Remote health management method and system for patients with rheumatic diseases

Abstract

The present invention relates to a remote health management method and system for rheumatism patients, and relates to the field of medical technology. The method comprises: obtaining the current geographical location and rheumatism type of the target patient; collecting historical weather data of the current geographical location and predicting short-term weather data; inputting the rheumatism type and the historical weather data into a health management model to obtain first health data; inputting the rheumatism type and the short-term weather data into the health management model to obtain second health data; generating early warning information according to the first health data and the second health data; transmitting the early warning information to a health management system to achieve timely and efficient remote health management. The present invention can perform timely and effective remote health management on rheumatism patients.

Description

Technical Field

[0001] The present invention relates to the field of medical technology, and in particular to a remote health management method, system, electronic device and non-transitory computer-readable storage medium for patients with rheumatic diseases. Background Art

[0002] Nowadays, rheumatism is a common disease with a certain degree of complexity in its causes. When patients work in cold, dark, and humid outdoor environments for a long time, such as fishing, cold storage, and washing clothes, cold and damp air enter the body, making them more likely to suffer from rheumatic diseases.

[0003] However, patients often need to travel to other places for business or visit friends due to work or life needs. The existing remote health management system usually monitors at home, resulting in insufficient targeting and efficiency of health management intervention measures, affecting the quality and effectiveness of care. Summary of the invention

[0004] In view of the technical problems existing in the prior art, the present invention provides a timely and efficient remote health management method, system, electronic device and non-transitory computer-readable storage medium for rheumatic disease patients.

[0005] The technical solution of the present invention to solve the above technical problems is as follows:

[0006] The present invention provides a remote health management method for patients with rheumatism, the method comprising:

[0007] Obtain the current geographic location of the target patient;

[0008] Collecting historical weather data of the current geographical location; the historical weather data includes historical temperature, historical humidity and historical air pressure;

[0009] Predicting short-term weather data for the current geographical location; the short-term weather data includes short-term temperature, short-term humidity and short-term air pressure;

[0010] obtaining the type of rheumatic disease of the target patient;

[0011] Inputting the rheumatism type and the historical weather data into a health management model to obtain first health data;

[0012] Inputting the rheumatism type and the short-term weather data into the health management model to obtain second health data;

[0013] generating warning information according to the first health data and the second health data;

[0014] The warning information is transmitted to the health management system to realize remote health management.

[0015] Optionally, the predicting short-term weather data of the current geographical location includes:

[0016] The historical weather data is input into a weather prediction model to predict short-term weather data for the current geographical location.

[0017] Optionally, before obtaining the current geographic location of the target patient, an FNN model is trained, and the trained FNN model is used as the health management model.

[0018] Optionally, the step of inputting the rheumatism type and the historical weather data into a health management model to obtain first health data includes:

[0019] The rheumatism type and the historical weather data are input into the FNN model to obtain first health data; wherein the first health data includes a first health degree and a first health label.

[0020] Optionally, the step of inputting the rheumatism type and the short-term weather data into the health management model to obtain the second health data comprises:

[0021] The rheumatism type and the short-term weather data are input into the FNN model to obtain second health data; wherein the second health data includes a second health degree and a second health label.

[0022] Optionally, generating warning information according to the first health data and the second health data includes:

[0023] generating a first warning value based on the first health level and the second health level;

[0024] generating a second warning value based on the first health tag and the second health tag;

[0025] Warning information is generated according to the first warning value and the second warning value.

[0026] Optionally, generating a first warning value based on the first health level and the second health level includes:

[0027] Preset safety interval;

[0028] If both the first health level and the second health level are within the safety range, the first warning value is calculated according to the following formula:

[0029] ;

[0030] Among them, E represents the first warning value, H1 represents the first health level, H2 represents the second health level, α and β represent the first weight and the second weight respectively.

[0031] Optionally, generating a second warning value based on the first health tag and the second health tag includes:

[0032] Calculating the cosine similarity between the first health label and the second health label;

[0033] If the cosine similarity is greater than the similarity threshold, the FNN model is retrained; otherwise, the cosine similarity is used as the second warning value.

[0034] Optionally, generating warning information according to the first warning value and the second warning value includes:

[0035] If either the first health level or the second health level is not within the safety range, generating a first warning message;

[0036] Conversely, the first warning value and the second warning value are added to obtain a third warning value;

[0037] Second warning information is generated according to the third warning value.

[0038] The present invention also provides a remote health management system for patients with rheumatism, the system comprising:

[0039] A first data acquisition module is used to obtain the current geographic location of the target patient;

[0040] A data collection module, used to collect historical weather data of the current geographical location; the historical weather data includes historical temperature, historical humidity and historical air pressure;

[0041] A weather forecast module, used to forecast short-term weather data of the current geographical location; the short-term weather data includes short-term temperature, short-term humidity and short-term air pressure;

[0042] A second data acquisition module is used to acquire the type of rheumatism of the target patient;

[0043] A first health management module, used for inputting the rheumatism type and the historical weather data into a health management model to obtain first health data;

[0044] A second health management module, inputting the rheumatism type and the short-term weather data into the health management model to obtain second health data;

[0045] an early warning module, configured to generate early warning information according to the first health data and the second health data;

[0046] The data transmission module is used to transmit the warning information to the health management system to realize remote health management.

[0047] In addition, to achieve the above-mentioned purpose, the present invention also proposes an electronic device, comprising: a memory for storing a computer software program; a processor for reading and executing the computer software program, thereby realizing a remote health management method for rheumatic patients as described above.

[0048] In addition, to achieve the above-mentioned purpose, the present invention also proposes a non-transitory computer-readable storage medium, in which a computer software program is stored. When the computer software program is executed by a processor, it implements a remote health management method for rheumatic patients as described above.

[0049] The beneficial effects of the present invention are:

[0050] (1) The present invention obtains the current geographic location and rheumatism type of the target patient; collects historical weather data of the current geographic location and predicts short-term weather data; inputs the rheumatism type and the historical weather data into the health management model to obtain first health data; inputs the rheumatism type and the short-term weather data into the health management model to obtain second health data; generates warning information based on the first health data and the second health data; and transmits the warning information to the health management system. It can be seen that if the patient changes location, the present invention can achieve timely and efficient remote health management.

[0051] (2) The present invention trains an FFN model as a health management model, the model includes an input layer, multiple fully connected layers, a data output layer and a classification output layer; in the health management process, the rheumatism type and the historical weather data are input into the FNN model to obtain first health data including a first health degree and a first health label; the rheumatism type and the short-term weather data are input into the FNN model to obtain a second health degree and a second health label. By introducing the FFN model and the composite output data, the impact of the current geographical location on the patient's health can be more accurately predicted.

[0052] (3) The present invention generates a first warning value based on the first health level and the second health level; generates a second warning value based on the first health tag and the second health tag; and generates warning information based on the first warning value and the second warning value. In this way, the health level and the health tag are combined to improve the accuracy of the warning information.

[0053] (4) At the same time, the present invention also calculates the cosine similarity between the first health label and the second health label; if the cosine similarity is greater than the similarity threshold, the FNN model is retrained, thereby improving the prediction accuracy of the FNN model. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1A scene diagram of a remote health management method for rheumatic patients provided by the present invention;

[0055] Figure 2 A flowchart of a remote health management method for rheumatic patients provided by the present invention;

[0056] Figure 3 A schematic diagram of the structure of a remote health management system for rheumatic patients provided by the present invention;

[0057] Figure 4 A schematic diagram of the hardware structure of a possible electronic device provided by the present invention;

[0058] Figure 5 A schematic diagram of the hardware structure of a possible computer-readable storage medium provided by the present invention. DETAILED DESCRIPTION

[0059] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0060] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0061] In the description of the present invention, the term "for example" is used to mean "used as an example, illustration or explanation". Any embodiment described as "for example" in the present invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any technician in the field to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present invention.

[0062] See also Figure 1 , Figure 1This is a scene diagram of a remote health management method for rheumatic patients provided by the present invention. Figure 1 As shown, the terminal and the server are connected via a network, such as a wired or wireless network connection. The terminal may include but is not limited to portable terminals such as mobile phones and tablets installed with various network platform applications, as well as fixed terminals such as computers, query machines, and advertising machines. The server provides users with various business services, including service push servers, user recommendation servers, etc.

[0063] It should be noted that Figure 1 The scenario diagram of a remote health management method for rheumatic patients shown is only an example. The terminal, server and application scenario described in the embodiment of the present invention are intended to more clearly illustrate the technical solution of the embodiment of the present invention, and do not generate limitations on the technical solution provided by the embodiment of the present invention. Ordinary technicians in this field can know that with the evolution of the system and the emergence of new business scenarios, the technical solution provided by the embodiment of the present invention is also applicable to similar technical problems.

[0064] Among them, the terminal can be used for:

[0065] Obtain the current geographic location of the target patient;

[0066] Collecting historical weather data of the current geographical location;

[0067] forecasting short-term weather data for said current geographic location;

[0068] obtaining the type of rheumatic disease of the target patient;

[0069] Inputting the rheumatism type and the historical weather data into a health management model to obtain first health data;

[0070] Inputting the rheumatism type and the short-term weather data into the health management model to obtain second health data;

[0071] generating warning information according to the first health data and the second health data;

[0072] The warning information is transmitted to the health management system to realize remote health management.

[0073] See also Figure 2 , provides a flowchart of a remote health management method for rheumatic patients of the present invention, comprising the following steps:

[0074] Step 201: Obtain the current geographic location of the target patient.

[0075] Among them, the current geographic location of the target patient can be obtained by using GPS positioning and other methods.

[0076] Optionally, the health management system may automatically obtain the current geographic location of the target patient in real time, or the target patient may click a "location detection" button in the health management system to wake up the positioning function of the system.

[0077] In one embodiment, before step 201, a FNN model (Fully Connected Neural Network) is trained, wherein the FNN model includes an input layer, multiple fully connected layers, a data output layer, and a classification output layer, and the trained model is used as a health management model for subsequent use.

[0078] Step 202: Collect historical weather data for the current geographical location.

[0079] The historical weather data may be weather data for a historical week or a historical month.

[0080] Optionally, the historical weather data includes historical temperature, historical humidity and historical air pressure.

[0081] Step 203: predicting short-term weather data for the current geographical location.

[0082] Among them, short-term weather data can be weather data for the next few periods or the next week.

[0083] Optionally, the short-term weather data includes short-term temperature, short-term humidity and short-term air pressure.

[0084] Optionally, the target patient can directly input the required short-term weather data in the health management system; if the target patient inputs the required short-term weather data for 7 days, the weather data within 7 days will be predicted.

[0085] Optionally, the target patient can input the length of stay at the current location in the health management system, and the system determines the short-term weather data that needs to be predicted based on the length of stay; if the target patient inputs a length of stay of 6-7 days, the weather data within 7 days will be predicted.

[0086] In one implementation, the historical weather data is input into a weather prediction model to predict short-term weather data for the current geographic location.

[0087] Among them, the weather forecast model is specifically the ARIMA model (AutoRegressiveIntegrated Moving Average Model), which inputs historical temperature, historical humidity and historical air pressure into the LSTM model to predict short-term weather data.

[0088] Step 204: Obtain the rheumatic disease type of the target patient.

[0089] Among them, rheumatic disease types may include diffuse connective tissue disease, diseases with joint symptoms, diseases without joint symptoms, etc.

[0090] Step 205: Input the rheumatism type and the historical weather data into a health management model to obtain first health data.

[0091] In one embodiment, the rheumatism type and the historical weather data are input into the FNN model to obtain first health data; wherein the first health data includes a first health degree and a first health label.

[0092] Optionally, the input layer of the FNN model is used to receive the rheumatism type and the historical weather data, the data output layer is used to output the first health level, and the classification output layer is used to output the first health label.

[0093] Step 206: Input the rheumatism type and the short-term weather data into the health management model to obtain second health data.

[0094] In one embodiment, the rheumatic disease type and the short-term weather data are input into the FNN model to obtain second health data; wherein the second health data includes a second health degree and a second health label.

[0095] Optionally, the input layer of the FNN model is used to receive the rheumatic disease type and the short-term weather data, the data output layer is used to output the second health level, and the classification output layer is used to output the second health label.

[0096] Step 207: Generate warning information based on the first health data and the second health data.

[0097] In one implementation, step 207 may specifically include the following steps:

[0098] A first warning value is generated based on the first health level and the second health level.

[0099] Based on the first health label and the second health label, a second warning value is generated.

[0100] Warning information is generated according to the first warning value and the second warning value.

[0101] Optionally, a safety interval is preset; if both the first health level and the second health level are within the safety interval, a first warning value is calculated according to the following formula:

[0102] ;

[0103] Among them, E represents the first warning value, H1 represents the first health level, H2 represents the second health level, α and β represent the first weight and the second weight respectively.

[0104] Among them, the safety interval can be automatically set by the system.

[0105] Optionally, the cosine similarity between the first health label and the second health label is calculated; if the cosine similarity is greater than a similarity threshold, the FNN model is retrained; otherwise, the cosine similarity is used as a second warning value.

[0106] Among them, the similarity threshold can be automatically set by the system.

[0107] Optionally, if either the first health level or the second health level is not within the safety range, a first warning message is generated; otherwise, the first warning value and the second warning value are added to obtain a third warning value; and a second warning message is generated based on the third warning value.

[0108] Among them, the first warning information can be "the current location is not suitable for the patient to stay"; the second warning information can be "the current location score is xx, please ask the doctor for further evaluation". For example, if the third warning value is 1.2, the second warning information is "the current location score is 1.2, please ask the doctor for further evaluation".

[0109] Step 208: Transmit the warning information to the health management system to achieve remote health management.

[0110] Optionally, if the first warning information is transmitted to the health management system, the text is marked in red; if the second warning information is transmitted to the health management system, the text is marked in green.

[0111] Furthermore, the health management system can assign departments and doctors to target patients based on early warning information, and arrange online consultations or offline consultation services.

[0112] Therefore, if the patient changes location, the present invention can achieve timely and efficient remote health management.

[0113] See also Figure 3 , Figure 3 A schematic structural diagram of a remote health management system for rheumatic patients provided by the present invention.

[0114] like Figure 3 As shown, a remote health management system for rheumatic patients proposed in an embodiment of the present invention includes:

[0115] A first data acquisition module is used to obtain the current geographic location of the target patient;

[0116] A data collection module, used to collect historical weather data of the current geographical location;

[0117] A weather forecast module, used to forecast short-term weather data for the current geographical location;

[0118] A second data acquisition module is used to acquire the type of rheumatism of the target patient;

[0119] A first health management module, used for inputting the rheumatism type and the historical weather data into a health management model to obtain first health data;

[0120] A second health management module, inputting the rheumatism type and the short-term weather data into the health management model to obtain second health data;

[0121] an early warning module, configured to generate early warning information according to the first health data and the second health data;

[0122] The data transmission module is used to transmit the warning information to the health management system to realize remote health management.

[0123] See also Figure 4 , Figure 4 Schematic diagram of an electronic device provided by an embodiment of the present invention. Figure 4 As shown, an embodiment of the present invention provides an electronic device 400, including a memory 410, a processor 420, and a computer program 411 stored in the memory 410 and executable on the processor 420. When the processor 420 executes the computer program 411, the following steps are implemented:

[0124] Obtain the current geographic location of the target patient;

[0125] Collecting historical weather data of the current geographical location;

[0126] forecasting short-term weather data for said current geographic location;

[0127] obtaining the type of rheumatic disease of the target patient;

[0128] Inputting the rheumatism type and the historical weather data into a health management model to obtain first health data;

[0129] Inputting the rheumatism type and the short-term weather data into the health management model to obtain second health data;

[0130] generating warning information according to the first health data and the second health data;

[0131] The warning information is transmitted to the health management system to realize remote health management.

[0132] See also Figure 5 , Figure 5 A schematic diagram of an embodiment of a computer-readable storage medium provided in an embodiment of the present invention. Figure 5 As shown, this embodiment provides a computer-readable storage medium 500, on which a computer program 411 is stored. When the computer program 411 is executed by a processor, the following steps are implemented:

[0133] Obtain the current geographic location of the target patient;

[0134] Collecting historical weather data of the current geographical location;

[0135] forecasting short-term weather data for said current geographic location;

[0136] obtaining the type of rheumatic disease of the target patient;

[0137] Inputting the rheumatism type and the historical weather data into a health management model to obtain first health data;

[0138] Inputting the rheumatism type and the short-term weather data into the health management model to obtain second health data;

[0139] generating warning information according to the first health data and the second health data;

[0140] The warning information is transmitted to the health management system to realize remote health management.

[0141] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0142] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0143] 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 computer, 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 system that specifies the functions of a box or multiple boxes.

[0144] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction system, which is implemented in the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0145] 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. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0146] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0147] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A remote health management method for patients with rheumatic diseases, characterized in that: The method comprises: Train the FNN model and use the trained FNN model as a health management model; Obtain the current geographic location of the target patient; Collecting historical weather data of the current geographical location; forecasting short-term weather data for said current geographic location; obtaining the type of rheumatic disease of the target patient; Inputting the rheumatism type and the historical weather data into a health management model to obtain first health data; comprising: Inputting the rheumatism type and the historical weather data into the FNN model to obtain first health data; wherein the first health data includes a first health degree and a first health label; Inputting the rheumatism type and the short-term weather data into the health management model to obtain second health data; comprising: Inputting the rheumatism type and the short-term weather data into the FNN model to obtain second health data; wherein the second health data includes a second health degree and a second health label; Generating warning information according to the first health data and the second health data; comprising: generating a first warning value based on the first health level and the second health level; generating a second warning value based on the first health tag and the second health tag; generating warning information according to the first warning value and the second warning value; The generating a first warning value based on the first health level and the second health level includes: Preset safety interval; If both the first health level and the second health level are within the safety range, the first warning value is calculated according to the following formula: ； Wherein, E represents the first warning value, H1 represents the first health level, H2 represents the second health level, α and β represent the first weight and the second weight respectively; The generating a second warning value based on the first health tag and the second health tag includes: Calculating the cosine similarity between the first health label and the second health label; If the cosine similarity is greater than the similarity threshold, the FNN model is retrained; otherwise, the cosine similarity is used as the second warning value; The generating warning information according to the first warning value and the second warning value includes: If either the first health level or the second health level is not within the safety range, generating a first warning message; Conversely, the first warning value and the second warning value are added to obtain a third warning value; generating second warning information according to the third warning value; The warning information is transmitted to the health management system to realize remote health management.

2. The remote health management method for rheumatic patients according to claim 1, characterized in that: The historical weather data includes historical temperature, historical humidity and historical air pressure; the short-term weather data includes short-term temperature, short-term humidity and short-term air pressure.

3. The remote health management method for rheumatic patients according to claim 1, characterized in that: The short-term weather data for the current geographical location is predicted, including: The historical weather data is input into a weather prediction model to predict short-term weather data for the current geographical location.

4. A remote health management system for patients with rheumatism, used to implement the remote health management method for patients with rheumatism as claimed in claim 1, the system comprising: A first data acquisition module is used to obtain the current geographic location of the target patient; A data collection module, used to collect historical weather data of the current geographical location; The historical weather data includes historical temperature, historical humidity and historical air pressure; A weather forecast module, used to forecast short-term weather data of the current geographical location; the short-term weather data includes short-term temperature, short-term humidity and short-term air pressure; A second data acquisition module is used to acquire the type of rheumatism of the target patient; A first health management module, used for inputting the rheumatism type and the historical weather data into a health management model to obtain first health data; A second health management module, inputting the rheumatism type and the short-term weather data into the health management model to obtain second health data; an early warning module, configured to generate early warning information according to the first health data and the second health data; The data transmission module is used to transmit the warning information to the health management system to realize remote health management.

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