Health management scheme recommendation method and device, computer equipment and storage medium

Abstract

The invention relates to the technical field of big data, and discloses a health management scheme recommendation method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring medical question information; according to the medical question information, searching a solution of which the similarity meets the requirement from a pre-constructed case library to obtain a query result; and outputting the query result. By implementing the method provided by the invention, the efficiency of medical decision making can be improved, and high-quality medical services can be provided under the condition of limited resources.

Description

Technical Field

[0001] The present invention relates to the field of big data technology, and more specifically to a health management program recommendation method, device, computer equipment and storage medium. Background Art

[0002] In the critical field of medical decision-making, although large-scale intelligent systems such as AutoGPT and LangChain ResearchAgent have incorporated cutting-edge GPT-4 technology, they still face many challenges in achieving highly reliable and successful decisions. Such systems are often not stable enough in providing logically rigorous and trustworthy medical advice, and sometimes even provide wrong information, which poses a potential risk to the health and safety of patients.

[0003] In order to improve the performance of these systems, especially for complex medical diagnosis tasks, fine-tuning them for specific fields is considered an effective method. However, this process is not without difficulties. First, building an effective feedback mechanism, that is, allowing the system to learn from its mistakes and continuously optimize its performance, requires a lot of time and effort to collect high-quality training data. This data must not only cover a wide range of medical scenarios, but also undergo rigorous verification to ensure its accuracy and relevance.

[0004] In addition, the back-propagation algorithm used in the fine-tuning process, while essential for correcting the model’s prediction errors, also greatly increases the computational cost and resource requirements. As the model complexity increases, the required computing power will also increase accordingly, which can be a considerable expense for many medical institutions, especially in areas where resources are relatively scarce.

[0005] Therefore, it is necessary to design a new method to improve the efficiency of medical decision-making and provide high-quality medical services under limited resources. Summary of the invention

[0006] The present invention provides a health management plan recommendation method, device, computer equipment and storage medium to solve the technical problems of instability, misleading information and high computing cost of current recommendation systems.

[0007] First, it provides recommended methods for health management programs, including:

[0008] Obtain information for medical questions;

[0009] According to the medical question information, searching for solutions with similarity that meet the requirements from a pre-built case library to obtain a query result;

[0010] The query result is output.

[0011] In the second aspect, a health management program recommendation device is provided, including:

[0012] An acquisition unit, used for acquiring medical question information;

[0013] A query unit, used to search for a solution with a similarity that meets the requirements from a pre-built case library according to the medical question information, so as to obtain a query result;

[0014] An output unit is used to output the query result.

[0015] In a third aspect, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned health management plan recommendation method when executing the computer program.

[0016] In a fourth aspect, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned health management plan recommendation method are implemented.

[0017] The above-mentioned health management solution recommendation method receives medical question information, finds and outputs solutions with high similarity from the pre-built case library, so as to improve the efficiency and quality of medical decision-making. Specifically, by building a case library and using natural language processing technology to standardize medical history information, a structured solution is generated and stored in the database; when receiving the user's medical question information, the system can quickly match a solution with high similarity from the case library; this method not only improves the efficiency of medical decision-making, but also provides high-quality medical services under limited resources; in particular, the solution generation model is trained based on a large amount of health management history information and actual solutions, ensuring the accuracy and practicality of the recommendation results.

[0018] The present invention is further described below in conjunction with the accompanying drawings and specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying any creative work.

[0020] Figure 1 A schematic diagram of an application scenario of the health management solution recommendation method provided by an embodiment of the present invention;

[0021] Figure 2A flowchart of a health management program recommendation method provided by an embodiment of the present invention;

[0022] Figure 3 A schematic diagram of a sub-process of a health management program recommendation method provided by an embodiment of the present invention;

[0023] Figure 4 A flowchart of a health management program recommendation method provided by another embodiment of the present invention;

[0024] Figure 5 A schematic block diagram of a health management program recommendation device provided by an embodiment of the present invention;

[0025] Figure 6 A schematic block diagram of a case library construction unit of a health management program recommendation device provided in an embodiment of the present invention;

[0026] Figure 7 A schematic block diagram of a health management program recommendation device provided by another embodiment of the present invention;

[0027] Figure 8 is a schematic diagram of a structure of a computer device in an embodiment of the present invention;

[0028] Fig. 9 2 is another schematic diagram of the structure of a computer device in an embodiment of the present invention. DETAILED DESCRIPTION

[0029] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0030] It should be understood that when used in this specification and the appended claims, the terms "include" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0031] It should also be understood that the terms used in this specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0032] It should be further understood that the term "and / or" used in the present description and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0033] See also Figure 1 and Figure 2 , Figure 1 A schematic diagram of an application scenario of the health management plan recommendation method provided in an embodiment of the present invention. Figure 2 A schematic flow chart of a health management program recommendation method provided by an embodiment of the present invention. The health management program recommendation method is applied in a server. The server interacts with a terminal, inputs medical question information from the terminal, searches from a case library based on the information, calculates the similarity between the medical question information and the standardized results in the case library, determines the solution case corresponding to the standardized result with the highest similarity as the query result, and outputs the query result, thereby improving the efficiency of medical decision-making and providing high-quality medical services under limited resources.

[0034] Figure 2 FIG. 1 is a flow chart of a health management program recommendation method provided by an embodiment of the present invention. Figure 2 As shown, the method includes the following steps S110 to S140.

[0035] S110. Build a case library.

[0036] In this embodiment, the case library refers to a database that stores and manages specific cases. In the medical field, the case library is mainly used to store and manage diagnostic cases of various diseases and symptoms and related information to provide reference and support in future diagnosis and treatment processes.

[0037] In one embodiment, see Figure 3 , the above-mentioned step S110 may include steps S111 to S114.

[0038] S111. Obtain health management history information.

[0039] In this embodiment, the goal of this step is to obtain historical information related to health management from multiple sources. This information may include, but is not limited to, the patient's medical records, physical examination reports, lifestyle data (such as eating habits, exercise frequency, etc.), previous medical history, etc. The information obtained should be as detailed as possible so that it can be processed and analyzed more accurately in subsequent steps.

[0040] S112. Standardize the health management history information to obtain a standardized result.

[0041] In this embodiment, natural language processing technology is used to retrieve case reports, medical literature and expert opinions related to the medical diagnosis task from the health management history information, and the retrieved results are cleaned and standardized to obtain standardized results.

[0042] Use natural language processing (NLP) technology to pre-process the acquired health management history information. Specifically, extract case reports, medical literature, and expert opinions related to medical diagnosis tasks from unstructured texts. Remove irrelevant information, correct erroneous data, fill in missing values, etc. Convert the extracted information into a unified format to ensure that data from different sources are compatible with each other for subsequent processing.

[0043] S113, generating a solution for the standardized result using a solution generation model.

[0044] In this embodiment, the solution generation model is obtained by training an NLP model using a number of health management history information and corresponding actual solutions as sample sets.

[0045] In this embodiment, after standardization is completed, a pre-trained solution generation model is used to generate solutions for specific health management problems. The model is trained by a large amount of health management historical information and its corresponding actual solutions. During the model training process, machine learning algorithms (such as deep learning, support vector machines, etc.) are used to identify and learn patterns and regularities in historical data, so that reasonable solutions can be generated on new data.

[0046] S114: Storing the standardization results and corresponding solutions in a database in a structured manner to form a case library.

[0047] In this embodiment, the standardized results and the solutions generated therefrom are stored in a database in a structured manner to form a case library.

[0048] Ensure data is stored in a format that is easy to retrieve and use, such as a relational database table structure. Establish appropriate indexes to enable quick retrieval of relevant cases. Ensure data security and privacy, especially data involving personal health information.

[0049] The above steps can effectively extract valuable data from a large amount of health management history information, generate personalized health management solutions, and store them in a structured case library to provide reference and support for subsequent health management. This process not only improves the efficiency and accuracy of health management, but also provides a scientific basis for medical decision-making.

[0050] In addition, when the training scenario generates the model, human expert knowledge related to the specific medical diagnosis task is retrieved from the enterprise database. For example, if the task is to diagnose a rare skin disease, all relevant case reports, medical literature, and expert opinions can be extracted from the database. Next, natural language processing (NLP) models such as BERT or its variants are used to understand and analyze medical record text. These models are able to capture medical terms, symptom descriptions, and treatment histories in medical records to assist in building a preliminary diagnosis.

[0051] The training process uses an automatic iteration approach, that is, based on the preliminary solution, the model is trained through programming and debugging. This includes adjusting model parameters, selecting appropriate features, and optimizing algorithms. Specifically consider the following performance indicators to determine whether the model has converged and whether the output solution can be stored in the case library.

[0052] Accuracy: Measures the proportion of correct predictions made by the model, i.e. the number of samples successfully predicted divided by the total number of samples. The recommended threshold is ≥ 90% to ensure that most predictions are correct.

[0053] Recall: measures the proportion of actual positive cases predicted by the model, i.e. TP / (TP+FN), where TP is a true positive case and FN is a false negative case. The recommended threshold is ≥85% to ensure that most actual positive cases can be correctly identified.

[0054] Root Mean Square Error (RMSE): The square root of the MSE, measuring the error in the same units as the original data. The recommended threshold is ≤ 0.1 to ensure that the gap between the predicted value and the actual value is small.

[0055] Silhouette Coefficient: Measures the similarity of a sample to its own category and the difference between it and other categories, ranging from -1 to 1. The larger the value, the better the clustering effect. The recommended threshold is ≥0.6 to ensure a good clustering effect.

[0056] BLEU: A metric used to evaluate the quality of machine translation, calculated by calculating the overlap between the machine-translated text and a set of reference translations. The recommended threshold is ≥ 0.7 to ensure that the generated text is highly consistent with the reference text.

[0057] ROUGE: A metric used to evaluate automatic summarization and machine translation, focusing on recall. The recommended threshold is ≥ 0.7 to ensure that the generated summary or translation contains most of the important information.

[0058] Limit of Detection (LOD): The measured value obtained by a given measurement procedure, for which the probability of misjudging the presence of a certain component in a given substance is α, and the probability of misjudging the absence of the component is β. The recommended thresholds are α≤0.05 and β≤0.1 to ensure the reliability and accuracy of the test results.

[0059] After multiple iterations, the best solution is selected and saved in the case library. The case library will contain diagnosis cases of various diseases and symptoms, as well as corresponding solutions and performance indicators. As new medical discoveries and clinical data accumulate, the case library needs to be continuously updated and optimized to keep its information timely and accurate.

[0060] By setting the above processes and performance indicators, we can ensure the effectiveness and reliability of the model in medical diagnosis tasks, while continuously optimizing and improving the performance of the model.

[0061] S120. Obtain medical question information.

[0062] In this embodiment, at this stage, the system needs to collect specific medical questions from the user through one or more methods (such as online forms, telephone consultations, instant messages, etc.). This information includes but is not limited to the user's symptom description, past medical history, current health status, and any other relevant information. The purpose is to understand the user's problem as comprehensively as possible in order to provide more accurate help.

[0063] S130, searching for solutions with similarity that meet the requirements from a pre-built case library according to the medical question information to obtain a query result.

[0064] In this embodiment, natural language processing is used to analyze the medical question information provided by the user and compare it with a large number of pre-stored medical case libraries. The goal is to find one or more cases from the case library that best match the user's situation, so as to obtain possible diagnostic suggestions or treatment plans. This process depends not only on the accuracy of the technology, but also on the quality and coverage of the case library.

[0065] Specifically, the keywords of each case can be determined from the case library, and the cosine similarity between the medical question information and the keywords can be calculated to determine the solution corresponding to the case with the highest similarity, which is used as the query result.

[0066] S140: Output the query result.

[0067] In this embodiment, the query results are provided to the user. This can be done in a variety of ways, such as email, SMS, in-app message, or directly displayed on the website. The output should be clear and concise, and complex medical terms should be explained or linked to further information resources. In addition, it can also include suggestions for the user to take the next step, such as making an appointment with a doctor or undergoing a specific examination.

[0068] The above-mentioned health management solution recommendation method receives medical question information, finds and outputs solutions with high similarity from the pre-built case library, so as to improve the efficiency and quality of medical decision-making. Specifically, by building a case library and using natural language processing technology to standardize medical history information, a structured solution is generated and stored in the database; when receiving the user's medical question information, the system can quickly match a solution with high similarity from the case library; this method not only improves the efficiency of medical decision-making, but also provides high-quality medical services under limited resources; in particular, the solution generation model is trained based on a large amount of health management history information and actual solutions, ensuring the accuracy and practicality of the recommendation results.

[0069] Figure 4 FIG. 1 is a flow chart of a health management program recommendation method provided by another embodiment of the present invention. Figure 4 As shown, the health management program recommendation method of this embodiment includes steps S210-S250. Steps S210-S240 are similar to steps S110-S140 in the above embodiment, and will not be repeated here. The following is a detailed description of step S250 added in this embodiment.

[0070] S250: Update the case library using the query result and the medical question information.

[0071] In this embodiment, the query result and the medical question information are saved in a case library.

[0072] Specifically, when the system receives a patient's medical problem (such as a new skin lesion report), it automatically performs a retrieval process aimed at finding successful cases in the case library that are most similar to the current problem. These cases not only include detailed medical history information, but also cover the final diagnosis results and effective treatment plans. Once a suitable case is matched, the system will quickly generate specific health management recommendations for the current patient based on the data of these successful cases. These recommendations typically include possible diagnoses of the disease, recommended treatments, and measures to prevent future health problems. This approach significantly reduces the time and resources required to analyze each new case from scratch, greatly improving the speed and quality of medical decision-making.

[0073] In addition, a function is designed to facilitate users (whether doctors or patients) to provide feedback. This feedback can be an evaluation of the suggestions generated by the system or new findings or updated information about a specific case. Through online learning algorithms or regular batch processing updates, this feedback is effectively integrated into the system to help it continue to improve. As new data is added and user feedback accumulates, the solution generation model can self-adjust and optimize to better adapt to the latest developments in the medical field. This dynamic update capability ensures that the system can maintain a high degree of accuracy and practicality even in the face of a changing medical environment and emerging health challenges.

[0074] The above method integrates advanced AI technology and a huge skin disease case library. The intelligent diagnosis system can quickly respond to the consultation needs of doctors and patients and provide immediate diagnostic advice and support. This automated and intelligent approach significantly shortens the diagnosis cycle and improves the overall efficiency of the medical process.

[0075] The system greatly reduces the reliance on professional medical personnel and effectively reduces human resource costs through highly automated diagnostic processes and case library support. In addition, it also reduces excessive examinations and unnecessary treatments by accurately matching patient symptoms with historical cases, further saving medical resources and reducing hospital operating costs.

[0076] Using deep learning and big data analysis technology, the intelligent diagnosis system can integrate multiple diagnostic bases, such as symptoms, signs, laboratory test results, etc., and combine the latest medical research results to provide doctors with more accurate diagnostic references. This not only improves the accuracy of diagnosis, but also effectively reduces the probability of misdiagnosis and missed diagnosis.

[0077] The system can provide tailored health management suggestions based on each patient's specific health status and lifestyle habits, helping patients better manage their health. This feature not only improves patients' treatment experience and satisfaction, but also helps maintain long-term patient relationships and increase patients' trust and loyalty to medical institutions.

[0078] With the continuous progress of medical research and the diversification of patient needs, the intelligent diagnosis system has a strong self-learning ability, can continuously absorb new medical knowledge and technological advances, and timely adjust and optimize its diagnosis model. In this way, the system can always maintain the most cutting-edge service level, flexibly respond to market changes, and ensure that it occupies a favorable position in the fiercely competitive medical market.

[0079] Figure 5 is a schematic block diagram of a health management program recommendation device 300 provided in an embodiment of the present invention. Figure 5As shown, corresponding to the above health management program recommendation method, the present invention also provides a health management program recommendation device 300. The health management program recommendation device 300 includes a unit for executing the above health management program recommendation method, and the device can be configured in a server. Figure 5 The health management plan recommendation device 300 includes an acquisition unit 302 , a query unit 303 and an output unit 304 .

[0080] The acquisition unit 302 is used to acquire medical question information; the query unit 303 is used to find a solution with a similarity that meets the requirements from a pre-built case library according to the medical question information to obtain a query result; and the output unit 304 is used to output the query result.

[0081] In one embodiment, if Figure 5 As shown, the above-mentioned device also includes:

[0082] The construction unit 301 is used to construct a case library.

[0083] In one embodiment, if Figure 6 As shown, the construction unit 301 includes a historical information acquisition subunit 3011 , a standardization subunit 3012 , a solution generation subunit 3013 and a storage subunit 3014 .

[0084] The historical information acquisition subunit 3011 is used to acquire health management historical information; the standardization subunit 3012 is used to standardize the health management historical information to obtain standardized results; the solution generation subunit 3013 is used to generate solutions for the standardized results using a solution generation model; the storage subunit 3014 is used to store the standardized results and corresponding solutions in a database in a structured manner to form a case library.

[0085] In one embodiment, the standardization subunit 3012 is used to use natural language processing technology to retrieve case reports, medical literature and expert opinions related to medical diagnosis tasks from the health management history information, and clean and standardize the retrieved results to obtain standardized results.

[0086] Figure 7 FIG. 3 is a schematic block diagram of a health management program recommendation device 300 provided in another embodiment of the present invention. Figure 7 As shown, the health management program recommendation device 300 of this embodiment is based on the above embodiment and adds an updating unit 305.

[0087] The updating unit 305 is used to update the case library using the query result and the medical question information.

[0088] In one embodiment, the updating unit 305 is used to save the query result and the medical question information into the case library.

[0089] It should be noted that those skilled in the art can clearly understand that the specific implementation process of the above-mentioned health management program recommendation device 300 and each unit can refer to the corresponding description in the aforementioned method embodiment, and for the convenience and brevity of description, it will not be repeated here.

[0090] Each module in the health management program recommendation device 300 can be implemented in whole or in part by software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module.

[0091] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 8 As shown. The computer device includes a processor, a memory, a network interface and a database connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile and / or volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external client through a network connection. When the computer program is executed by the processor, it implements the functions or steps of a health management program recommendation method service side.

[0092] In one embodiment, a computer device is provided. The computer device may be a client, and its internal structure diagram may be as follows: Fig. 9 As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external server via a network connection. When the computer program is executed by the processor, it implements the functions or steps of a client side of a health management program recommendation method

[0093] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program:

[0094] Acquire medical question information; search for solutions with similarity that meet the requirements from a pre-built case library based on the medical question information to obtain query results; and output the query results.

[0095] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0096] Acquire medical question information; search for solutions with similarity that meet the requirements from a pre-built case library based on the medical question information to obtain query results; and output the query results.

[0097] It should be noted that the above functions or steps that can be implemented by the computer-readable storage medium or computer device can refer to the relevant descriptions on the server side and the client side in the aforementioned method embodiment. To avoid repetition, they will not be described one by one here.

[0098] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0099] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0100] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

[0101] The non-Company software tools or components appearing in the embodiments of this application are merely examples and do not represent actual use.

Claims

1. A health management program recommendation method, characterized in that: include: Obtain information for medical questions; According to the medical question information, searching for solutions with similarity that meet the requirements from a pre-built case library to obtain a query result; The query result is output.

2. The health management program recommendation method according to claim 1, characterized in that: Before obtaining the medical question information, the following steps are also included: Build a case library.

3. The health management program recommendation method according to claim 2, characterized in that: The construction of the case library includes: Obtain health management history information; Standardizing the health management history information to obtain a standardized result; Generate a solution for the standardized result using a solution generation model; The standardized results and corresponding solutions are stored in a database in a structured manner to form a case library.

4. The health management program recommendation method according to claim 3, characterized in that: The step of normalizing the health management history information to obtain a standardized result includes: Natural language processing technology is used to retrieve case reports, medical literature and expert opinions related to the medical diagnosis task from the health management history information, and the retrieved results are cleaned and standardized to obtain standardized results.

5. The health management program recommendation method according to claim 3, characterized in that: The solution generation model is obtained by training the NLP model using a number of health management history information and corresponding actual solutions as sample sets.

6. The health management program recommendation method according to claim 1, characterized in that: After outputting the query result, the method further includes: The case library is updated using the query result and the medical question information.

7. The health management program recommendation method according to claim 6, characterized in that: The updating of the case database using the query result and the medical question information includes: The query result and the medical question information are saved in a case library.

8. A device for recommending health management plans, characterized in that: include: An acquisition unit, used for acquiring medical question information; A query unit, used to search for a solution with a similarity that meets the requirements from a pre-built case library according to the medical question information, so as to obtain a query result; An output unit is used to output the query result.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the health management program recommendation method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the health management program recommendation method according to any one of claims 1 to 7 are implemented.

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