Following type chronic disease management system based on Internet technology

Abstract

The invention discloses a following type chronic disease management system based on an internet technology, and relates to the technical field of data processing. The system comprises: a chronic disease acquisition module for acquiring a target chronic disease of a target patient and a first reference chronic disease of a first reference patient; the region determination module is used for determining a main disease region of the target chronic disease based on the first reference chronic disease of each first reference patient and the region to which each first reference patient belongs; the chronic disease analysis module is used for determining disease quantity similarity and disease matching factors of the target patient and the second reference patients based on the target chronic disease and the second reference chronic diseases of the second reference patients; and the follow-up visit frequency determination module is used for determining the first follow-up visit frequency of the target patient in the preset time period based on the similarity of the disease quantities, the disease matching factors and the reference follow-up visit frequency of the second reference patients in the preset time period. The accuracy of the follow-up visit times of the target patient can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly to a follow-up chronic disease management system based on Internet technology. Background Art

[0002] Chronic diseases, simply referred to as chronic diseases, are a class of diseases with a long course and a relatively gentle development trend. Given the significant differences in different types of chronic diseases and the disease development trajectories presented by the same chronic disease in different patient individuals, the implementation of scientific and effective management of chronic diseases requires long-term and continuous disease monitoring. The core point of accurately controlling chronic disease management lies in tailoring personalized follow-up frequencies for patients based on their real-time health status and the specific progression of the disease.

[0003] Currently, in actual operation, individual characteristic information such as the age, gender, and past medical history of patients is usually collected first, and these personalized data are deeply analyzed with the help of a machine learning model to predict the health trend of patients, and based on this, the theoretically appropriate follow-up frequency is calculated for each patient.

[0004] However, this method has obvious limitations. It only focuses on the basic characteristics of individual patients, which directly leads to poor accuracy of the calculated follow-up frequencies of patients. Summary of the Invention

[0005] An embodiment of the present invention provides a follow-up chronic disease management system based on Internet technology, which can effectively improve the accuracy of the follow-up frequency of target patients.

[0006] In the first aspect of the embodiment of the present invention, a follow-up chronic disease management system based on Internet technology is provided, including: A chronic disease acquisition module for acquiring the target chronic disease of a target patient and the first reference chronic disease of a first reference patient; A region determination module for determining the main incidence region of the target chronic disease based on the first reference chronic diseases of each first reference patient and the region to which each first reference patient belongs; A chronic disease analysis module for determining the similarity of the number of diseases and the disease matching factor between the target patient and each second reference patient based on the target chronic disease of the target patient and the second reference chronic diseases of each second reference patient, where the second reference patient is a first reference patient whose region belongs to the main incidence region of the target chronic disease; A follow-up frequency determination module for determining the first follow-up frequency of the target patient within a preset time period based on each similarity of the number of diseases, each disease matching factor, and the reference follow-up frequencies of each second reference patient within the preset time period.

[0007] In some possible implementation manners, the chronic disease analysis module specifically includes: A quantity similarity determination unit, configured to determine the disease quantity similarity between the target patient and the target second reference patient by using the quantity of the first same chronic disease in the target chronic disease of the target patient and the target second reference chronic disease of the target second reference patient, where the target second reference patient is any one of the second reference patients; A disease similarity determination unit, configured to compare each target chronic disease with each target second reference chronic disease respectively to obtain the disease similarity between each target chronic disease and each target second reference chronic disease; A matching factor determination unit, configured to determine the disease matching factor between the target patient and the target second reference patient by using each disease similarity.

[0008] In some possible implementation manners, the disease similarity determination unit specifically is configured to: Obtain the first disease description information of the target chronic disease and the second disease description information of the target second reference chronic disease; Perform word segmentation processing on the first disease description information and the second disease description information respectively to obtain a first word segmentation set and a second word segmentation set; Calculate the similarity between the first word segmentation set and the second word segmentation set to obtain the disease similarity between the target chronic disease and the target second reference chronic disease.

[0009] In some possible implementation manners, the follow-up frequency determination module specifically is configured to: Respectively determine the disease matching degree between the target patient and each second reference patient by using each disease quantity similarity and each disease matching factor; Determine the first follow-up frequency of the target patient within a preset time period by using each disease matching degree and each reference follow-up frequency.

[0010] In some possible implementation manners, the region determination module specifically is configured to: Based on the first reference chronic diseases of each first reference patient and the regions to which each first reference patient belongs, determine the quantity of the second same chronic diseases in each region, where the quantity of the second same chronic diseases is the quantity of the first reference chronic diseases that are the same as the target chronic disease in the region; Respectively determine the reference value of each region by using the quantity of the second same chronic diseases in each region; Determine the region with the largest reference value as the main disease incidence region of the target chronic disease.

[0011] In some possible implementation manners, after determining the first follow-up frequency of the target patient within a preset time period based on each disease quantity similarity, each disease matching factor, and the reference follow-up frequencies of each second reference patient within the preset time period, the system further includes: A distribution consistency determination module, configured to determine the disease distribution consistency between the region where the target patient is located and the main disease incidence region based on the number of various chronic diseases in the main disease incidence regions of the target chronic disease and the number of various chronic diseases in the region where the target patient is located; A climate consistency determination module, configured to determine the climate consistency between the region where the target patient is located and the main disease incidence region based on the climate data of the main disease incidence region and the climate data of the region where the target patient is located; A comprehensive similarity determination module, configured to determine the comprehensive similarity between the region where the target patient is located and the main disease incidence region by using the disease distribution consistency and the climate consistency; A follow-up frequency correction module, configured to correct the first follow-up frequency of the target patient within a preset time period by using each comprehensive similarity to obtain the second follow-up frequency of the target patient within the preset time period.

[0012] In some possible implementation manners, the distribution consistency determination module is specifically configured to: Determine the chronic diseases in the main disease incidence region whose ratio of the number of chronic diseases to the total number of corresponding chronic disease patients is greater than a preset threshold as the main chronic diseases in the main disease incidence region, and determine the chronic diseases in the region where the target patient is located whose ratio of the number of chronic diseases to the total number of corresponding chronic disease patients is greater than a preset threshold as the main chronic diseases in the region where the target patient is located; Form a first chronic disease set with each main chronic disease in the main disease incidence region, and form a second chronic disease set with each main chronic disease in the region where the target patient is located; Determine the number of the third identical chronic diseases in the first chronic disease set and the second chronic disease set as the disease distribution consistency between the region where the target patient is located and the main disease incidence region.

[0013] In some possible implementation manners, the climate consistency determination module is specifically configured to: Obtain a first climate data sequence of multiple climate dimensions in the main disease incidence region and a second climate data sequence of multiple climate dimensions in the region where the target patient is located; Calculate the similarity between the first climate data sequence and the corresponding second climate data sequence to obtain the local consistency of each climate dimension; Perform mean processing on the local consistency of each climate dimension to obtain the climate consistency between the region where the target patient is located and the main disease incidence region.

[0014] In some possible implementation manners, after correcting the first follow-up frequency of the target patient within a preset time period by using each comprehensive similarity to obtain the second follow-up frequency of the target patient within the preset time period, the system further includes: A risk factor determination module, configured to determine the disease risk factors of a target patient based on the third reference chronic diseases of each family member corresponding to the target patient; A follow-up frequency correction module is further configured to use the disease risk factors of the target patient to correct the second follow-up frequency of the target patient within a preset time period to obtain the third follow-up frequency of the target patient within the preset time period.

[0015] In some possible implementation manners, the risk factor determination module is specifically configured to: Obtain the genetic similarity between each family member and the target patient, and the number of third recessive chronic diseases of each family member, where the number of third recessive chronic diseases is the number of third reference chronic diseases that the target patient does not have; Use each genetic similarity and the corresponding number of third recessive chronic diseases to determine the disease risk factors of the target patient.

[0016] The present invention has the following beneficial effects: In the follow-up chronic disease management system based on Internet technology provided by the present invention, first, the chronic disease information of the target patient and the first reference patient is obtained, and based on the chronic diseases and the regions to which the first reference patient belongs, the main incidence regions of the target chronic diseases are accurately located. Breaking the limitation of traditional single information and introducing the regional dimension can capture the incidence rules of chronic diseases in specific regions and make subsequent analysis more targeted. Then, focusing on the second reference patients in the main incidence regions, the similarity of the number of diseases and the disease symptom matching factors between the target patient and the second reference patients are calculated. Through multi-dimensional comparison, the disease associations between the target patient and the second reference patients are deeply analyzed, and the similarity between the target patient and the second reference patients in the main incidence regions is accurately reflected. Finally, by comprehensively considering the similarity of the number of diseases, the disease symptom matching factors, and the reference follow-up frequency of the second reference patients, the follow-up frequency of the target patient is accurately evaluated. In this way, this comprehensive consideration method not only takes into account the individual characteristics of the patient but also combines the actual follow-up experience of the same-disease group in the main incidence regions of the target chronic diseases, making the calculation result of the follow-up frequency highly consistent with the actual condition of the disease and effectively improving the accuracy of the follow-up frequency of the target patient. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings without creative efforts based on these drawings.

[0018] Figure 1 FIG. 23 is a schematic structural diagram of a first follow-up chronic disease management system based on Internet technology provided by an embodiment of the present invention; Figure 2 Schematic structural diagram of the chronic disease analysis module provided by an embodiment of the present invention; Figure 3 Schematic structural diagram of the second follow-up chronic disease management system based on Internet technology provided by an embodiment of the present invention; Figure 4 Schematic structural diagram of the third follow-up chronic disease management system based on Internet technology provided by an embodiment of the present invention. Detailed implementation manners

[0019] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following, in combination with the accompanying drawings and preferred embodiments, details the specific implementation manners, structures, features and effects of the follow-up chronic disease management system based on Internet technology proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0021] It should be noted that the acquisition, storage, use, processing, etc. of data in the technical solution of the present invention all comply with the relevant regulations of laws and regulations.

[0022] It should be noted that in the embodiments of the present invention, some existing industry solutions such as certain software, components, models, etc. may be mentioned, and they should be regarded as exemplary. The purpose is only to illustrate the feasibility in the implementation of the technical solution of the present invention, but it does not mean that the applicant has already or necessarily used this solution.

[0023] Chronic diseases, that is, the so-called "slow diseases" commonly referred to by people, have the characteristics of a long and protracted course and a relatively gentle progression of the disease. There are many types of chronic diseases, and different types of chronic diseases vary in pathological mechanisms, symptom manifestations and harm degrees; even for the same chronic disease, the disease development trajectories in different patients will be very different. Some patients have a slow disease progression, with mild and stable symptoms; some patients have an erratic disease condition and a faster deterioration speed. Given the significant individual and diverse characteristics of the development of chronic diseases, long-term and continuous disease monitoring is essential for the scientific and effective management of chronic diseases. Disease monitoring can capture the subtle changes in the patient's health status in real time, providing a basis for the adjustment of subsequent treatment and management strategies. The key to accurately grasping the management of chronic diseases is to tailor a personalized follow-up plan according to the patient's current health status and the specific degree of disease progression, especially to determine the appropriate number of follow-ups.

[0024] In actual clinical practice, the currently widely adopted method is to first collect individual characteristic information such as the patient's age, gender, and past medical history, and then use a machine learning model to deeply analyze this personalized data, predict the patient's health trend, and calculate a theoretically reasonable follow-up frequency for the patient accordingly.

[0025] However, this method has obvious drawbacks. It overly relies on the basic characteristics of individual patients, ignoring the group common characteristics of diseases in different regions and different living environments, as well as the possible disease correlations among patients with the same disease, which directly leads to a large deviation between the calculated follow-up frequency and the actual disease needs of the patients, and the accuracy is poor.

[0026] The purpose of the present invention is to provide a follow-up chronic disease management system based on Internet technology. In the follow-up chronic disease management system based on Internet technology provided by the embodiments of the present invention, first, the chronic disease information of the target patient and the first reference patient is obtained, and based on the chronic diseases and the regions to which the first reference patient belongs. Then, focusing on the second reference patients in the main disease incidence regions, the disease quantity similarity and the symptom matching factor between the target patient and the second reference patients are calculated. Finally, based on the disease quantity similarity, the symptom matching factor, and the reference follow-up frequency of the second reference patients, the follow-up frequency of the target patient is accurately evaluated.

[0027] The following introduces the specific embodiments of the follow-up chronic disease management system based on Internet technology provided by the embodiments of the present invention.

[0028] As Figure 1 shown, a structural schematic diagram of a follow-up chronic disease management system based on Internet technology is provided. The follow-up chronic disease management system 100 based on Internet technology includes a chronic disease acquisition module 110, a region determination module 120, a chronic disease analysis module 130, and a follow-up frequency determination module 140.

[0029] The chronic disease acquisition module 110 is used to obtain the target chronic diseases of the target patient and the first reference chronic diseases of the first reference patient; In the chronic disease acquisition module 110, the target patient is a specific patient individual for whom the follow-up frequency needs to be determined and who needs to be focused on and have a follow-up plan; the target chronic diseases are the chronic diseases suffered by the target patient.

[0030] The first reference patient is a group of patient populations used as a reference sample, and the first reference chronic diseases are the chronic diseases suffered by the first reference patient.

[0031] As an example, the chronic disease acquisition module 110 inputs or queries the basic information of the target patient, including name, age, gender, etc., through channels such as the hospital's electronic medical record system and health management platform, and at the same time extracts its diagnosis records to determine the target chronic diseases suffered by the target patient, such as diabetes, hypertension, etc.

[0032] Meanwhile, a certain number of first reference patients are screened from sources such as the hospital's historical case database and the disease surveillance database of the public health system. Similarly, the basic information and diagnostic records of these patients are extracted to obtain the first reference chronic diseases they suffer from.

[0033] The region determination module 120 is configured to determine the main incidence regions of the target chronic diseases based on the first reference chronic diseases of each first reference patient and the regions to which each first reference patient belongs. In the region determination module 120, the region to which a patient belongs is the geographical region information where the first reference patient is located, such as province, city, district, etc., and is used to analyze the distribution of chronic diseases in different regions.

[0034] The main incidence region is the region where the incidence of the target chronic disease is relatively concentrated and the incidence rate is relatively high by analyzing the first reference chronic diseases of each first reference patient and the regions to which they belong.

[0035] As an example, the region determination module 120 sorts out the first reference chronic disease information and region information of the obtained first reference patients and establishes a data table, which includes fields such as patient number, first reference chronic disease name, and region to which the patient belongs.

[0036] Then, a data analysis tool is used to perform statistical analysis on the data. The number of first reference patients suffering from the target chronic disease in each region is counted, sorted by region, and the regions with a relatively large number of patients suffering from the target chronic disease are found. These regions are the main incidence regions of the target chronic disease. For example, through statistics, it is found that the number of first reference patients suffering from the target chronic disease in City A of a certain province ranks first among the cities in the province, then City A can be determined as one of the main incidence regions.

[0037] The chronic disease analysis module 130 is configured to determine the similarity of the number of diseases and the symptom matching factor between the target patient and each second reference patient based on the target chronic disease of the target patient and the second reference chronic diseases of each second reference patient. The second reference patients are the first reference patients whose regions to which they belong are the main incidence regions of the target chronic disease. In the chronic disease analysis module 130, the second reference patients are a specific group selected from the first reference patients, and the regions to which they belong are the main incidence regions of the target chronic disease, and are used to more accurately compare and analyze with the target patient; the second reference chronic diseases are the chronic diseases suffered by the second reference patients.

[0038] The number of diseases similarity is used to measure the similarity degree of the target patient and the second reference patient in terms of the number of chronic diseases they suffer from. The disease matching factor is an index obtained by comprehensively considering the similarities in disease manifestations, severity, etc. of the chronic diseases suffered by the target patient and the second reference patient, and is used to reflect the degree of fit between the two in terms of disease characteristics.

[0039] As an example, the chronic disease analysis module 130 screens out patients from the first reference patients whose place of residence is the main incidence area of the target chronic disease as the second reference patients.

[0040] Then, count the number of chronic diseases suffered by the target patient and the number of chronic diseases suffered by each second reference patient. Calculate the absolute value of the difference in the number of chronic diseases suffered by the target patient and each second reference patient, and then according to a certain formula, such as the number of diseases similarity = 1 - (absolute value of the difference / the larger number of diseases among the two), calculate the number of diseases similarity. The similarity value usually ranges from 0 to 1, and the closer the value is to 1, the more similar the number of diseases is.

[0041] Then, establish a disease characteristic library, which contains information such as common symptoms and severity grading of various chronic diseases. For the target patient and each second reference patient, extract the disease characteristics of the chronic diseases they suffer from respectively. By comparing the disease characteristics of the two, calculate the disease matching factor. For example, the vector similarity algorithm (such as the cosine similarity algorithm) can be used to transform the disease characteristics into vectors, and calculate the similarity between the two vectors as the disease matching factor, and the value range is also between 0 and 1. The larger the value, the higher the degree of disease matching.

[0042] The follow-up frequency determination module 140 is used to determine the first follow-up frequency of the target patient within a preset time period based on each number of diseases similarity, each disease matching factor, and the reference follow-up frequencies of each second reference patient within the preset time period.

[0043] In the follow-up frequency determination module 140, the reference follow-up frequency is the actual number of follow-ups received by each second reference patient within the preset time period, and is used as a reference basis for determining the follow-up frequency of the target patient.

[0044] The preset time period is a pre-set time range, such as one month, one quarter, one year, etc., and is used as the time span for determining the follow-up frequency.

[0045] The first follow-up frequency is the number of follow-ups that the target patient should conduct within the preset time period calculated by comprehensively analyzing factors such as the number of diseases similarity, the disease matching factor, and the reference follow-up frequency.

[0046] As an example, the follow-up frequency determination module 140 collects the reference follow-up frequencies of each second reference patient within a preset time period, integrates them with the previously calculated disease quantity similarity and symptom matching factor, and forms a data set including fields such as the second reference patient number, disease quantity similarity, symptom matching factor, and reference follow-up frequency.

[0047] Then, a machine learning algorithm (such as a linear regression model, a decision tree model, etc.) is used to construct a follow-up frequency prediction model. Using the disease quantity similarity and symptom matching factor as input features and the reference follow-up frequency as the output label, the model is trained.

[0048] Then, the disease quantity similarity and symptom matching factor of the target patient and each second reference patient are input into the trained model to obtain the predicted follow-up frequencies of the target patient corresponding to each second reference patient. Then, these predicted follow-up frequencies are weighted averaged or other comprehensive processing is performed (for example, different weights are assigned according to the magnitudes of the similarity and matching factor), and finally the first follow-up frequency of the target patient within the preset time period is determined.

[0049] Furthermore, after determining the first follow-up frequency of the target patient within the preset time period, a customized follow-up plan is generated according to the first follow-up frequency of the target patient. Specifically, methods such as text messages, push notifications, and emails can be used to remind the target patient to have a health check or participate in a remote video consultation on time. Reminders can be automatically pushed according to the patient's follow-up frequency, such as monthly or quarterly follow-up appointments.

[0050] As an alternative embodiment, as Figure 2 shown, the chronic disease analysis module 130 specifically includes the following units: The quantity similarity determination unit 131 is used to determine the disease quantity similarity between the target patient and the target second reference patient by using the number of first identical chronic diseases in the target chronic diseases of the target patient and the target second reference chronic diseases of the target second reference patient, and the target second reference patient is any one of the second reference patients; The symptom similarity determination unit 132 is used to compare each target chronic disease with each target second reference chronic disease respectively to obtain the symptom similarity between each target chronic disease and each target second reference chronic disease; The matching factor determination unit 133 is used to determine the symptom matching factor between the target patient and the target second reference patient by using each symptom similarity.

[0051] In the quantity similarity determination unit 131, the first identical chronic disease quantity is the quantity of chronic diseases with the same type among the target chronic diseases suffered by the target patient and the target second reference chronic diseases suffered by the target second reference patient. For example, if the target patient has diabetes, hypertension, and coronary heart disease, and the target second reference patient has diabetes, hyperlipidemia, and coronary heart disease, then the first identical chronic disease quantity is 2 (diabetes and coronary heart disease).

[0052] The disease similarity is an index for measuring the similarity degree of the disease characteristics of the target chronic disease and the target second reference chronic disease by comparing aspects such as symptom manifestations, disease mechanisms, and complications between them.

[0053] The disease matching factor is a comprehensive index that synthesizes multiple disease similarity results and is used to comprehensively reflect the degree of fit of the disease characteristics between the target patient and the target second reference patient.

[0054] As an example, the quantity similarity determination unit 131 extracts the list of target chronic diseases suffered by the target patient and the list of target second reference chronic diseases suffered by the target second reference patient from the electronic medical record of the target patient and the medical record of the target second reference patient respectively. These two lists are sorted out to ensure that each chronic disease has a clear name and coding (such as ICD coding) for subsequent accurate comparison.

[0055] Then, a program is written or a data processing tool is used to traverse and compare the chronic disease lists of the target patient and the target second reference patient. For each chronic disease in the list, check whether there is the same chronic disease in the other list. Count the number of identical chronic diseases, which is the first identical chronic disease quantity.

[0056] Then, the disease quantity similarity between the target patient and the target second reference patient is determined by the following formula 1: Formula 1 In formula 1, is used to represent the disease quantity similarity between the target patient h and the target second reference patient g, is used to represent the first identical chronic disease quantity between the target patient h and the target second reference patient g, is used to represent the total quantity of chronic diseases suffered by the target patient h.

[0057] Then, the disease similarity determination unit 132 collects detailed disease information of various chronic diseases, including common symptoms (such as fever, cough, pain, etc.), severity grading of symptoms (such as mild, moderate, severe), development stage of the disease, common complications, etc. This information is sorted into structured data and stored in the disease feature library. Each chronic disease corresponds to a feature vector, and each element in the vector represents a disease feature.

[0058] For each target chronic disease of the target patient and each target second reference chronic disease of the target second reference patient, their corresponding feature vectors are extracted from the disease feature library. The cosine similarity algorithm (such as the cosine similarity algorithm) is used to compare these two feature vectors. The cosine similarity algorithm measures the similarity between two vectors by calculating the cosine value of the angle between them, with a value range between -1 and 1. In the comparison of disease similarity, the result is usually mapped to the range between 0 and 1, and the larger the value, the higher the disease similarity.

[0059] Finally, the disease matching factor determination unit 133 determines the disease matching factor between the target patient and the target second reference patient through the following formula 2: Formula 2 In formula 2, is used to represent the disease matching factor between the target patient h and the target second reference patient g, is used to represent the disease similarity between the x-th target chronic disease and the y-th target second reference chronic disease. is used to represent the preset adjustment coefficient between the x-th target chronic disease and the y-th target second reference chronic disease. Specifically, when the x-th target chronic disease is the same as the y-th target second reference chronic disease, the preset adjustment coefficient is 1; when the x-th target chronic disease is different from the y-th target second reference chronic disease, the preset adjustment coefficient is 0. X is used to represent the total number of target chronic diseases, and Y is used to represent the total number of target second reference chronic diseases.

[0060] Among them, when the x-th target chronic disease is the same as the y-th target second reference chronic disease, the reference degree of its disease similarity is greater; when the x-th target chronic disease is different from the y-th target second reference chronic disease, the reference degree of its disease similarity is smaller. The greater the disease similarities between the target patient h and the target second reference patient g, the greater the disease matching factor between the target patient h and the target second reference patient g.

[0061] Through the chronic disease analysis module 130, comprehensively considering the similarity of the number of diseases and the disease matching factor, the similarity degree between the target patient and the target second reference patient in terms of disease status can be evaluated more comprehensively and accurately. The follow-up strategy formulated based on this scientific evaluation result can better meet the actual needs of patients and improve the effect and quality of follow-up.

[0062] As an optional embodiment, the disease similarity determination unit 132 is specifically configured to: Obtain the first disease description information of the target chronic disease and the second disease description information of the target second reference chronic disease; Perform word segmentation on the first disease description information and the second disease description information respectively to obtain the first word segmentation set and the second word segmentation set; Calculate the similarity between the first word segmentation set and the second word segmentation set to obtain the disease similarity between the target chronic disease and the target second reference chronic disease.

[0063] In the disease similarity determination unit 132, the first disease description information is the literal description content of the detailed characteristics of the chronic disease symptoms, signs, disease course development, complications, etc. extracted from medical literature, electronic medical records, disease diagnosis criteria and other materials for the target chronic disease. For example, for diabetes, the first disease description information may include "polydipsia, polyphagia, polyuria, weight loss, long-term hyperglycemia can cause complications such as retinopathy and nephropathy", etc.

[0064] The second disease description information is the detailed written description of the disease characteristics of the chronic disease obtained from various medical materials corresponding to the target second reference chronic disease. For example, if the target second reference chronic disease is hypertension, the second disease description information may be "headache, dizziness, palpitation, long-term poor blood pressure control can lead to damage to organs such as the heart, brain, and kidneys", etc.

[0065] Exemplarily, in the case where the target chronic disease and the target second reference chronic disease belong to the same type of chronic disease, the first disease description information and the second disease description information are respectively the diagnosis results noted by the doctor in the case; in the case where the target chronic disease and the target second reference chronic disease belong to different types of chronic diseases, the first disease description information and the second disease description information are respectively the written description information obtained from medical materials.

[0066] The first word segmentation set and the second word segmentation set are respectively the sets of a series of words obtained after performing word segmentation on the first disease description information and the second disease description information. Word segmentation is to cut continuous text into lexical units with semantic or grammatical meanings according to certain rules. For example, after word segmentation of "polydipsia, polyphagia, polyuria, weight loss", a set composed of four words "polydipsia", "polyphagia", "polyuria", and "weight loss" is obtained.

[0067] As an example, the disease similarity determination unit 132 collects disease description information of relevant chronic diseases from multiple channels. For the target chronic disease, authoritative medical textbooks, professional medical journals, clinical practice guidelines, patient medical record records in the electronic medical record system, etc. can be consulted. For the target second reference chronic disease, the corresponding disease description information is also obtained from these data sources. For example, in the electronic medical record system, by searching for the diagnosis keywords of the target chronic disease and the target second reference chronic disease, the symptom description part in the medical record records of relevant patients is extracted.

[0068] Then, organize the collected first disease description information and second disease description information, remove irrelevant text content such as the patient's personal information, examination items, etc., and only retain the descriptive text directly related to the disease characteristics. At the same time, merge and deduplicate the information from different sources to ensure that the obtained disease description information is accurate, complete, and non-repetitive.

[0069] Next, input the first disease description information and the second disease description information into the selected word segmentation tool respectively. For example, use the Jieba word segmentation tool and call its word segmentation function in the Python environment to segment the text. During the word segmentation process, the tool will cut the continuous text into word units according to the built-in dictionary and algorithm rules. For some professional medical terms, the word segmentation tool may not be able to accurately recognize them. At this time, common medical terms can be added to the dictionary by customizing the dictionary to improve the accuracy of word segmentation. After the word segmentation is completed, store the word segmentation results in the form of a set to obtain the first word segmentation set and the second word segmentation set. When storing, preprocess the words, such as removing stop words (such as words like "of", "is", "and" that have little meaning for disease description), unifying the case, removing special symbols, etc., to reduce interference in subsequent similarity calculations.

[0070] Finally, select a similarity calculation algorithm to calculate the similarity between the first word segmentation set and the second word segmentation set, so as to realize the calculation of the disease similarity between the target chronic disease and the target second reference chronic disease. Specifically, common text similarity calculation algorithms include the Jaccard similarity coefficient, cosine similarity, edit distance, etc.

[0071] Through the disease similarity determination unit 132, using word segmentation processing and similarity calculation algorithms, it is possible to accurately analyze the disease description information of the target chronic disease and the target second reference chronic disease at the text level. Compared with manual experience judgment, it reduces the interference of subjective factors and improves the accuracy and objectivity of disease similarity evaluation.

[0072] As an optional embodiment, the follow-up frequency determination module 140 is specifically used for: Use the similarity of the number of patients with each disease and each disease matching factor to determine the disease matching degree between the target patient and each second reference patient respectively; Use each disease matching degree and each reference follow-up frequency to determine the first follow-up frequency of the target patient within a preset time period.

[0073] In the follow-up frequency determination module 140, the disease matching degree is an index used to quantify the similarity degree of the overall disease condition between the target patient and each second reference patient after comprehensively considering the similarity of the number of patients with each disease and the disease matching factor. This index can more comprehensively reflect the similarity of their conditions.

[0074] As an example, the follow-up frequency determination module 140 can specifically determine the disease matching degree between the target patient and each second reference patient through the following formula 3: Formula 3 In formula 3, is used to represent the disease matching degree between the target patient h and the target second reference patient g, is used to represent the similarity of the number of diseases between the target patient h and the target second reference patient g, is used to represent the disease matching factor between the target patient h and the target second reference patient g.

[0075] Then, the first follow-up frequency of various target chronic diseases of the target patient within the preset time period is determined through the following formula 4: Formula 4 In formula 4, is used to represent the first follow-up frequency of the m-th target chronic disease of the target patient h within the preset time period, is used to represent the number of second reference patients in the main disease incidence area of the m-th target chronic disease. is used to represent the reference follow-up frequency of the g-th second reference patient within the preset time period, is used to represent the disease matching degree between the target patient h and the g-th second reference patient.

[0076] Among them, the greater the disease matching degree, the more applicable the corresponding reference follow-up frequency is to the target chronic disease of the target patient. At this time, if the reference follow-up frequency is greater, the first follow-up frequency of the target chronic disease of the target patient within the preset time period is greater.

[0077] Finally, the first follow-up frequencies of various target chronic diseases of the target patient within the preset time period are accumulated and rounded up to obtain the first follow-up frequency of the target patient within the preset time period.

[0078] Through the follow-up frequency determination module 140, considering the similarity of the number of diseases and the disease matching factor to determine the disease matching degree, and then combining the reference follow-up frequency to calculate the first follow-up frequency, it avoids the irrationality of formulating the follow-up plan only based on a single factor. It can more accurately formulate a follow-up plan that meets the disease needs of the target patient according to the actual disease characteristics of the target patient, improving the pertinence and effectiveness of the follow-up.

[0079] As an alternative embodiment, the area determination module 120 is specifically used for: Based on the first reference chronic diseases of each first reference patient and the region to which each first reference patient belongs, determine the number of second identical chronic diseases in each region, where the number of second identical chronic diseases is the number of first reference chronic diseases in the region that are the same as the target chronic disease; Using the number of second identical chronic diseases in each region, determine the reference value of each region respectively; Determine the region with the largest reference value as the main incidence region of the target chronic disease.

[0080] In the region determination module 120, the number of second identical chronic diseases is the number of first reference chronic diseases in a specific region that are the same as the type of target chronic disease, which reflects the existence scale of the target chronic disease in the first reference patient group in this region.

[0081] The reference value is a quantitative evaluation index for the importance and availability of different regions in studying the incidence situation, epidemic trend, etc. of the target chronic disease after comprehensively considering factors such as the number of second identical chronic diseases in each region.

[0082] As an example, the region determination module 120 classifies the first reference patient data according to the region to which they belong according to a pre-set region division standard (such as administrative division). For example, the patient data is divided into different region categories such as Beijing City, Shanghai City, and Guangdong Province. For each region, traverse the first reference chronic disease information of all first reference patients in this region, and count the number of first reference chronic diseases that are the same as the target chronic disease. For example, if the target chronic disease is diabetes, in the Beijing City region, there are a total of 50 first reference patients, and among them, 15 patients' first reference chronic disease is diabetes, then the number of second identical chronic diseases in the Beijing City region is 15.

[0083] Then, for each region, divide the number of second identical chronic diseases in this region by the number of first reference patients in this region to obtain the reference value of this region.

[0084] Finally, sort the calculated reference values of each region from largest to smallest to clarify the high and low order of the reference values of each region. Determine the region with the largest reference value in the sorting as the main incidence region of the target chronic disease. For example, among all regions, the reference value of the Beijing City region is the largest, then Beijing is the main incidence region of the target chronic disease.

[0085] Through the region determination module 120, by comprehensively considering the number of first reference chronic diseases in the region that are the same as the target chronic disease, the main incidence region of the target chronic disease can be accurately determined, providing a clear direction for subsequent disease prevention and control and research.

[0086] As an optional embodiment, such as Figure 3As shown, after determining the first follow-up frequency of the target patient within the preset time period based on the similarity of the number of patients with each disease, each disease matching factor, and the reference follow-up frequency of each second reference patient within the preset time period, the Internet-based follow-up chronic disease management system 100 further includes the following modules: A distribution consistency determination module 150, configured to determine the disease distribution consistency between the region where the target patient is located and the main disease incidence region based on the number of various chronic diseases in the main disease incidence region of the target chronic disease and the number of various chronic diseases in the region where the target patient is located; A climate consistency determination module 160, configured to determine the climate consistency between the region where the target patient is located and the main disease incidence region based on the climate data of the main disease incidence region and the climate data of the region where the target patient is located; A comprehensive similarity determination module 170, configured to utilize the disease distribution consistency and the climate consistency to determine the comprehensive similarity between the region where the target patient is located and the main disease incidence region; A follow-up frequency correction module 180, configured to utilize the comprehensive similarity to correct the first follow-up frequency of the target patient within the preset time period to obtain the second follow-up frequency of the target patient within the preset time period.

[0087] In the follow-up frequency correction module 180, chronic diseases are usually highly related to the region where the patient is located. Factors such as the environment, lifestyle, medical resources, and economic level of the region can all affect the incidence rate of chronic diseases, the management method, and the health status of the patient. For example, cities with severe air pollution may have a higher incidence rate of respiratory diseases and cardiovascular diseases. Then, better results may be achieved in aspects such as the control of respiratory diseases and the prevention of complications in such cities. Therefore, by understanding the types of chronic diseases in each region, the overall characteristics and general laws of such diseases in this region can be initially grasped, providing a reference basis for formulating personalized follow-up frequencies.

[0088] The disease distribution consistency is used to measure the similarity degree of the distribution of the number of various chronic diseases between the region where the target patient is located and the main disease incidence region of the target chronic disease. By comparing the incidence numbers of different chronic diseases in the two regions, the degree of fit between them in terms of the types and numbers of chronic disease incidences is judged.

[0089] The climate data covers various information related to the climate, such as temperature (average temperature, maximum temperature, minimum temperature), humidity, precipitation (annual precipitation, seasonal precipitation), air pressure, wind speed, sunshine duration, etc. These data can reflect the climate characteristics of a region.

[0090] The climate consistency refers to the similarity degree of the climate data between the region where the target patient is located and the main disease incidence region, reflecting the closeness of the climate environments of the two regions.

[0091] The comprehensive similarity is a quantitative index obtained by comprehensively considering the consistency of disease distribution and climate consistency, and is used to comprehensively evaluate the similarity degree of the region where the target patient is located and the main disease incidence regions in terms of the chronic disease incidence environment, climate conditions, etc.

[0092] The second follow-up frequency is the follow-up frequency obtained by correcting the first follow-up frequency by using the comprehensive similarity between the region where the target patient is located and the main disease incidence regions. It is more in line with the impact of the environment where the target patient is located on the condition, and is more targeted and reasonable.

[0093] As an example, the distribution consistency determination module 150 collects the quantity data of various chronic diseases in the main disease incidence regions and the regions where the target patients are located from channels such as medical statistical systems and medical record databases. Clean the collected data to remove outliers, duplicate data, etc., to ensure the accuracy and reliability of the data. At the same time, standardize the quantities of various chronic diseases, and specifically, the range normalization method can be used. Then, methods such as cosine similarity or Pearson correlation coefficient are used to calculate the disease distribution consistency between the region where the target patient is located and the main disease incidence regions.

[0094] Then, the climate consistency determination module 160 obtains the climate data of the main disease incidence regions and the regions where the target patients are located from channels such as meteorological departments and meteorological data websites, including data such as temperature, humidity, and precipitation in different time periods. Extract features from the obtained climate data, such as calculating statistical indicators such as annual average temperature, annual precipitation, and seasonal humidity change, to reflect the climate characteristics of the region. Similarly, methods such as cosine similarity or Pearson correlation coefficient are used to calculate the climate consistency between the region where the target patient is located and the main disease incidence regions.

[0095] Then, the comprehensive similarity determination module 170 assigns weights to the disease distribution consistency and the climate consistency according to the influence degrees of the disease distribution and the climate on the incidence of chronic diseases. For example, through expert consultation or data analysis, it is found that the disease distribution has a greater impact on the incidence of chronic diseases, then a higher weight, such as 0.7, can be assigned to the disease distribution consistency, and the climate consistency weight is 0.3. The value range of the weight is between 0 and 1, and the sum of the two weights is 1. Then, the weighted summation method is used to calculate the comprehensive similarity between the region where the target patient is located and the main disease incidence regions.

[0096] Finally, the follow-up frequency correction module 180 determines the second follow-up frequency of the target patient within the preset time period through the following formula 5: Formula 5 In formula 5, is used to represent the second follow-up frequency of the target patient h within the preset time period, is used to represent the number of target chronic diseases of the target patient h, Used to characterize the first follow-up frequency of the m-th target chronic disease of the target patient h within a preset time period. Used to characterize the comprehensive similarity between the region where the target patient h is located and the main incidence region of the m-th target chronic disease. Used to characterize the reference value of the main incidence region of the m-th target chronic disease.

[0097] Among them, the greater the comprehensive similarity, the more similar the environment and the main incidence diseases between the region where the target patient h is located and the main incidence region of the target chronic disease. Then, the medical data of the main incidence region of the target chronic disease is more specific and referenceable, and the greater the weight that needs to be given to the first follow-up frequency of the target chronic disease of the target patient.

[0098] Through the comprehensive similarity determination module 170 and the follow-up frequency correction module 180, the follow-up frequency is corrected after comprehensively considering the consistency of disease distribution and climate consistency, so that the follow-up plan can more accurately fit the impact of the environment where the target patient is located on the condition. The differences in the incidence characteristics of chronic diseases and the climate environment in different regions may lead to different changes in the condition. By correcting the follow-up frequency, the changes in the condition can be detected in time and the treatment effect can be improved.

[0099] As an optional embodiment, the distribution consistency determination module 150 is specifically used for: Determine the chronic diseases whose ratio of the number of chronic diseases to the total number of corresponding chronic disease patients in the main incidence region is greater than a preset threshold as the main chronic diseases in the main incidence region, and determine the chronic diseases whose ratio of the number of chronic diseases to the total number of corresponding chronic disease patients in the region where the target patient is located is greater than a preset threshold as the main chronic diseases in the region where the target patient is located; Form the first chronic disease set with each main chronic disease in the main incidence region, and form the second chronic disease set with each main chronic disease in the region where the target patient is located; Determine the number of the third same chronic diseases in the first chronic disease set and the second chronic disease set as the disease distribution consistency between the region where the target patient is located and the main incidence region.

[0100] In the distribution consistency determination module 150, the number of chronic diseases refers to the statistical result of the number of patients suffering from a certain specific chronic disease in a specific region (such as the main incidence region, the region where the target patient is located).

[0101] The total number of chronic disease patients is the sum of the numbers of all chronic disease patients in a specific region, that is, the value obtained by adding up the numbers of patients suffering from various chronic diseases in this region.

[0102] A major chronic disease refers to a chronic disease in a specific region where the ratio of the number of chronic diseases to the total number of chronic disease patients in that region is greater than a preset threshold, which means that this chronic disease has a relatively high incidence rate in that region and has an important impact on the local chronic disease epidemic situation.

[0103] The first set of chronic diseases is a set composed of the major chronic diseases determined in the main disease - incidence regions, and the elements in the set are representative types of chronic diseases in that region.

[0104] The second set of chronic diseases is a set composed of the major chronic diseases determined in the region where the target patient is located, which reflects the characteristics of chronic disease incidence in the environment where the target patient is located.

[0105] The number of the third common chronic diseases is the number of chronic disease types that exist simultaneously in the first set of chronic diseases and the second set of chronic diseases, which reflects the degree of overlap in the types of chronic disease incidence between the region where the target patient is located and the main disease - incidence regions.

[0106] As an example, the distribution - consistency determination module 150 collects chronic - disease - related data of the main disease - incidence regions and the region where the target patient is located from channels such as the medical record system of medical institutions and the statistical database of the public health department, including the number of patients with each chronic disease and the total number of chronic disease patients in that region. For each chronic disease in the main disease - incidence regions and the region where the target patient is located, calculate the ratio of the number of chronic diseases to the total number of corresponding chronic disease patients respectively. For example, in the main disease - incidence region, the number of hypertension patients is 500, and the total number of chronic disease patients in that region is 2000, then the ratio of the number of hypertension patients to the total number of chronic disease patients is 500÷2000 = 0.25. Then compare the calculated ratio with the preset threshold. Suppose the preset threshold is 0.2. If the ratio of a certain chronic disease is greater than 0.2, then this chronic disease is determined as the major chronic disease in the corresponding region. For example, in the main disease - incidence region, the hypertension ratio 0.25>0.2, so hypertension is the major chronic disease in the main disease - incidence region.

[0107] Then, collect all the major chronic diseases determined in the main disease - incidence regions and organize them according to certain rules (such as sorting by ratio from high to low or classifying by chronic - disease type) to form the first set of chronic diseases. For example, the major chronic diseases in the main disease - incidence region are hypertension, diabetes, and coronary heart disease, then the first set of chronic diseases is {hypertension, diabetes, coronary heart disease}. Similarly, collect and organize the major chronic diseases determined in the region where the target patient is located to form the second set of chronic diseases. For example, the major chronic diseases in the region where the target patient is located are diabetes, hyperlipidemia, and hypertension, then the second set of chronic diseases is {diabetes, hyperlipidemia, hypertension}.

[0108] Finally, by comparing the first chronic disease set and the second chronic disease set, the types of chronic diseases that exist in both sets are identified. These chronic diseases are the third identical chronic diseases. In the above example, the third identical chronic diseases in the first chronic disease set and the second chronic disease set are hypertension and diabetes. The number of the third identical chronic diseases is counted, and this number represents the consistency of disease distribution between the region where the target patient is located and the main disease incidence region. In this example, the number of the third identical chronic diseases is 2, indicating a certain similarity in the types of chronic diseases occurring in the two regions.

[0109] Through the distribution consistency determination module 150, using the number of the third identical chronic diseases as a measure of the disease distribution consistency, the similarity of chronic disease incidence types between regions, which was originally abstract, is quantified, making the evaluation results more objective and accurate, and providing a strong basis for further studying the epidemic patterns and influencing factors of chronic diseases between regions.

[0110] As an optional embodiment, the climate consistency determination module 160 is specifically configured to: Obtain the first climate data sequence of multiple climate dimensions in the main disease incidence region, and the second climate data sequence of multiple climate dimensions in the region where the target patient is located; Calculate the similarity between the first climate data sequence and the corresponding second climate data sequence to obtain the local consistency of each climate dimension; Perform a mean process on the local consistencies of each climate dimension to obtain the climate consistency between the region where the target patient is located and the main disease incidence region.

[0111] In the climate consistency determination module 160, the climate dimension refers to multiple different aspects used to describe climate characteristics. Common climate dimensions include temperature (such as average temperature, maximum temperature, minimum temperature), humidity (such as relative humidity, absolute humidity), precipitation (such as annual precipitation, monthly precipitation, precipitation intensity), air pressure (such as average air pressure, air pressure change range), wind speed (such as average wind speed, maximum wind speed, wind direction frequency), sunshine duration (such as annual sunshine duration, seasonal sunshine duration), etc. Each dimension reflects the climate conditions from a specific angle.

[0112] The first climate data sequence is a sequence formed by arranging a series of climate data collected in multiple climate dimensions in the main disease incidence region in chronological order. For example, in the temperature dimension, the average temperature data of each day in the past year in this region are recorded, and these data arranged in date order constitute the first climate data sequence in the temperature dimension.

[0113] The second climate data sequence is a sequence of climate data collected in multiple climate dimensions in the region where the target patient is located and arranged in chronological order. Taking the temperature dimension as an example again, it is a sequence formed by recording the average temperature data of each day in the past year in the region where the target patient is located and arranging them in date order.

[0114] As an example, the climate consistency determination module 160 obtains climate data from channels such as meteorological departments, professional meteorological data websites, and meteorological monitoring stations. These channels usually provide detailed and accurate meteorological records, including various climate parameters at different time points. According to the research time range and accuracy requirements, data on multiple climate dimensions in the main disease incidence areas and the areas where the target patients are located are filtered. For example, if the research time range is the past year, data such as daily temperature, humidity, and precipitation in the two areas in the past year are collected. The collected data is sorted to ensure uniform data format and complete time series, and outliers and missing values are removed. For missing values, interpolation methods (such as linear interpolation and spline interpolation) can be used for filling. Then, the sorted data is arranged in chronological order to construct the first climate data sequence of the main disease incidence area and the second climate data sequence of the area where the target patient is located respectively. For example, for the temperature dimension, the average daily temperature data in the two areas in the past year are arranged in date order to form two climate data sequences in the temperature dimension.

[0115] Then, according to the characteristics of the climate data and research requirements, a suitable similarity calculation method is selected. For example, the Dynamic Time Warping (DTW) method can be used. DTW calculates the similarity between two climate data sequences by finding the best alignment path between them. Its basic idea is to allow the sequences to be stretched and compressed to a certain extent on the time axis to find the optimal matching method. Specifically, the reciprocal of the DTW distance value between the first climate data sequence and the second climate data sequence can be used as the local consistency of the corresponding climate dimension.

[0116] Finally, the local consistencies of each climate dimension are averaged to obtain the climate consistency.

[0117] Through the climate consistency determination module 160, considering multiple climate dimensions comprehensively, the climate similarity between the area where the target patient is located and the main disease incidence area can be evaluated more comprehensively and accurately. A single climate dimension may not fully reflect the overall differences in the climate environments of the two areas, while the comprehensive analysis of multiple climate dimensions can make up for this deficiency and provide a more reliable climate basis for subsequent research.

[0118] As an alternative embodiment, as Figure 4 shown, after using the comprehensive similarities to correct the first follow-up times of the target patient within a preset time period to obtain the second follow-up times of the target patient within the preset time period, the Internet technology-based follow-up chronic disease management system 100 further includes the following modules: A risk factor determination module 190 is configured to determine the disease risk factors of a target patient based on the third reference chronic diseases of each family member corresponding to the target patient. A follow-up frequency correction module 180 is further configured to use the disease risk factors of the target patient to correct the second follow-up frequency of the target patient within a preset time period to obtain the third follow-up frequency of the target patient within the preset time period.

[0119] In the follow-up frequency correction module 180, the third reference chronic diseases are used to refer to the types of chronic diseases suffered by each family member corresponding to the target patient. The chronic disease conditions of family members may be associated with the disease risk of the target patient through factors such as genetics, lifestyle, and family environment. Therefore, this chronic disease information can be used as an important reference basis for evaluating the disease risk of the target patient.

[0120] The disease risk factors are quantitative indicators or key factors obtained through analysis and calculation based on information such as the third reference chronic diseases of the target patient's family members, which can reflect the likelihood of the target patient developing a certain or certain chronic diseases at a specific time in the future. These factors comprehensively consider the impacts of various factors such as family genetics and disease comorbidity on the target patient.

[0121] The third follow-up frequency is the follow-up frequency obtained after correcting the second follow-up frequency using the disease risk factors of the target patient. It is more in line with the actual situation of the target patient, comprehensively considering special factors such as family genetic factors that may affect disease progression and the patient's health status, and can provide a more reasonable and personalized follow-up plan for the patient.

[0122] As an example, the risk factor determination module 190 collects the chronic disease information of the target patient and each of their family members (such as parents, siblings, children, etc.), and identifies the types of the third reference chronic diseases suffered by each family member. These data can be obtained through hospital medical record systems, health records, questionnaires, etc. Using medical statistical methods and principles of epidemiological research, analyze the correlation between the chronic diseases suffered by family members and the chronic diseases that the target patient may suffer from. For example, study the inheritance pattern of a certain genetic chronic disease in the family and determine which chronic diseases have a higher comorbidity risk in the family. At the same time, consider the mutual influence between different chronic diseases, such as the association between diabetes and cardiovascular diseases.

[0123] Based on the above correlation analysis results, a disease risk assessment model is constructed. This model can comprehensively consider various factors, such as the types and quantities of chronic diseases suffered by family members, the severity of the diseases, the degree of genetic relationship with the target patient, etc. For example, in the case of a first-degree relative (parent, child, sibling) suffering from a severe chronic disease, a higher risk weight is given. Through model calculation, a factor value or risk level that can quantify the disease risk of the target patient is obtained. The result output by the risk assessment model is used as the disease risk factor of the target patient. These factors can be specific numerical values, such as a risk score (ranging from 0 to 100, the higher the value, the greater the risk); or they can be risk levels, such as low risk, medium risk, high risk, etc.

[0124] Then, the follow-up frequency correction module 180 determines the third follow-up frequency of the target patient within a preset time period through the following formula 6: Formula 6 In formula 6, is used to represent the third follow-up frequency of the target patient h within a preset time period, is used to represent the second follow-up frequency of the target patient h within a preset time period, is used to represent the disease risk factor of the target patient h, is the ceiling function.

[0125] Through the risk factor determination module 190 and the follow-up frequency correction module 180, considering the chronic disease conditions of the target patient's family members, the disease risk factor is determined, and based on this, the follow-up frequency is corrected, which can formulate a follow-up plan that better suits the individual health status of the target patient. Since different patients have different family genetic backgrounds and disease risks, the follow-up frequencies will also vary, avoiding the "one-size-fits-all" follow-up mode and improving the pertinence and effectiveness of medical services.

[0126] As an optional embodiment, the risk factor determination module 190 is specifically used for: Obtain the genetic similarity between each family member and the target patient, as well as the number of third recessive chronic diseases of each family member. The number of third recessive chronic diseases is the number of third reference chronic diseases that the target patient does not suffer from; Use each genetic similarity and the corresponding number of third recessive chronic diseases to determine the disease risk factor of the target patient.

[0127] In the risk factor determination module 190, since some chronic diseases have a genetic predisposition, family history often increases the probability of the target patient having multiple chronic diseases. For example, patients with a family history of hypertension, diabetes, heart disease, etc. are at a higher risk of developing these diseases in their offspring. Moreover, the genetic susceptibility of certain chronic diseases may interact crosswise, leading to the coexistence of multiple diseases. Therefore, it is necessary to further consider whether there are still undetected recessive diseases in the patient. When there is a high probability that the patient has inherited recessive diseases, the number of follow-up visits needs to be increased to ensure timely reflection of the patient's physical condition.

[0128] Genetic similarity refers to the degree of similarity between each family member and the target patient at the level of genetic material (mainly DNA sequence). It reflects the closeness of gene inheritance among family members. Since genes carry a large amount of information related to the occurrence and development of diseases, the higher the genetic similarity, the greater the possibility that the target patient inherits certain disease-related gene mutations from this family member, thereby affecting their disease risk.

[0129] The number of third recessive chronic diseases is the number of third reference chronic diseases that each family member has and are different from the chronic diseases of the target patient. By counting the number of family members suffering from this chronic disease, the clustering situation of this disease in the family can be understood, and then the potential risk of the target patient getting sick due to family history can be evaluated.

[0130] As an example, based on blood relationship, a genetic similarity (such as 0 - 1 point) is assigned to the target patient and each family member. For example, the genetic similarity of the parent-child relationship is 0.9 points, that of siblings is 0.8 points, that of grandparents and grandchildren is 0.6 points, that of aunts / uncles and nephews / nieces is 0.5 points, and that of cousins is 0.3 points.

[0131] Then, the chronic disease information of the collected family members is compared with the target chronic disease of the target patient, and the number of third reference chronic diseases that each family member has and are different from the target chronic disease is counted, that is, the number of third recessive chronic diseases.

[0132] Finally, the disease risk factor of the target patient is determined by the following formula 7: Formula 7 In formula 7, is used to characterize the disease risk factor of the target patient h, is used to characterize the number of family members of the target patient h, is used to characterize the number of third recessive chronic diseases of the j-th family member of the target patient h, is used to characterize the genetic similarity of the j-th family member of the target patient h. It is used to characterize the normalization function and normalize the value to the range of [0, 1].

[0133] Among them, the greater the genetic similarity, the more likely it is that the target patient h inherits the genetic susceptibility similar to that of the j-th family member, further increasing the disease risk of the target patient h, that is, the greater the disease risk factor of the target patient h.

[0134] Through the risk factor determination module 190, by comprehensively considering the genetic similarity between family members and the target patient and the number of diseases different from the target chronic disease among family members, the genetic and family history factors affecting the disease of the target patient can be captured more comprehensively. Compared with the method that only considers a single factor, this comprehensive evaluation method can more accurately reflect the actual disease risk of the target patient and provide a more reliable basis for the early prevention and intervention of diseases.

[0135] It should be clear that the present invention is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, the detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present invention is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order between steps after understanding the spirit of the present invention.

[0136] It should also be noted that the exemplary embodiments mentioned in the present invention describe some methods or systems based on a series of steps or devices. However, the present invention is not limited to the order of the above steps, that is, the steps can be executed in the order mentioned in the embodiments, or different from the order in the embodiments, or several steps can be executed simultaneously.

[0137] As described above, only the specific embodiments of the present invention are provided. Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, modules, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be repeated here. It should be understood that the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of various equivalent modifications or substitutions, and these modifications or substitutions should be covered within the protection scope of the present invention.

Claims

1. A follow-up chronic disease management system based on Internet technology, characterized in that, The system includes: a chronic disease acquisition module, configured to acquire the target chronic disease of a target patient and the first reference chronic disease of a first reference patient; a region determination module, configured to determine the main incidence region of the target chronic disease based on the first reference chronic diseases of the first reference patients and the regions to which the first reference patients belong; a chronic disease analysis module, configured to determine the similarity of the number of diseases and the symptom matching factors between the target patient and each second reference patient based on the target chronic disease of the target patient and the second reference chronic diseases of each second reference patient, where the second reference patients are the first reference patients whose regions belong to the main incidence region of the target chronic disease; a follow-up frequency determination module, configured to determine the first follow-up frequency of the target patient within the preset time period based on the similarities of the number of diseases, the symptom matching factors, and the reference follow-up frequencies of the second reference patients within the preset time period; 2. The follow-up chronic disease management system based on Internet technology according to claim 1, wherein The chronic disease analysis module includes: a number similarity determination unit, configured to determine the similarity of the number of diseases between the target patient and the target second reference patient by using the number of first identical chronic diseases in the target chronic disease of the target patient and the target second reference chronic disease of the target second reference patient, where the target second reference patient is any one of the second reference patients; a symptom similarity determination unit, configured to compare each of the target chronic diseases with each of the target second reference chronic diseases to obtain the symptom similarity between each of the target chronic diseases and each of the target second reference chronic diseases; a matching factor determination unit, configured to determine the symptom matching factor between the target patient and the target second reference patient by using each of the symptom similarities; 3. The follow-up chronic disease management system based on Internet technology according to claim 2, characterized in that The symptom similarity determination unit is configured to: acquire the first symptom description information of the target chronic disease and the second symptom description information of the target second reference chronic disease; perform word segmentation processing on the first symptom description information and the second symptom description information respectively to obtain a first word segmentation set and a second word segmentation set; calculate the similarity between the first word segmentation set and the second word segmentation set to obtain the symptom similarity between the target chronic disease and the target second reference chronic disease; 4. The follow-up chronic disease management system based on Internet technology according to claim 1, characterized in that, The follow-up frequency determination module is configured to: determine the disease condition matching degree between the target patient and each second reference patient by using each of the similarities of the number of diseases and each of the symptom matching factors; determine the first follow-up frequency of the target patient within the preset time period by using each of the disease condition matching degrees and each of the reference follow-up frequencies; 5. The follow-up chronic disease management system based on Internet technology according to claim 1, characterized in that The region determination module is configured to: determine the number of second identical chronic diseases in each region based on the first reference chronic diseases of the first reference patients and the regions to which the first reference patients belong, where the number of second identical chronic diseases is the number of the first reference chronic diseases that are the same as the target chronic disease in the region; determine the reference value of each region by using the number of second identical chronic diseases in each region; determine the region with the maximum reference value as the main incidence region of the target chronic disease.

6. The follow-up chronic disease management system based on Internet technology according to any one of claims 1-5, characterized in that, After determining the first follow-up frequency of the target patient within the preset time period based on the similarity of the number of patients with each disease, each disease matching factor, and the reference follow-up frequency of each second reference patient within the preset time period, the system further includes: A distribution consistency determination module, configured to determine the disease distribution consistency between the region where the target patient is located and the main disease incidence region based on the number of various chronic diseases in the main disease incidence region of the target chronic disease and the number of various chronic diseases in the region where the target patient is located; A climate consistency determination module, configured to determine the climate consistency between the region where the target patient is located and the main disease incidence region based on the climate data of the main disease incidence region and the climate data of the region where the target patient is located; A comprehensive similarity determination module, configured to determine the comprehensive similarity between the region where the target patient is located and the main disease incidence region by using the disease distribution consistency and the climate consistency; A follow-up frequency correction module, configured to correct the first follow-up frequency of the target patient within the preset time period by using the comprehensive similarity to obtain the second follow-up frequency of the target patient within the preset time period.

7. The follow-up chronic disease management system based on Internet technology according to claim 6, characterized in that, The distribution consistency determination module is configured to: Determine the chronic diseases in the main disease incidence region whose ratio of the number of chronic diseases to the total number of corresponding chronic disease patients is greater than a preset threshold as the main chronic diseases in the main disease incidence region, and determine the chronic diseases in the region where the target patient is located whose ratio of the number of chronic diseases to the total number of corresponding chronic disease patients is greater than a preset threshold as the main chronic diseases in the region where the target patient is located; Form a first chronic disease set with each of the main chronic diseases in the main disease incidence region, and form a second chronic disease set with each of the main chronic diseases in the region where the target patient is located; Determine the number of the third identical chronic diseases in the first chronic disease set and the second chronic disease set as the disease distribution consistency between the region where the target patient is located and the main disease incidence region.

8. The follow-up chronic disease management system based on Internet technology according to claim 6, characterized in that, The climate consistency determination module is configured to: Obtain a first climate data sequence of multiple climate dimensions in the main disease incidence region and a second climate data sequence of multiple climate dimensions in the region where the target patient is located; Calculate the similarity between the first climate data sequence and the corresponding second climate data sequence to obtain the local consistency of each climate dimension; Perform an averaging process on the local consistency of each climate dimension to obtain the climate consistency between the region where the target patient is located and the main disease incidence region.

9. The follow-up chronic disease management system based on Internet technology according to claim 6, characterized in that After correcting the first follow-up frequency of the target patient within the preset time period by using each comprehensive similarity to obtain the second follow-up frequency of the target patient within the preset time period, the system further includes: A risk factor determination module, configured to determine the disease risk factors of the target patient based on the third reference chronic diseases of each family member corresponding to the target patient. The follow-up frequency correction module is further configured to correct the second follow-up frequency of the target patient within the preset time period by using the disease risk factors of the target patient, so as to obtain the third follow-up frequency of the target patient within the preset time period.

10. The follow-up chronic disease management system based on Internet technology according to claim 9, characterized in that, The risk factor determination module is configured to: Obtain the genetic similarity between each family member and the target patient, and the number of third latent chronic diseases of each family member, where the number of third latent chronic diseases is the number of the third reference chronic diseases that the target patient does not have; Determine the disease risk factors of the target patient by using each genetic similarity and the corresponding number of third latent chronic diseases.

Images