Patient health guidance method and system based on follow-up visit data

Abstract

The invention belongs to the technical field of health management, and provides a patient health guidance method and system based on follow-up visit data. The method comprises the following steps: determining basic data of a target patient needing health guidance, and calling first health guidance data in a recovery stage according to a disease type; receiving follow-up visit data which is uploaded by follow-up visitors and corresponds to the target patient, extracting illness state evaluation data and recovery execution data from the follow-up visit data, and calculating a first guidance optimization coefficient according to the illness state evaluation data and the recovery execution data; second health guidance data is calculated according to the first health guidance data and the first guidance optimization coefficient, and remote health guidance is performed on the target patient according to the second health guidance data. According to the invention, based on the basic data of the patient and the follow-up visit data corresponding to the target patient and uploaded by the follow-up visitors, the health guidance data suitable for the actual illness state recovery condition of the patient is obtained through decision making, and the health guidance effect on the patient is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of health management, and in particular to a patient health guidance method and system based on follow-up data. Background Art

[0002] Remote health guidance for patients is currently a popular way of assisting health management. That is, the health management platform will obtain the user's disease type and retrieve the health guidance data for that type of disease, including but not limited to exercise methods, recipe recommendations, etc. Remote health guidance can help patients recover faster and better. However, the health management platform can currently only provide guidance to patients based on fixed health guidance data, and cannot adjust the health guidance data according to the patient's specific recovery situation, resulting in the patient's recovery effect not being optimal.

[0003] Therefore, how to further improve the effectiveness of health guidance for patients is a technical problem that needs to be further solved. Summary of the invention

[0004] In order to solve the technical problems existing in the above-mentioned background technology, the present invention provides a patient health guidance method, system, electronic device, computer storage medium and computer program product based on follow-up data.

[0005] The present invention provides a patient health guidance method based on follow-up data, which is applied to a health management platform. The method comprises the following steps: determining basic data of a target patient who needs health guidance, including a disease type and a recovery stage, and retrieving first health guidance data according to the disease type and the recovery stage; receiving follow-up data corresponding to the target patient uploaded by a follow-up personnel, extracting condition assessment data and recovery execution data from the follow-up data, and calculating a first guidance optimization coefficient according to the condition assessment data and the recovery execution data; calculating second health guidance data according to the first health guidance data and the first guidance optimization coefficient, and performing remote health guidance on the target patient according to the second health guidance data.

[0006] In some embodiments, basic data of a target patient who needs health guidance is determined, including disease type and recovery stage, and first health guidance data is retrieved according to the disease type and the recovery stage, including: obtaining the diagnosis and treatment record data of the target patient, parsing the disease type and diagnosis and treatment time of the target patient from the diagnosis and treatment record data, and matching the recovery stage of the target patient according to the recovery stage time control data corresponding to the disease type, the diagnosis and treatment time and the current time; matching the first health guidance data from the database according to the disease type and the recovery stage, and retrieving it.

[0007] In some embodiments, the receiving follow-up data corresponding to the target patient uploaded by the follow-up personnel includes: receiving the follow-up data corresponding to the target patient uploaded by a terminal device equipped by the follow-up personnel, wherein the follow-up data is structured data or unstructured data; wherein the follow-up personnel follows up on the target patient through at least one of telephone follow-up, online follow-up, and on-site follow-up, and then uses the terminal device to generate the follow-up data.

[0008] In some embodiments, condition assessment data and recovery execution data are extracted from the follow-up data, and a first guidance optimization coefficient is calculated based on the condition assessment data and the recovery execution data, including: using a semantic analysis component to perform semantic analysis on the follow-up data to obtain the condition assessment data and the recovery execution data of the target patient; wherein the condition assessment data includes a first evaluation value for characterizing the degree of negative deviation between the actual recovery degree and the standard recovery degree corresponding to the current recovery stage of the target patient, and the recovery execution data includes a second evaluation value of the degree of compliance of the target patient with the health guidance data sent by the health management platform; the first guidance optimization coefficient is calculated based on the first evaluation value and the second evaluation value, and the calculation formula is as follows: ; In the formula, is the first guiding optimization coefficient; is the first evaluation value, is the average of the first evaluation values ​​of all other patients with the same disease type and the same recovery stage as the target patient; is the second evaluation value, It is the average of the second evaluation values ​​of all other patients who have the same disease type and the same recovery stage as the target patient.

[0009] In some embodiments, the second health guidance data is calculated based on the first health guidance data and the first guidance optimization coefficient, including: calculating the third health guidance data based on the first health guidance data and the first guidance optimization coefficient; and also parsing the follow-up time from the follow-up data, and calculating the upload delay duration based on the upload time of the follow-up data and the follow-up time, and determining the second guidance optimization coefficient based on the upload delay duration; wherein the second guidance optimization coefficient is negatively correlated with the upload delay duration; and the second health guidance data is calculated using the second guidance optimization coefficient and the third health guidance data.

[0010] In some embodiments, the first health guidance data, the second health guidance data and the third health guidance data all include exercise type and exercise intensity, recipe type and recipe recommendation compliance; the first guidance optimization coefficient and the second guidance optimization coefficient are used to adjust the exercise intensity and the recipe recommendation compliance.

[0011] The present invention also provides a patient health guidance system based on follow-up data, which is applied to a health management platform. The system includes a processing device and a storage device. The storage device stores computer program code. The processing device executes the computer program code stored in the storage device to implement the following steps: determining basic data of a target patient who needs health guidance, including disease type and recovery stage, and retrieving first health guidance data according to the disease type and the recovery stage; receiving follow-up data corresponding to the target patient uploaded by a follow-up personnel, extracting condition assessment data and recovery execution data from the follow-up data, and calculating a first guidance optimization coefficient according to the condition assessment data and the recovery execution data; calculating second health guidance data according to the first health guidance data and the first guidance optimization coefficient, and performing remote health guidance on the target patient according to the second health guidance data.

[0012] The present invention also provides an electronic device, which includes: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor implements the steps of any one of the above methods when executing the computer program.

[0013] The present invention also provides a computer storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of any one of the above methods can be implemented.

[0014] The present invention also provides a computer program product, wherein the computer program product includes computer program codes, and when the computer program codes are executed by a processor of an electronic device, the steps of any one of the above methods are implemented.

[0015] The beneficial effect of the present invention is that the health management platform of the present invention is based on the basic data of the target patient and the follow-up data corresponding to the target patient uploaded by the follow-up personnel, and makes decisions to obtain health guidance data suitable for the actual condition recovery of the target patient, which is beneficial to improving the health guidance effect on the target patient and facilitating rapid recovery of health. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 It is a flowchart of a patient health guidance method based on follow-up data disclosed in an embodiment of the present invention.

[0018] Figure 2 It is a schematic diagram of a flow chart of calculating the second health guidance data disclosed in an embodiment of the present invention.

[0019] Figure 3 It is a structural schematic diagram of a patient health guidance system based on follow-up data disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0020] The following is a description of the implementation of the present application by specific specific embodiments. People familiar with the technology can easily understand other advantages and effects of the present application from the contents disclosed in this specification. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0021] In addition, the technical features involved in the different embodiments of the present application described below can be combined with each other as long as they do not conflict with each other.

[0022] Remote health guidance for patients is currently a popular way of assisting health management. That is, the health management platform will obtain the user's disease type and retrieve the health guidance data for that type of disease, including but not limited to exercise methods, recipe recommendations, etc. Remote health guidance can help patients recover faster and better. However, the health management platform can currently only provide guidance to patients according to fixed health guidance data, but cannot adjust the health guidance data according to the patient's specific recovery situation, resulting in the patient's recovery effect not being optimal. Therefore, how to further improve the health guidance effect for patients is a technical problem that needs to be further solved.

[0023] like Figure 1As shown, in view of the above technical problems, an embodiment of the present invention discloses a patient health guidance method based on follow-up data, which is applied to a health management platform, and the method includes the following steps: determining basic data of a target patient who needs health guidance, including disease type and recovery stage, and retrieving first health guidance data according to the disease type and the recovery stage; receiving follow-up data corresponding to the target patient uploaded by a follow-up personnel, extracting condition assessment data and recovery execution data from the follow-up data, and calculating a first guidance optimization coefficient according to the condition assessment data and the recovery execution data; calculating second health guidance data according to the first health guidance data and the first guidance optimization coefficient, and performing remote health guidance on the target patient according to the second health guidance data.

[0024] In the above scheme, the health management platform of the present invention simultaneously obtains the basic data of the target patient who needs health guidance and the follow-up data corresponding to the target patient uploaded by the follow-up personnel. Among them, the basic data obtained includes the type of disease and recovery stage of the target patient, and the follow-up data includes the condition assessment data and recovery execution data.

[0025] Next, the first health guidance data suitable for the recovery stage of the type of disease can be retrieved from the database, and the first health guidance data can be used to provide remote health guidance to the target patient, such as guiding the target patient on what exercise intensity to use to perform a certain type of exercise. At the same time, the data related to the condition assessment and recovery execution of the target patient written by the follow-up personnel are extracted from the follow-up data, and the first guidance optimization coefficient is calculated by combining the condition assessment data and the recovery execution data.

[0026] Finally, the first health guidance data retrieved from the database is optimized and adjusted using the first guidance optimization coefficient, so as to obtain second health guidance data that is more suitable for the actual situation of the target patient individual.

[0027] Therefore, the health management platform of the present invention simultaneously makes decisions based on the basic data of the target patient and the follow-up data corresponding to the target patient uploaded by the follow-up personnel to obtain health guidance data suitable for the actual recovery of the target patient's condition, which is beneficial to improving the health guidance effect on the target patient and facilitating rapid recovery of health.

[0028] In some embodiments, basic data of a target patient who needs health guidance is determined, including disease type and recovery stage, and first health guidance data is retrieved according to the disease type and the recovery stage, including: obtaining the diagnosis and treatment record data of the target patient, parsing the disease type and diagnosis and treatment time of the target patient from the diagnosis and treatment record data, and matching the recovery stage of the target patient according to the recovery stage time control data corresponding to the disease type, the diagnosis and treatment time and the current time; matching the first health guidance data from the database according to the disease type and the recovery stage, and retrieving it.

[0029] In this embodiment, the health management platform can communicate with various medical institutions to obtain the diagnosis and treatment record data of the target patient. Of course, the diagnosis and treatment record data needs to be authorized and confirmed by the target patient before it is obtained. The diagnosis and treatment record data includes the disease type and diagnosis and treatment time (such as operation time) of the target patient. At the same time, the database also presets recovery stage time control data corresponding to various disease types. The recovery stage time control data includes the recovery stage division of the corresponding type of disease after diagnosis and treatment, for example, the first week is the first stage, the second week is the second stage... Therefore, according to the time difference between the current time and the diagnosis and treatment time, the recovery stage to which the target patient currently belongs can be matched from the recovery stage time control data. After the disease type and recovery stage of the target patient are obtained according to the above method, the first health guidance data suitable for the target patient can be further matched from the database and retrieved.

[0030] It should be noted that there are multiple first health guidance data stored in the database, and each first health guidance data corresponds to a type of disease. Health guidance data related to the corresponding type of disease is obtained through various channels, such as exercise recovery plans (including exercise type, exercise intensity, etc.) and recommended recipes for different stages of this type of disease compiled by well-known experts and scholars. By sorting and analyzing multi-source data, a corresponding first health guidance data can be obtained. Among them, the above-mentioned multiple channels include but are not limited to professional medical forums, academic platforms, various video apps, etc.

[0031] In some embodiments, the receiving follow-up data corresponding to the target patient uploaded by the follow-up personnel includes: receiving the follow-up data corresponding to the target patient uploaded by a terminal device equipped by the follow-up personnel, wherein the follow-up data is structured data or unstructured data; wherein the follow-up personnel follows up on the target patient through at least one of telephone follow-up, online follow-up, and on-site follow-up, and then uses the terminal device to generate the follow-up data.

[0032] In this embodiment, the follow-up personnel can follow up the target patient through at least one of telephone follow-up, online follow-up (such as online video, online questionnaire, etc.), and on-site follow-up according to the actual situation. During the follow-up process, the follow-up personnel can communicate with the target patient one-on-one to understand the target patient's recovery at the current stage, as well as the compliance with the health guidance data, etc., and compile the follow-up data based on these data, and transmit it to the health management platform in a timely manner.

[0033] It should be noted that in order to ensure patient privacy, encryption should be used before and during the transmission of follow-up data to avoid the leakage of relevant information.

[0034] In some embodiments, condition assessment data and recovery execution data are extracted from the follow-up data, and a first guidance optimization coefficient is calculated based on the condition assessment data and the recovery execution data, including: using a semantic analysis component to perform semantic analysis on the follow-up data to obtain the condition assessment data and the recovery execution data of the target patient; wherein the condition assessment data includes a first evaluation value for characterizing the degree of negative deviation between the actual recovery degree and the standard recovery degree corresponding to the current recovery stage of the target patient, and the recovery execution data includes a second evaluation value of the degree of compliance of the target patient with the health guidance data sent by the health management platform; the first guidance optimization coefficient is calculated based on the first evaluation value and the second evaluation value, and the calculation formula is as follows: ; In the formula, is the first guiding optimization coefficient; is the first evaluation value, is the average of the first evaluation values ​​of all other patients with the same disease type and the same recovery stage as the target patient; is the second evaluation value, It is the average of the second evaluation values ​​of all other patients who have the same disease type and the same recovery stage as the target patient.

[0035] In this embodiment, since the follow-up data uploaded by the follow-up personnel may be structured data (such as tabular data) or unstructured data (such as natural language describing the recovery status of the target patient), the present invention first uses a semantic analysis component to perform semantic analysis on the follow-up data, thereby extracting the target patient's condition assessment data and recovery execution data. Among them, the condition assessment data includes a first evaluation value, which is obtained by the follow-up personnel based on the follow-up conversation analysis with the target patient. It is used to characterize the negative deviation between the actual recovery degree of the target patient in the current recovery stage and the standard recovery degree corresponding to the current recovery stage, that is, the degree to which the actual recovery degree is inferior to the standard recovery degree. Obviously, the greater the negative deviation, the smaller the first evaluation value. In addition, the deviation between the actual recovery degree and the corresponding standard recovery degree may also be a positive deviation. In this case, the first evaluation value is set. , that is, the first guidance optimization coefficient is not increased based on this factor, and there is no need to enhance the guidance intensity for the target patient based on this factor.

[0036] The recovery execution data includes a second evaluation value, which is also obtained by the follow-up personnel based on the follow-up conversation analysis with the target patient. It is used to characterize the target patient's compliance with the health guidance data sent by the health management platform (mainly the second health guidance data mentioned above, but when the health guidance is conducted for the first time, the health guidance data is the first health guidance data), that is, whether the target patient has recovered his health in accordance with the health guidance data, and the specific degree of implementation. Obviously, the smaller the degree of compliance, the smaller the second evaluation value.

[0037] At the same time, the first evaluation value and the second evaluation value of other patients with the same type of disease at the same recovery stage can also be retrieved. Obviously, these first evaluation values ​​and second evaluation values ​​are obtained by other follow-up personnel based on follow-up conversations with patients. The average of these first evaluation values ​​and second evaluation values ​​is calculated and substituted into the above calculation formula to obtain the final first guidance optimization coefficient. Among them, the calculation of the average of the first evaluation value and the second evaluation value is periodic, so , is a constant value, and correspondingly, the first guiding optimization coefficient is negatively correlated with the first evaluation value and the second evaluation value.

[0038] In some embodiments, Figure 2As shown, the second health guidance data is calculated based on the first health guidance data and the first guidance optimization coefficient, including: the third health guidance data is calculated based on the first health guidance data and the first guidance optimization coefficient; and the follow-up time is also parsed from the follow-up data, and the upload delay duration is calculated based on the upload time of the follow-up data and the follow-up time, and the second guidance optimization coefficient is determined based on the upload delay duration; wherein the second guidance optimization coefficient is negatively correlated with the upload delay duration; the second health guidance data is calculated using the second guidance optimization coefficient and the third health guidance data.

[0039] In this embodiment, since the follow-up personnel may not upload the follow-up data within the first time after the follow-up is completed, the duration in between is the upload delay duration. During the upload delay duration, the target patient may have taken the initiative to carry out a certain degree of content related to the health guidance data pushed by the health management platform, especially when the health guidance data to be pushed by the health management platform contains the same content as the health guidance data to be pushed last time or last few times. For example, in the second and third recovery stages, the health guidance data pushed by the health management platform include "performing 20 minutes of Ba Duan Jin exercise", so in the current fourth recovery stage, the target patient may carry out "performing 20 minutes of Ba Duan Jin exercise" on his own after the follow-up personnel leave, and this situation is not recorded in the follow-up data, and the probability of the above situation occurring increases with the increase of the upload delay duration.

[0040] In view of the above situation, the present invention further sets a second guidance optimization coefficient determined according to the upload delay time and the corresponding negative correlation, that is, the larger the upload delay time, the smaller the corresponding second guidance optimization coefficient (for example, 0.6), that is, a smaller second guidance optimization coefficient is used to severely weaken the third health guidance data; the smaller the upload delay time, the larger the corresponding second guidance optimization coefficient (for example, 0.8), that is, a larger second guidance optimization coefficient is used to slightly weaken the third health guidance data. Among them, the first guidance optimization coefficient is a value greater than 1, and the second guidance optimization coefficient is a value less than 1. Therefore, the first health guidance data after the aforementioned enhanced processing, that is, the third health guidance data, is appropriately weakened by the second guidance optimization coefficient to avoid excessive enhancement from bringing adverse effects to the target patient.

[0041] In some embodiments, the first health guidance data, the second health guidance data and the third health guidance data all include exercise type and exercise intensity, recipe type and recipe recommendation compliance; the first guidance optimization coefficient and the second guidance optimization coefficient are used to adjust the exercise intensity and the recipe recommendation compliance.

[0042] In this embodiment, the health guidance data sent by the health management platform includes the types of exercise and intensity recommended for the target patient, as well as the types of recipes that should be executed and the degree of compliance with the recipe recommendations. Among them, the intensity of exercise can be characterized by the duration of exercise, and the degree of compliance with the recipe recommendations can be characterized by a level. The higher the level, the stricter the target patient is required to comply with the recipe, and the lower the level, the looser the target patient is required to comply with the recipe. Therefore, by using the first guidance optimization coefficient and the second guidance optimization coefficient to dynamically adjust the above-mentioned exercise intensity and recipe recommendation compliance, health guidance data that is adapted to the actual individual situation of the target patient can be obtained, which is conducive to improving the recovery speed.

[0043] like Figure 3 As shown, an embodiment of the present invention also discloses a patient health guidance system based on follow-up data, which is applied to a health management platform. The system includes a processing device and a storage device. The storage device stores computer program code. The processing device executes the computer program code stored in the storage device to implement the following steps: determining basic data of a target patient who needs health guidance, including a disease type and a recovery stage, and retrieving first health guidance data according to the disease type and the recovery stage; receiving follow-up data corresponding to the target patient uploaded by a follow-up personnel, extracting condition assessment data and recovery execution data from the follow-up data, and calculating a first guidance optimization coefficient according to the condition assessment data and the recovery execution data; calculating second health guidance data according to the first health guidance data and the first guidance optimization coefficient, and performing remote health guidance on the target patient according to the second health guidance data.

[0044] The system in this embodiment adopts all the technical solutions of the above embodiments, and therefore has at least all the beneficial effects brought by the technical solutions of the above embodiments, which will not be described one by one here.

[0045] An embodiment of the present invention further discloses an electronic device, which includes: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor implements the steps of any one of the above methods when executing the computer program.

[0046] The embodiment of the present invention further discloses a computer storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of any one of the above methods can be implemented.

[0047] An embodiment of the present invention further discloses a computer program product, wherein the computer program product includes computer program codes, and when the computer program codes are executed by a processor of an electronic device, the steps of any one of the above methods are implemented.

[0048] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0049] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0050] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0051] The above description is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention.

Claims

1. A patient health guidance method based on follow-up data, applied to a health management platform, characterized in that: The method comprises the following steps: determining basic data of a target patient who needs health guidance, including disease type and recovery stage, and retrieving first health guidance data according to the disease type and the recovery stage; receiving follow-up data corresponding to the target patient uploaded by a follow-up person, extracting condition assessment data and recovery execution data from the follow-up data, and calculating a first guidance optimization coefficient according to the condition assessment data and the recovery execution data; Second health guidance data is calculated based on the first health guidance data and the first guidance optimization coefficient, and remote health guidance is performed on the target patient according to the second health guidance data.

2. A patient health guidance method based on follow-up data according to claim 1, characterized in that: Determine the basic data of the target patient who needs health guidance, including the disease type and the recovery stage, and retrieve the first health guidance data according to the disease type and the recovery stage, including: obtaining the diagnosis and treatment record data of the target patient, parsing the disease type and diagnosis and treatment time of the target patient from the diagnosis and treatment record data, and matching the recovery stage of the target patient according to the recovery stage time control data corresponding to the disease type, the diagnosis and treatment time and the current time; match the first health guidance data from the database according to the disease type and the recovery stage, and retrieve it.

3. A patient health guidance method based on follow-up data according to claim 2, characterized in that: The receiving follow-up data corresponding to the target patient uploaded by the follow-up personnel includes: receiving the follow-up data corresponding to the target patient uploaded by the terminal device equipped by the follow-up personnel, wherein the follow-up data is structured data or unstructured data; wherein the follow-up personnel implements the follow-up of the target patient through at least one of telephone follow-up, network follow-up, and on-site follow-up, and then uses the terminal device to generate the follow-up data.

4. A patient health guidance method based on follow-up data according to claim 3, characterized in that: Extracting condition assessment data and recovery execution data from the follow-up data, and calculating a first guidance optimization coefficient based on the condition assessment data and the recovery execution data, including: using a semantic analysis component to perform semantic analysis on the follow-up data to obtain the condition assessment data and the recovery execution data of the target patient; wherein the condition assessment data includes a first evaluation value for characterizing the degree of negative deviation between the actual recovery degree and the standard recovery degree corresponding to the current recovery stage of the target patient, and the recovery execution data includes a second evaluation value of the degree of compliance of the target patient with the health guidance data sent by the health management platform; calculating the first guidance optimization coefficient based on the first evaluation value and the second evaluation value, the calculation formula is as follows: ; In the formula, is the first guiding optimization coefficient; is the first evaluation value, is the average of the first evaluation values ​​of all other patients with the same disease type and the same recovery stage as the target patient; is the second evaluation value, It is the average of the second evaluation values ​​of all other patients who have the same disease type and the same recovery stage as the target patient.

5. A patient health guidance method based on follow-up data according to claim 4, characterized in that: The second health guidance data is calculated based on the first health guidance data and the first guidance optimization coefficient, including: calculating the third health guidance data based on the first health guidance data and the first guidance optimization coefficient; and also parsing the follow-up time from the follow-up data, and calculating the upload delay duration based on the upload time of the follow-up data and the follow-up time, and determining the second guidance optimization coefficient based on the upload delay duration; wherein the second guidance optimization coefficient is negatively correlated with the upload delay duration; and the second health guidance data is calculated using the second guidance optimization coefficient and the third health guidance data.

6. A patient health guidance method based on follow-up data according to claim 5, characterized in that: The first health guidance data, the second health guidance data and the third health guidance data all include exercise type and exercise intensity, recipe type and recipe recommendation compliance; the first guidance optimization coefficient and the second guidance optimization coefficient are used to adjust the exercise intensity and the recipe recommendation compliance.

7. A patient health guidance system based on follow-up data, applied to a health management platform, characterized by: The system includes a processing device and a storage device, wherein the storage device stores a computer program code, and the processing device executes the computer program code stored in the storage device to implement the following steps: determining basic data of a target patient who needs health guidance, including a disease type and a recovery stage, and retrieving first health guidance data according to the disease type and the recovery stage; receiving follow-up data corresponding to the target patient uploaded by a follow-up person, extracting condition assessment data and recovery execution data from the follow-up data, and calculating a first guidance optimization coefficient according to the condition assessment data and the recovery execution data; Second health guidance data is calculated based on the first health guidance data and the first guidance optimization coefficient, and remote health guidance is performed on the target patient according to the second health guidance data.

8. An electronic device, comprising: At least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor implements the method according to any one of claims 1 to 6 when executing the computer program.

9. A computer storage medium, wherein the computer readable storage medium stores a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

10. A computer program product, comprising computer program code, characterized in that: When the computer program code is executed by a processor of an electronic device, the method according to any one of claims 1 to 6 is implemented.

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