Self-health management system for sudden deafness based on multi-source data

Abstract

The invention discloses a self-health management system for sudden deafness based on multi-source data, and belongs to the technical field of sudden deafness, and the system comprises a data collection module which is used for collecting the multi-source data of a patient; the data processing module is used for processing the patient multi-source data and determining standardized patient multi-source data; the analysis and diagnosis module is used for carrying out analysis and prediction on the standardized multi-source data of the patient and carrying out early warning on the sudden deafness diagnosis condition of the patient; and the health management module is used for formulating a self-health management scheme of sudden deafness for the patient and carrying out self-health management on the sudden deafness patient. According to the invention, the problem of poor self-health management and treatment effects of the patient caused by incapability of providing a timely and accurate sudden deafness prediction and health management scheme for the patient in the prior art is solved. According to the invention, a timely and accurate sudden deafness prediction and health management scheme can be provided for the patient, and the self-health management and treatment effect of the patient can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of sudden deafness, and in particular to a self-health management system for sudden deafness based on multi-source data. Background Art

[0002] Sudden deafness refers to sudden, unexplained sensorineural hearing loss. The main clinical manifestation is unilateral hearing loss, which may be accompanied by tinnitus, ear blockage, dizziness, nausea, vomiting, etc. To a certain extent, it greatly affects people's lives. Therefore, self-health management of sudden deafness is particularly important.

[0003] The existing ones cannot provide patients with timely and accurate predictions and health management plans for sudden deafness, resulting in poor self-health management and treatment effects for patients. Summary of the invention

[0004] The purpose of the present invention is to provide a self-health management system for sudden deafness based on multi-source data, which can provide patients with timely and accurate sudden deafness prediction and health management plans, improve patients' self-health management and treatment effects, and solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A self-health management system for sudden deafness based on multi-source data, including:

[0007] The data acquisition module is used to collect the patient's physiological parameters, environmental parameters and behavioral parameters and determine the patient's multi-source data;

[0008] A data processing module, used to process the multi-source patient data, determine standardized multi-source patient data, and store the standardized multi-source patient data;

[0009] The analysis and diagnosis module is used to analyze and predict standardized multi-source patient data and to provide early warning for the patient's sudden deafness diagnosis;

[0010] The health management module is used to develop a self-health management plan for sudden deafness patients and to perform self-health management for patients with sudden deafness.

[0011] Preferably, the data acquisition module comprises:

[0012] A physiological collection unit, used to collect physiological parameters of the patient;

[0013] Based on the smart chips and sensors built into wearable devices, the patient's electrocardiogram, electroencephalogram, blood pressure and body temperature are monitored in real time and continuously collected to obtain the patient's physiological parameters;

[0014] An environment collection unit is used to collect the patient's environmental conditions;

[0015] Based on the smart chip and sensor built into the wearable device, the patient's environmental conditions are monitored in real time and continuously collected to determine whether the patient is in a noisy environment and obtain the patient's environmental parameters;

[0016] A behavior collection unit is used to collect the patient's behavior;

[0017] Based on the smart chips and sensors built into wearable devices, the patient's behavior is monitored in real time and continuously collected to determine whether the patient often uses headphones, whether he gets enough sleep, whether he smokes and drinks, whether he has hearing loss, and whether he has tinnitus, and obtain the patient's behavior parameters;

[0018] Among them, the patient's multi-source data is determined based on the patient's physiological parameters, patient environmental parameters and patient behavioral parameters.

[0019] Preferably, the data processing module includes:

[0020] A data cleaning unit, used to clean multi-source patient data;

[0021] Obtain multi-source patient data and clean the multi-source patient data, including:

[0022] Conduct consistency checks on multi-source patient data;

[0023] According to the data consistency requirements, check whether there are inconsistent data that are useless for the self-health management of sudden deafness in the multi-source data of patients, and remove the inconsistent data that are useless for the self-health management of sudden deafness in the multi-source data of patients;

[0024] Check invalid and missing values ​​for multi-source patient data;

[0025] According to the requirements of data validity and completeness, check whether there are invalid values ​​and missing values ​​in the multi-source data of patients that are useless for the self-health management of sudden deafness, and remove the invalid values ​​and missing values ​​in the multi-source data of patients that are useless for the self-health management of sudden deafness;

[0026] Identify patient multi-source data useful for self-health management of sudden deafness.

[0027] Preferably, the data processing module further includes:

[0028] A data conversion unit, used for converting the cleaned multi-source patient data;

[0029] Obtain multi-source patient data that is useful for self-health management of sudden deafness after cleaning;

[0030] Transform multi-source patient data useful for self-health management of sudden deafness;

[0031] Unify the formats of multi-source patient data useful for self-health management of sudden deafness, and eliminate the dimensional differences among multi-source patient data useful for self-health management of sudden deafness;

[0032] Determine standardized multi-source patient data;

[0033] A data storage unit, used for storing the converted multi-source patient data;

[0034] Obtaining multi-source patient data after conversion and standardization;

[0035] The standardized multi-source patient data is stored in a designated repository.

[0036] Preferably, the analysis and diagnosis module includes:

[0037] An analysis and prediction unit, used for analyzing and predicting standardized multi-source patient data;

[0038] Obtain standardized multi-source patient data;

[0039] Based on deep learning technology, the standardized multi-source patient data is analyzed and predicted to determine whether the patient has sudden deafness and determine the patient analysis and prediction results based on multi-source data;

[0040] An early warning diagnosis unit is used to provide early warning of the patient's sudden deafness diagnosis;

[0041] Obtain patient analysis and prediction results based on multi-source data;

[0042] When a patient suffers from sudden deafness, an early warning alarm will be automatically issued to remind the patient to take action to manage their own health.

[0043] Preferably, the analysis and prediction unit comprises:

[0044] A historical data acquisition unit, used for acquiring historical patient multi-source data from a storage repository, including historical patient physiological data, historical patient environmental data and historical patient behavioral data from the historical patient multi-source data;

[0045] A behavior analysis unit, used to obtain historical patient physiological data and historical patient environmental data from historical patient multi-source data, and when the data similarity meets a preset similarity, determine the influence of different historical patient behavior data on causing sudden deafness based on historical diagnosis conditions, determine the behavior type weight of the behavior type of the historical patient behavior data, and determine the normal data range of the behavior type;

[0046] An environmental analysis unit, used to obtain historical patient physiological data and historical patient behavioral data from historical patient multi-source data, and when the data similarity meets a preset similarity, determine the influence of different historical patient environmental data on causing sudden deafness based on historical diagnosis conditions, determine the environmental type weight of the environmental type of the historical patient environmental data, and determine the normal data range of the environmental type;

[0047] A physiological analysis unit, for obtaining historical patient behavior data and historical patient environment data from historical patient multi-source data, when the data similarity meets a preset similarity, determining the influence of different historical patient physiological data on causing sudden deafness based on historical diagnosis conditions, determining the physiological type weight of the physiological type of the historical patient physiological data, and determining the normal data range of the physiological type;

[0048] A data processing unit, configured to screen the historical patient multi-source data based on the behavior type weight, the environment type weight and the physiological type weight to obtain target multi-source data useful for the diagnosis of sudden deafness, and to label the target multi-source data based on the normal data range of the behavior type, the environment type and the physiological type to obtain training data;

[0049] The diagnosis prediction unit is used to train the deep learning model based on the training data to obtain a deafness prediction model, and analyze the standardized patient multi-source data based on the deafness prediction model to obtain patient analysis prediction results.

[0050] Preferably, the health management module includes:

[0051] A program-making unit, which is used to develop a self-health management plan for sudden deafness for patients;

[0052] When the patient has sudden deafness, a self-health management plan for sudden deafness will be developed for the patient based on the patient's own situation;

[0053] Health management unit, used for self-health management of patients with sudden deafness;

[0054] Obtain a self-health management plan for sudden deafness developed for the patient;

[0055] Carry out self-health management for patients with sudden deafness according to the self-health management plan for sudden deafness developed for the patients;

[0056] A feedback display unit, used to provide feedback to patients and display the patient's analysis and prediction results and the self-health management plan for sudden deafness;

[0057] Obtain patient analysis and prediction results and develop a self-health management plan for sudden deafness for the patient;

[0058] The patient analysis and prediction results and the feedback of the self-health management plan for sudden deafness developed for the patient are displayed on the head-mounted display of the wearable device. The patient can view the real-time deafness status through the head-mounted display of the wearable device and receive customized self-health management suggestions.

[0059] Preferably, the program formulation unit includes:

[0060] The health scoring unit is used to calculate the patient's self-health management score based on the patient's physiological parameters and according to the following formula when it is determined that the patient has sudden deafness;

[0061]

[0062] Among them, F represents the patient's self-health management score, n represents the number of parameter types of physiological parameters, represents the standard physiological parameter value of the patient under the i-th parameter type, represents the maximum parameter threshold in the normal parameter range of the i-th parameter type, represents the minimum parameter threshold in the normal parameter range for the i-th parameter type, represents the standardized coefficient of the i-th parameter type, Represents a constant, with a value of 5. represents the physiological weight of the i-th parameter type;

[0063] A plan determination unit, used to determine the patient's self-health management level based on the self-health management score, and obtain an initial management plan under the self-health management level;

[0064] An environmental scoring unit, used to obtain an environmental score of the patient based on the patient's environmental parameters and according to the following formula;

[0065]

[0066] in, represents the patient's environmental score, The number of parameter types representing the environment parameters, represents the standard environmental parameter value of the patient under the jth parameter type, Indicates the normal environment parameter value under the jth parameter type, represents the environmental weight of the jth parameter type;

[0067] The plan adjustment unit is used to determine the adjustment range for each management type in the initial management plan based on the patient's environmental score, and to formulate a self-health management plan for sudden deafness for the patient based on the adjustment range.

[0068] Preferably, patients with sudden deafness should be provided with self-health management, including:

[0069] Anti-inflammatory and hormonal drugs are used to treat patients with sudden deafness, and medication is taken according to the doctor's prescription;

[0070] Low-frequency electrical stimulation, hyperbaric oxygen therapy or traditional Chinese medicine acupuncture are used to treat patients with sudden deafness, and the patients must comply with the doctor's treatment plan and receive treatment on time.

[0071] Preferably, the self-health management for patients with sudden deafness also includes:

[0072] Real-time monitoring and tracking of the self-health management of patients with sudden deafness;

[0073] Obtain the real-time self-health management status of patients with sudden deafness, analyze the real-time self-health management status of patients with sudden deafness, and evaluate the real-time treatment effect of patients with sudden deafness;

[0074] According to the real-time treatment effect of patients with sudden deafness evaluated, the self-health management plan for sudden deafness developed for the patients is adjusted in real time to adapt to the situation of patients with sudden deafness at different treatment periods.

[0075] Compared with the prior art, the present invention has the following beneficial effects:

[0076] The present invention uses the built-in smart chip and sensor of the wearable device to collect the patient's physiological parameters, environmental parameters and behavioral parameters in real time, determine the patient's multi-source data, and determine the standardized patient multi-source data by cleaning and converting the patient's multi-source data. The standardized patient multi-source data is stored for standby use. Based on deep learning technology, the standardized patient multi-source data is analyzed and predicted to determine whether the patient has sudden deafness, and the patient analysis and prediction results based on the multi-source data are determined. When the patient has sudden deafness, an early warning alarm is automatically issued, and a self-health management plan for sudden deafness is formulated for the patient, so as to perform self-health management on the sudden deafness patient, so as to provide the patient with timely and accurate sudden deafness prediction and health management plan, and improve the patient's self-health management and treatment effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1This is a structural block diagram of the self-health management system for sudden deafness based on multi-source data of the present invention. DETAILED DESCRIPTION

[0078] 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 ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0079] In order to solve the problem that the existing system cannot provide patients with timely and accurate prediction and health management solutions for sudden deafness, resulting in poor self-health management and treatment effects, please refer to Figure 1 , this embodiment provides the following technical solutions:

[0080] The self-health management system for sudden deafness based on multi-source data includes: a data acquisition module, a data processing module, an analysis and diagnosis module and a health management module.

[0081] It should be noted that the patient's physiological parameters, environmental parameters and behavioral parameters are collected through the data acquisition module to determine the patient's multi-source data; the patient's multi-source data is processed through the data processing module to determine the standardized patient's multi-source data, and the standardized patient's multi-source data is stored; the standardized patient's multi-source data is analyzed and predicted through the analysis and diagnosis module, and the patient's sudden deafness diagnosis is warned; the health management module formulates a self-health management plan for sudden deafness for the patient, and performs self-health management for patients with sudden deafness, which can provide patients with timely and accurate sudden deafness predictions and health management plans, and can improve patients' self-health management and treatment effects.

[0082] In this embodiment, as a preferred technical solution of the present invention, the data acquisition module includes:

[0083] A physiological collection unit, used to collect physiological parameters of the patient;

[0084] Based on the smart chips and sensors built into wearable devices, the patient's electrocardiogram, electroencephalogram, blood pressure and body temperature are monitored in real time and continuously collected to obtain the patient's physiological parameters;

[0085] An environment collection unit is used to collect the patient's environmental conditions;

[0086] Based on the smart chip and sensor built into the wearable device, the patient's environmental conditions are monitored in real time and continuously collected to determine whether the patient is in a noisy environment and obtain the patient's environmental parameters;

[0087] A behavior collection unit is used to collect the patient's behavior;

[0088] Based on the smart chips and sensors built into wearable devices, the patient's behavior is monitored in real time and continuously collected to determine whether the patient often uses headphones, whether he gets enough sleep, whether he smokes and drinks, whether he has hearing loss, and whether he has tinnitus, and obtain the patient's behavior parameters;

[0089] Among them, the patient's multi-source data is determined based on the patient's physiological parameters, patient environmental parameters and patient behavioral parameters.

[0090] It should be noted that the smart chips and sensors built into wearable devices can be used to collect the patient's physiological parameters, environmental parameters and behavioral parameters in real time, determine the patient's multi-source data, and provide data support for the diagnosis of the patient's sudden deafness, facilitating the subsequent prediction and diagnosis of the patient's sudden deafness.

[0091] In this embodiment, as a preferred technical solution of the present invention, the data processing module includes:

[0092] A data cleaning unit, used to clean multi-source patient data;

[0093] Obtain multi-source patient data and clean the multi-source patient data, including:

[0094] Conduct consistency checks on multi-source patient data;

[0095] According to the data consistency requirements, check whether there are inconsistent data that are useless for the self-health management of sudden deafness in the multi-source data of patients, and remove the inconsistent data that are useless for the self-health management of sudden deafness in the multi-source data of patients;

[0096] Check invalid and missing values ​​for multi-source patient data;

[0097] According to the requirements of data validity and completeness, check whether there are invalid values ​​and missing values ​​in the multi-source data of patients that are useless for the self-health management of sudden deafness, and remove the invalid values ​​and missing values ​​in the multi-source data of patients that are useless for the self-health management of sudden deafness;

[0098] Identify patient multi-source data useful for self-health management of sudden deafness.

[0099] It should be noted that data cleaning is one of the important links in data analysis, data mining and application, aiming to improve data quality and the accuracy of analysis results, including checking data consistency, processing invalid values ​​and missing values, etc.

[0100] Among them, the collected multi-source patient data may contain inconsistent data, invalid values ​​and missing values ​​that are useless for self-health management of sudden deafness. Therefore, it is necessary to remove the inconsistent data, invalid values ​​and missing values ​​in order to determine the multi-source patient data that is useful for self-health management of sudden deafness, which can improve the subsequent processing accuracy and efficiency of the multi-source patient data.

[0101] In this embodiment, as a preferred technical solution of the present invention, the data processing module further includes:

[0102] A data conversion unit, used for converting the cleaned multi-source patient data;

[0103] Obtain multi-source patient data that is useful for self-health management of sudden deafness after cleaning;

[0104] Transform multi-source patient data useful for self-health management of sudden deafness;

[0105] Unify the formats of multi-source patient data useful for self-health management of sudden deafness, and eliminate the dimensional differences among multi-source patient data useful for self-health management of sudden deafness;

[0106] Determine standardized multi-source patient data;

[0107] It should be noted that by converting the multi-source patient data that is useful for self-health management of sudden deafness, it is convenient to determine standardized multi-source patient data, so as to facilitate subsequent analysis and prediction of the standardized multi-source patient data and determine whether the patient has sudden deafness.

[0108] A data storage unit, used for storing the converted multi-source patient data;

[0109] Obtaining multi-source patient data after conversion and standardization;

[0110] The standardized multi-source patient data is stored in a designated repository.

[0111] It should be noted that after converting the patient multi-source data, the standardized patient multi-source data will be stored in a designated repository to facilitate subsequent review of the patient's actual situation and to make a health management plan suitable for the patient.

[0112] In this embodiment, as a preferred technical solution of the present invention, the analysis and diagnosis module includes:

[0113] An analysis and prediction unit, used for analyzing and predicting standardized multi-source patient data;

[0114] Obtain standardized multi-source patient data;

[0115] Based on deep learning technology, the standardized multi-source patient data is analyzed and predicted to determine whether the patient has sudden deafness and determine the patient analysis and prediction results based on multi-source data;

[0116] An early warning diagnosis unit is used to provide early warning of the patient's sudden deafness diagnosis;

[0117] Obtain patient analysis and prediction results based on multi-source data;

[0118] When a patient suffers from sudden deafness, an early warning alarm will be automatically issued to remind the patient to take action to manage their own health.

[0119] In this embodiment, as a preferred technical solution of the present invention, the analysis and prediction unit includes:

[0120] A historical data acquisition unit, used for acquiring historical patient multi-source data from a storage repository, including historical patient physiological data, historical patient environmental data and historical patient behavioral data from the historical patient multi-source data;

[0121] A behavior analysis unit, used to obtain historical patient physiological data and historical patient environmental data from historical patient multi-source data, and when the data similarity meets a preset similarity, determine the influence of different historical patient behavior data on causing sudden deafness based on historical diagnosis conditions, determine the behavior type weight of the behavior type of the historical patient behavior data, and determine the normal data range of the behavior type;

[0122] An environmental analysis unit, used to obtain historical patient physiological data and historical patient behavioral data from historical patient multi-source data, and when the data similarity meets a preset similarity, determine the influence of different historical patient environmental data on causing sudden deafness based on historical diagnosis conditions, determine the environmental type weight of the environmental type of the historical patient environmental data, and determine the normal data range of the environmental type;

[0123] A physiological analysis unit, for obtaining historical patient behavior data and historical patient environment data from historical patient multi-source data, when the data similarity meets a preset similarity, determining the influence of different historical patient physiological data on causing sudden deafness based on historical diagnosis conditions, determining the physiological type weight of the physiological type of the historical patient physiological data, and determining the normal data range of the physiological type;

[0124] A data processing unit, configured to screen the historical patient multi-source data based on the behavior type weight, the environment type weight and the physiological type weight to obtain target multi-source data useful for the diagnosis of sudden deafness, and to label the target multi-source data based on the normal data range of the behavior type, the environment type and the physiological type to obtain training data;

[0125] The diagnosis prediction unit is used to train the deep learning model based on the training data to obtain a deafness prediction model, and analyze the standardized patient multi-source data based on the deafness prediction model to obtain patient analysis prediction results.

[0126] In this embodiment, the greater the degree of influence on causing sudden deafness, the greater the corresponding weights of various types.

[0127] In this embodiment, the historical patient physiological data includes the patient's electrocardiogram, electroencephalogram, blood pressure, body temperature, etc.

[0128] In this embodiment, the historical patient behavior data includes sleeping, wearing headphones, smoking and drinking, etc.

[0129] In this embodiment, the historical patient environment data includes whether the patient is in a noisy environment, the size and type of the noise, etc.

[0130] The beneficial effects of the above design scheme are: by conducting deafness diagnosis analysis on historical patients' physiological data, historical patients' environmental data and historical patients' behavioral data in historical patients' multi-source data, the degree of influence of multi-source data on diagnosis and the standard range are determined, and the multi-source data of historical patients are screened and labeled to ensure the accuracy of the training data, the accuracy of the deafness prediction model obtained by training, and the accurate prediction of sudden deafness of patients is achieved, providing patients with ample time to prepare timely and accurate sudden deafness predictions and health management plans.

[0131] In this embodiment, as a preferred technical solution of the present invention, the health management module includes:

[0132] A program-making unit, which is used to develop a self-health management plan for sudden deafness for patients;

[0133] When the patient has sudden deafness, a self-health management plan for sudden deafness will be developed for the patient based on the patient's own situation;

[0134] Health management unit, used for self-health management of patients with sudden deafness;

[0135] Obtain a self-health management plan for sudden deafness developed for the patient;

[0136] Carry out self-health management for patients with sudden deafness according to the self-health management plan for sudden deafness developed for the patients;

[0137] A feedback display unit, used to provide feedback to patients and display the patient's analysis and prediction results and the self-health management plan for sudden deafness;

[0138] Obtain patient analysis and prediction results and develop a self-health management plan for sudden deafness for the patient;

[0139] The patient analysis and prediction results and the feedback of the self-health management plan for sudden deafness developed for the patient are displayed on the head-mounted display of the wearable device. The patient can view the real-time deafness status through the head-mounted display of the wearable device and receive customized self-health management suggestions.

[0140] In this embodiment, the plan formulation unit includes:

[0141] The health scoring unit is used to calculate the patient's self-health management score based on the patient's physiological parameters and according to the following formula when it is determined that the patient has sudden deafness;

[0142]

[0143] Among them, F represents the patient's self-health management score, n represents the number of parameter types of physiological parameters, represents the standard physiological parameter value of the patient under the i-th parameter type, represents the maximum parameter threshold in the normal parameter range of the i-th parameter type, represents the minimum parameter threshold in the normal parameter range for the i-th parameter type, represents the standardized coefficient of the i-th parameter type, Represents a constant, with a value of 5. represents the physiological weight of the i-th parameter type;

[0144] A plan determination unit, used to determine the patient's self-health management level based on the self-health management score, and obtain an initial management plan under the self-health management level;

[0145] An environmental scoring unit, used to obtain an environmental score of the patient based on the patient's environmental parameters and according to the following formula;

[0146]

[0147] in, represents the patient's environmental score, The number of parameter types representing the environment parameters, represents the standard environmental parameter value of the patient under the jth parameter type, Indicates the normal environment parameter value under the jth parameter type, represents the environmental weight of the jth parameter type;

[0148] The plan adjustment unit is used to determine the adjustment range for each management type in the initial management plan based on the patient's environmental score, and to formulate a self-health management plan for sudden deafness for the patient based on the adjustment range.

[0149] In this embodiment, the standard physiological parameter value is obtained by uniformly standardizing the physiological parameters.

[0150] In this embodiment, the standardization coefficient of the parameter type is specifically determined in such a way that when the fluctuation of the parameter type within the normal parameter range is relatively low, the standardization coefficient is small, such as body temperature; when the fluctuation of the parameter type within the normal parameter range is relatively large, the standardization coefficient is large, such as blood pressure. The specific setting is manually preset according to the actual situation.

[0151] In this embodiment, the greater the impact of a parameter type on physiological health, the greater the physiological weight of the parameter type.

[0152] In this embodiment, a matching relationship between the self-health management score and the self-health management level is preset. The higher the self-health management score, the better the patient's physical fitness, and the lower the self-health management level, the simpler the corresponding initial management plan.

[0153] In this embodiment, the initial management plan is set under a standard environment, and the environmental score corresponding to the standard environment is full marks. The lower the patient's environmental score, the greater the corresponding adjustment range.

[0154] In this embodiment, the greater the influence of the parameter type environmental parameter on the patient's deafness rehabilitation, the higher the corresponding environmental weight.

[0155] The beneficial effect of the above design scheme is: by analyzing the patient's multi-source data from both physiological and environmental aspects, a self-health management plan for sudden deafness is set up for the patient, ensuring that the self-health management plan is highly adaptable to the patient's physical condition and the environment in which he or she is located, making the self-health management plan more targeted to the patient and improving the patient's self-health management and treatment effects.

[0156] In this embodiment, as a preferred technical solution of the present invention, self-health management is performed on patients with sudden deafness, including:

[0157] Drug treatment management: Anti-inflammatory drugs and hormone drugs are used to treat patients with sudden deafness, and they are taken according to the doctor's prescription, paying attention to the use and side effects of the drugs to reduce inflammatory reactions and improve inner ear function;

[0158] Non-drug treatment management: low-frequency electrical stimulation, hyperbaric oxygen therapy or traditional Chinese medicine acupuncture are used to treat patients with sudden deafness, and the treatment plan is followed and carried out on time to promote the recovery of the cochlear nerve and improve local blood flow;

[0159] Lifestyle management: reducing noise exposure and maintaining good sleeping habits can reduce the degree of deafness and improve the patient's quality of life;

[0160] Diet management: Develop a suitable diet plan based on the patient's physical condition, avoid high-fat and high-sugar foods, and increase the intake of vegetables and fruits to ensure balanced nutrition, maintain the patient's health, and improve the patient's nutritional status;

[0161] Sports management: Aerobic exercises such as walking, swimming, and badminton can improve blood circulation and metabolism;

[0162] Psychological management: Through psychological counseling and comfort, help patients relieve negative emotions, enhance confidence in treatment, establish a positive attitude, and avoid negative emotions.

[0163] It should be noted that through drug treatment management, non-drug treatment management, lifestyle management, diet management, exercise management and psychological management of patients with sudden deafness, patients can receive comprehensive health management and improve their self-health management and treatment effects.

[0164] In this embodiment, as a preferred technical solution of the present invention, self-health management for patients with sudden deafness also includes:

[0165] Real-time monitoring and tracking of the self-health management of patients with sudden deafness;

[0166] Obtain the real-time self-health management status of patients with sudden deafness, analyze the real-time self-health management status of patients with sudden deafness, and evaluate the real-time treatment effect of patients with sudden deafness;

[0167] According to the real-time treatment effect of patients with sudden deafness evaluated, the self-health management plan for sudden deafness developed for the patients is adjusted in real time to adapt to the situation of patients with sudden deafness at different treatment periods.

[0168] It should be noted that by real-time monitoring and tracking of the self-health management of patients with sudden deafness, it is convenient to evaluate the real-time treatment effect of patients with sudden deafness. According to the real-time treatment effect of patients with sudden deafness evaluated, the self-health management plan for sudden deafness formulated for patients is adjusted in real time to adapt to the situation of patients with sudden deafness in different treatment periods. This can be more in line with the self-health management of patients with sudden deafness and improve the treatment effect of patients with sudden deafness.

[0169] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0170] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A self-health management system for sudden deafness based on multi-source data, characterized in that: include: The data acquisition module is used to collect the patient's physiological parameters, environmental parameters and behavioral parameters and determine the patient's multi-source data; A data processing module, used to process the multi-source patient data, determine standardized multi-source patient data, and store the standardized multi-source patient data; The analysis and diagnosis module is used to analyze and predict standardized multi-source patient data and to provide early warning for the patient's sudden deafness diagnosis; The health management module is used to develop a self-health management plan for sudden deafness patients and to perform self-health management for patients with sudden deafness.

2. The self-health management system for sudden deafness based on multi-source data according to claim 1 is characterized in that: The data acquisition module comprises: A physiological collection unit, used to collect physiological parameters of the patient; Based on the smart chips and sensors built into wearable devices, the patient's electrocardiogram, electroencephalogram, blood pressure and body temperature are monitored in real time and continuously collected to obtain the patient's physiological parameters; An environment collection unit is used to collect the patient's environmental conditions; Based on the smart chip and sensor built into the wearable device, the patient's environmental conditions are monitored in real time and continuously collected to determine whether the patient is in a noisy environment and obtain the patient's environmental parameters; A behavior collection unit is used to collect the patient's behavior; Based on the smart chips and sensors built into wearable devices, the patient's behavior is monitored in real time and continuously collected to determine whether the patient often uses headphones, whether he gets enough sleep, whether he smokes and drinks, whether he has hearing loss, and whether he has tinnitus, and obtain the patient's behavior parameters; Among them, the patient's multi-source data is determined based on the patient's physiological parameters, patient environmental parameters and patient behavioral parameters.

3. The self-health management system for sudden deafness based on multi-source data according to claim 2 is characterized in that: The data processing module comprises: A data cleaning unit, used to clean multi-source patient data; Obtain multi-source patient data and clean the multi-source patient data, including: Conduct consistency checks on multi-source patient data; According to the data consistency requirements, check whether there are inconsistent data that are useless for the self-health management of sudden deafness in the multi-source data of patients, and remove the inconsistent data that are useless for the self-health management of sudden deafness in the multi-source data of patients; Check invalid and missing values ​​for multi-source patient data; According to the requirements of data validity and completeness, check whether there are invalid values ​​and missing values ​​in the multi-source data of patients that are useless for the self-health management of sudden deafness, and remove the invalid values ​​and missing values ​​in the multi-source data of patients that are useless for the self-health management of sudden deafness; Identify patient multi-source data useful for self-health management of sudden deafness.

4. The self-health management system for sudden deafness based on multi-source data according to claim 3 is characterized in that: The data processing module further includes: A data conversion unit, used for converting the cleaned multi-source patient data; Obtain multi-source patient data that is useful for self-health management of sudden deafness after cleaning; Transform multi-source patient data useful for self-health management of sudden deafness; Unify the formats of multi-source patient data useful for self-health management of sudden deafness, and eliminate the dimensional differences among multi-source patient data useful for self-health management of sudden deafness; Determine standardized multi-source patient data; A data storage unit, used for storing the converted multi-source patient data; Obtaining multi-source patient data after conversion and standardization; The standardized multi-source patient data is stored in a designated repository.

5. The self-health management system for sudden deafness based on multi-source data according to claim 4 is characterized in that: The analysis and diagnosis module comprises: An analysis and prediction unit, used for analyzing and predicting standardized multi-source patient data; Obtain standardized multi-source patient data; Based on deep learning technology, the standardized multi-source patient data is analyzed and predicted to determine whether the patient has sudden deafness and determine the patient analysis and prediction results based on multi-source data; An early warning diagnosis unit is used to provide early warning of the patient's sudden deafness diagnosis; Obtain patient analysis and prediction results based on multi-source data; When a patient suffers from sudden deafness, an early warning alarm will be automatically issued to remind the patient to take action to manage their own health.

6. The self-health management system for sudden deafness based on multi-source data according to claim 5 is characterized in that: The analysis and prediction unit comprises: A historical data acquisition unit, used for acquiring historical patient multi-source data from a storage repository, including historical patient physiological data, historical patient environmental data and historical patient behavioral data from the historical patient multi-source data; A behavior analysis unit, used to obtain historical patient physiological data and historical patient environmental data from historical patient multi-source data, and when the data similarity meets a preset similarity, determine the influence of different historical patient behavior data on causing sudden deafness based on historical diagnosis conditions, determine the behavior type weight of the behavior type of the historical patient behavior data, and determine the normal data range of the behavior type; An environmental analysis unit, used to obtain historical patient physiological data and historical patient behavioral data from historical patient multi-source data, and when the data similarity meets a preset similarity, determine the influence of different historical patient environmental data on causing sudden deafness based on historical diagnosis conditions, determine the environmental type weight of the environmental type of the historical patient environmental data, and determine the normal data range of the environmental type; A physiological analysis unit, for obtaining historical patient behavior data and historical patient environment data from historical patient multi-source data, when the data similarity meets a preset similarity, determining the influence of different historical patient physiological data on causing sudden deafness based on historical diagnosis conditions, determining the physiological type weight of the physiological type of the historical patient physiological data, and determining the normal data range of the physiological type; A data processing unit, configured to screen the historical patient multi-source data based on the behavior type weight, the environment type weight and the physiological type weight to obtain target multi-source data useful for the diagnosis of sudden deafness, and to label the target multi-source data based on the normal data range of the behavior type, the environment type and the physiological type to obtain training data; The diagnosis prediction unit is used to train the deep learning model based on the training data to obtain a deafness prediction model, and analyze the standardized patient multi-source data based on the deafness prediction model to obtain patient analysis prediction results.

7. The self-health management system for sudden deafness based on multi-source data according to claim 5 is characterized in that: The health management module includes: A program-making unit, which is used to develop a self-health management plan for sudden deafness for patients; When the patient has sudden deafness, a self-health management plan for sudden deafness will be developed for the patient based on the patient's own situation; Health management unit, used for self-health management of patients with sudden deafness; Obtain a self-health management plan for sudden deafness developed for the patient; Carry out self-health management for patients with sudden deafness according to the self-health management plan for sudden deafness developed for the patients; A feedback display unit, used to provide feedback to patients and display the patient's analysis and prediction results and the self-health management plan for sudden deafness; Obtain patient analysis and prediction results and develop a self-health management plan for sudden deafness for the patient; The patient analysis and prediction results and the feedback of the self-health management plan for sudden deafness developed for the patient are displayed on the head-mounted display of the wearable device. The patient can view the real-time deafness status through the head-mounted display of the wearable device and receive customized self-health management suggestions.

8. The self-health management system for sudden deafness based on multi-source data according to claim 7 is characterized in that: The program formulation unit includes: The health scoring unit is used to calculate the patient's self-health management score based on the patient's physiological parameters and according to the following formula when it is determined that the patient has sudden deafness; Among them, F represents the patient's self-health management score, n represents the number of parameter types of physiological parameters, represents the standard physiological parameter value of the patient under the i-th parameter type, represents the maximum parameter threshold in the normal parameter range of the i-th parameter type, represents the minimum parameter threshold in the normal parameter range for the i-th parameter type, represents the standardized coefficient of the i-th parameter type, Represents a constant, with a value of 5. represents the physiological weight of the i-th parameter type; A plan determination unit, used to determine the patient's self-health management level based on the self-health management score, and obtain an initial management plan under the self-health management level; An environmental scoring unit, used to obtain an environmental score of the patient based on the patient's environmental parameters and according to the following formula; in, represents the patient's environmental score, The number of parameter types representing the environment parameters, represents the standard environmental parameter value of the patient under the jth parameter type, Indicates the normal environment parameter value under the jth parameter type, represents the environmental weight of the jth parameter type; The plan adjustment unit is used to determine the adjustment range for each management type in the initial management plan based on the patient's environmental score, and to formulate a self-health management plan for sudden deafness for the patient based on the adjustment range.

9. The self-health management system for sudden deafness based on multi-source data according to claim 7 is characterized in that: Self-health management for patients with sudden deafness, including: Anti-inflammatory and hormonal drugs are used to treat patients with sudden deafness, and medication is taken according to the doctor's prescription; Low-frequency electrical stimulation, hyperbaric oxygen therapy or traditional Chinese medicine acupuncture are used to treat patients with sudden deafness, and the patients must comply with the doctor's treatment plan and receive treatment on time.

10. The self-health management system for sudden deafness based on multi-source data according to claim 9 is characterized in that: Self-health management for patients with sudden deafness also includes: Real-time monitoring and tracking of the self-health management of patients with sudden deafness; Obtain the real-time self-health management status of patients with sudden deafness, analyze the real-time self-health management status of patients with sudden deafness, and evaluate the real-time treatment effect of patients with sudden deafness; According to the real-time treatment effect of patients with sudden deafness evaluated, the self-health management plan for sudden deafness developed for the patients is adjusted in real time to adapt to the situation of patients with sudden deafness at different treatment periods.

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