8, β9, β 10 and β 11 and β 12 are the first undetermined coefficients of the first undetermined coefficient equation, Q is the number of types of food consumed by the patient, q ≤ Q, and both q and Q are positive integers; Solve for the first undetermined coefficient based on the patient's food intake, the patient's diet composition, and the patient's health score, and obtain the solution value of the first undetermined coefficient; Determine the diet-health relationship function of the k-th type of chronic disease type according to the solution value of the first undetermined coefficient and the first undetermined coefficient equation; Among them, the patient health score includes: According to the formula Determine the patient health score PH of the e-th historical patient of the k-th type of chronic disease in the j-th historical time period k,e,j , where α1 and α2 are preset weights, θ is a preset multiple, if is a conditional function, max is a function to take the maximum value, Hvd T is the preset standard high blood pressure data, Hvd e,j,r is the patient's high blood pressure data at the r-th moment in the j-th historical time period of the e-th historical patient, Bsa e,j,r is the patient's blood glucose data at the r-th moment in the j-th historical time period of the e-th historical patient, Bsa T is the preset standard blood glucose data, t r is the r-th moment of the historical time period, Hvd’ e,j (t r ) is the high blood pressure change rate of the e-th historical patient at the r-th moment in the j-th historical time period, D H,e,j is the standard deviation of the high blood pressure change rate of the e-th historical patient in the j-th historical time period, Lvd’ e,j (t r ) is the low blood pressure change rate of the e-th historical patient at the r-th moment in the j-th historical time period, L H,e,j is the standard deviation of the low blood pressure change rate of the e-th historical patient in the j-th historical time period, Cf e,j is the number of disease occurrences of the e-th historical patient in the j-th historical time period, R is the number of moments in the historical time period, r ≤ R, and both r and R are positive integers; Determine multiple types of food names; According to the i-th type of food name, obtain the nutritional components of the i-th food; Determine the food health score according to the nutritional components of the i-th food and the diet-health relationship function of the k-th type of chronic disease type; including: According to the formula Determine the food health score Fh of the i-th food for the k-th type of chronic disease i,k , where α1 and α2 are preset weights, Pr i is the protein content of the i-th food, Cw i is the carbohydrate content of the i-th food, Vit i is the vitamin content of the i-th food, Fa i is the fat content of the i-th food, β 1,F is the solution value of β1, β 2,F is the solution value of β2, β 3,F is the solution value of β3, β 4,F is the solution value of β4, β 5,F is the solution value of β5, β 6,F is the solution value of β6, β 7,F is the solution value of β7, β 9,F is the solution value of β8, β 9,F is the solution value of β9, β 10,F is the solution value of β 10 is the solution value of β 11,F is the solution value of β 11 is the solution value of β 12,F is the solution value of β 12 is the solution value; Generate a knowledge graph database according to the food health score, the food name, and the chronic disease type.

2. The method for constructing a knowledge graph database based on chronic disease diet management according to claim 1, wherein Determine the diet-health relationship function of the k-th type of chronic disease type according to the historical patient information of the k-th type of historical patients, including: Determine the patient's food intake and the nutritional components of the food consumed by the patient according to the patient's diet information, where the nutritional components of the food consumed by the patient include: protein content, carbohydrate content, vitamin content, and fat content; Determine the patient's blood pressure data and the patient's blood sugar data according to the patient's health data; Determine the patient's health score according to the patient's blood pressure data, the patient's blood sugar data, and the patient's disease occurrence times; Determine the diet-health relationship function of the k-th type of chronic disease type according to the patient's food intake, the nutritional components of the food consumed by the patient, and the patient's health score.

3. The method for constructing a knowledge graph database based on chronic disease diet management according to claim 2, wherein Determine the patient's health score according to the patient's blood pressure data, the patient's blood sugar data, and the patient's disease occurrence times, including: Determine the preset standard high blood pressure data and the preset standard blood sugar data according to the k-th type of chronic disease type; Determine the patient's high blood pressure data and the patient's low blood pressure data according to the patient's blood pressure data; Fit the patient's high blood pressure data in the historical time period according to the patient's high blood pressure data and the moments in the historical time period, and obtain the high blood pressure data function of the patient's high blood pressure data in the historical time period; Determine the high blood pressure data derivative function according to the high blood pressure data function; Determine the patient's high blood pressure change rate at multiple moments in the historical time period according to the high blood pressure data derivative function; Determine the standard deviation of the high blood pressure change rate according to the patient's high blood pressure change rate at multiple moments in the historical time period; Fit the patient's low blood pressure data in the historical time period according to the patient's low blood pressure data and the moments in the historical time period, and obtain the low blood pressure data function of the patient's low blood pressure data in the historical time period; Determine the low blood pressure data derivative function according to the low blood pressure data function; Determine the patient's low blood pressure change rate at multiple moments in the historical time period according to the low blood pressure data derivative function; Determine the standard deviation of the low blood pressure change rate according to the low blood pressure change rates of multiple moments in the historical time period; Determine the patient health score according to the patient blood glucose data, the standard deviation of the high blood pressure change rate, the standard deviation of the low blood pressure change rate, the preset standard high blood pressure data, the preset standard blood glucose data, the patient high blood pressure data, the patient high blood pressure change rate, the patient low blood pressure change rate, and the number of patient disease occurrences; 4. The method for constructing a knowledge graph database based on chronic disease diet management according to claim 3, wherein Determine the food health score according to the nutritional components of the i-th food and the dietary health relationship function of the k-th type of chronic disease type, including: Determine the fat content, protein content, carbohydrate content, and vitamin content of the i-th food according to the nutritional components of the i-th food; Determine the food health score of the i-th food regarding the k-th type of chronic disease type according to the fat content, protein content, carbohydrate content, and vitamin content of the i-th food, and the dietary health relationship function of the k-th type of chronic disease type; 5. The method for constructing a knowledge graph database based on chronic disease diet management according to claim 4, wherein Generate a knowledge graph database according to the food health score, the food name, and the chronic disease type, including: Determine the relationship between the food and the chronic disease type according to the food health score and the set food health score threshold; Generate a knowledge graph database entity according to the food name and the chronic disease type; Generate a knowledge graph database according to the knowledge graph database entity and the relationship between the food and the chronic disease type; 6. A construction system for a knowledge graph database based on the construction method of any one of claims 1 to 5 for chronic disease diet management, characterized in that, Including: A chronic disease type module for determining multiple types of chronic disease types; A patient information module for obtaining the historical patient information of the k-th type of historical patients according to the k-th type of chronic disease type, where the historical patient information includes: patient diet information, patient health data, and the number of patient disease occurrences; A relationship function module for determining the dietary health relationship function of the k-th type of chronic disease type according to the historical patient information of the k-th type of historical patients; A food name module for determining multiple types of food names; A food information module for obtaining the nutritional components of the i-th food according to the i-th type of food name; A food health module for determining the food health score according to the nutritional components of the i-th food and the dietary health relationship function of the k-th type of chronic disease type; A database generation module for generating a knowledge graph database according to the food health score, the food name, and the chronic disease type.

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