Intelligent diagnosis auxiliary system and method for children respiratory diseases

Abstract

The invention relates to the technical field of children respiratory diseases, and discloses an intelligent diagnosis auxiliary system and method for children respiratory diseases. The intelligent diagnosis auxiliary system comprises a physiological data acquisition module, an environmental data acquisition module, a children data analysis module and an auxiliary early warning module. According to the system, a child physiological risk coefficient is obtained by analyzing a child sleep coefficient, a child motion coefficient and a child symptom coefficient, meanwhile, all activity sites of a current child in a week and risk values in the sites are obtained, and a child environment risk coefficient is obtained through comprehensive calculation; the child physiological risk coefficient and the child environment risk coefficient are synthesized to obtain a comprehensive risk coefficient, and if the comprehensive risk coefficient exceeds a set threshold value, a part issued by a patient with the largest similar value with the comprehensive risk coefficient of the current child and the same age is taken to be displayed to a doctor. Therefore, the doctor can quickly judge and diagnose the pathogenesis of the child patient.

Description

Technical Field

[0001] The present invention relates to the technical field of children's respiratory diseases, and in particular to an intelligent diagnosis assistance system and method for children's respiratory diseases. Background Art

[0002] Respiratory diseases are common diseases, mainly including pneumonia, tuberculosis, colds, rhinitis, bronchitis, tracheitis, cor pulmonale, asthma, etc. The main symptoms are cough, sputum, hemoptysis, chest pain, dyspnea, etc. Respiratory diseases account for a high proportion of the mortality rate of major diseases among urban residents and rural residents in some areas. Compared with adults, children's lung metabolic function is not fully developed, their self-defense ability is weak, and they are easily affected by adverse external factors. The body usually finds it difficult to adapt; However, due to children's weak ability to express themselves, they cannot clearly understand each characteristic, resulting in low efficiency for doctors in diagnosing children's respiratory diseases. Summary of the invention

[0003] In view of the deficiencies of the prior art, the present invention provides an intelligent diagnosis assistance system and method for children's respiratory diseases, which has the advantages of assisting doctors in diagnosis and solves the above-mentioned technical problems.

[0004] To achieve the above-mentioned object, the present invention provides the following technical solutions: an intelligent diagnosis and assistance system for children's respiratory diseases, comprising a physiological data acquisition module, an environmental data acquisition module, a children's data analysis module and an auxiliary warning module; The physiological data acquisition module is used to collect children's physiological data, which includes children's movement data and children's clinical data. By measuring children's movement ability and clinical parameters and collecting their sleep parameters when they are sleeping, a physiological database is formed; The environmental data collection module collects parameters of children's activity points and places exposed to respiratory risks through questionnaires and GPS positioning, and forms a children's environmental database; The child data analysis module obtains the child's sleep coefficient based on the physiological database , Children's exercise coefficient and child symptom coefficient , and the sleep index of children , Children's exercise coefficient and child symptom coefficient Analyze and obtain children's physiological risk coefficient , in children's physiological risk factors When the physiological risk threshold is exceeded, the auxiliary warning module is called to execute the physiological risk warning instruction; The child data analysis module obtains the number of times the child is in a respiratory risk location based on the child environment database. and risk values ​​for respiratory risk sites , and based on the number of times children are exposed to respiratory risk and risk values ​​for respiratory risk sites Analyze and obtain the children's environmental risk factor ; The child data analysis module calculates the child's physiological risk coefficient when not executing the physiological risk warning instruction. and children's environmental risk factors Comprehensively obtain the comprehensive risk factor ; The auxiliary warning module is used to combine the comprehensive risk coefficient output by the child data analysis module , determine whether to output a warning instruction.

[0005] As a preferred technical solution of the present invention, the children's sleep coefficient The steps to obtain are as follows: Step A1: The duration of the child's mouth breathing collected by the home sleep monitoring device or manually entered during the child's sleep , duration of apnea and duration of snoring , and stored in the physiological database; Step A2: Based on the duration of the child's mouth breathing , duration of apnea and duration of snoring Calculate the children's sleep coefficient , the specific expression is as follows: ; in, Indicates the initial value of sleep quality, Indicates the total sleep time. Indicates the children's sleep coefficient.

[0006] As a preferred technical solution of the present invention, the children's motion coefficient The steps to obtain are as follows: Step B1: Collect the child's breathing rate after exercise , resting vital capacity and the time it takes for breathing rate to recover after exercise ; Step B2: Based on breathing rate , maximum vital capacity at rest and the time it takes for breathing rate to recover after exercise Calculating the motion coefficient for children , the specific expression is as follows: ; in, represents the child motion coefficient, It represents the average respiratory rate of children of the same age as the current child during exercise. Indicates the minimum value of resting vital capacity of children of the same age as the current child.

[0007] As a preferred technical solution of the present invention, the children's symptom coefficient The obtained expression is as follows: : in, represents the child symptom coefficient, , Indicates that the child currently has Symptoms, Indicates that the child currently does not have Symptoms, represents the sum of the total number of children's symptoms, Indicates the total number of symptoms.

[0008] As a preferred technical solution of the present invention, the child physiological risk factor The calculation expression is as follows: ; in, represents the child symptom coefficient, represents the child motion coefficient, represents the child's sleep coefficient, and the child data analysis module is in the child's physiological risk coefficient The specific steps for executing the physiological risk warning instruction when the physiological risk threshold is exceeded are: issuing a warning signal and reading the current child's mouth breathing duration , duration of apnea , snoring duration , any symptoms the child has, respiratory rate , maximum vital capacity at rest and the time it takes for breathing rate to recover after exercise for display.

[0009] As a preferred technical solution of the present invention, the number of times the child is in a respiratory risk location and risk values ​​for respiratory risk sites The steps to obtain it are as follows: Step C1: Get all the activity locations of the current child in a week and count the number of places where the child has been active. Risk value in locations The specific expression is as follows: ; in, Indicates The first risk, by The risk is compared with the preset risk situation. If they are consistent, the output If they are inconsistent, then output , Indicates that children The time at a location, Indicates The common The risks are summed up. The total number of risks is expressed as Risk value in locations When the set regional risk threshold is exceeded, the number of times the child is in a respiratory risk location plus one; Step C2: Calculate the risk value for respiratory risk sites , the specific expression is as follows: ; in, Indicates the first The risk value in each location, Indicates the total number of locations.

[0010] As a preferred technical solution of the present invention, the child environmental risk factor The specific expression of the calculation is as follows: ; in, Indicates the number of times children were exposed to respiratory risk. Indicates the risk value of respiratory risk locations.

[0011] As a preferred technical solution of the present invention, the child data analysis module converts the child's physiological risk coefficient and children's environmental risk factors Comprehensively obtain the comprehensive risk factor The specific expression is as follows: ; in, and They represent two weight coefficients whose sum is 1.

[0012] As a preferred technical solution of the present invention, the specific execution steps of the early warning instruction are: reading the comprehensive risk coefficient of the current child Similarity value The oldest patient with the same age is shown to the doctor. The calculation expression is as follows: ; in, Indicates The comprehensive risk factor of children in each patient.

[0013] The present invention also provides an intelligent diagnosis assistance method for children's respiratory diseases, which uses the above-mentioned intelligent diagnosis assistance system for children's respiratory diseases and includes the following steps: Step 1: Collect children's motor data and clinical data respectively, measure children's motor ability and clinical parameters, and collect their sleep parameters when they are sleeping to form a physiological database; Step 2: Collect the parameters of children's activity points and places exposed to respiratory risks through questionnaires and GPS positioning, and form a children's environment database; Step 3: Obtain children's sleep coefficient based on physiological database , Children's exercise coefficient and child symptom coefficient , and the sleep index of children , Children's exercise coefficient and child symptom coefficient Analyze and obtain children's physiological risk coefficient ; Step 4: Obtain the number of times children are in respiratory risk places based on the children's environment database and risk values ​​for respiratory risk sites , and based on the number of times children are exposed to respiratory risk and risk values ​​for respiratory risk sites Analyze and obtain the children's environmental risk factor ; Step 5: Children's physiological risk factors and children's environmental risk factors Comprehensively obtain the comprehensive risk factor , combined with the comprehensive risk factor output by the child data analysis module , determine whether to output a warning instruction.

[0014] Compared with the prior art, the present invention provides an intelligent diagnosis assistance system and method for children's respiratory diseases, which has the following beneficial effects: The present invention obtains a child physiological risk coefficient by analyzing a child sleep coefficient, a child movement coefficient and a child symptom coefficient, and simultaneously obtains all activity locations of the current child within a week and the risk values ​​in the locations, and comprehensively calculates the child environmental risk coefficient, and combines the child physiological risk coefficient and the child environmental risk coefficient to obtain a comprehensive risk coefficient, and determines whether the comprehensive risk coefficient exceeds a set threshold. If the threshold is exceeded, a prescription issued to a patient of the same age and having the largest similarity value with the current child's comprehensive risk coefficient is taken and displayed to the doctor, thereby accelerating the doctor's rapid judgment and diagnosis of the cause of the child patient's disease. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a schematic diagram of the system framework of the present invention; Figure 2 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0016] 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.

[0017] See also Figure 1 - Figure 2 , an intelligent diagnosis and assistance system for children's respiratory diseases, including a physiological data acquisition module, an environmental data acquisition module, a children's data analysis module and an auxiliary warning module; The physiological data collection module is used to collect children's physiological data, which includes children's movement data and children's clinical data. By measuring children's movement ability and clinical parameters, and collecting their sleep parameters when they are sleeping, a physiological database is formed; The environmental data collection module collects the parameters of children's activity points and places exposed to respiratory risks through questionnaires and GPS positioning, and forms a children's environmental database; The children's data analysis module obtains children's sleep coefficient based on the physiological database , Children's exercise coefficient and child symptom coefficient , and the sleep index of children , Children's exercise coefficient and child symptom coefficient Analyze and obtain children's physiological risk coefficient , in children's physiological risk factors When the physiological risk threshold is exceeded, the auxiliary warning module is called to execute the physiological risk warning instruction; Children's sleep index The steps to obtain are as follows: Step A1: The duration of the child's mouth breathing collected by the home sleep monitoring device or manually entered during the child's sleep , duration of apnea and duration of snoring , and stored in the physiological database; Table 1 below shows the specific data for one week transmitted by sleep monitoring equipment or manually entered: Table 1 date Duration of apnea (minutes) Snoring duration (minutes) Mouth breathing duration (minutes) on Monday 5 15 30 Tuesday 7 20 25 Wednesday 3 10 15 Thursday 10 25 40 Friday 2 5 20 Saturday 8 18 35 Saturday 8 17 22 Step A2: Based on the duration of the child's mouth breathing , duration of apnea and duration of snoring Calculate the children's sleep coefficient , the specific expression is as follows: ; in, Indicates the initial value of sleep quality, Indicates the total sleep time. Indicates the children's sleep coefficient.

[0018] Children's exercise coefficient The steps to obtain are as follows: Step B1: Collect the child's breathing rate after exercise , resting vital capacity and the time it takes for breathing rate to recover after exercise ; Step B2: Based on breathing rate , maximum vital capacity at rest and the time it takes for breathing rate to recover after exercise Calculating the motion coefficient for children , the specific expression is as follows: ; in, represents the child motion coefficient, It represents the average respiratory rate of children of the same age as the current child during exercise. Indicates the minimum value of resting vital capacity of children of the same age as the current child.

[0019] Child symptom index The obtained expression is as follows: ; in, represents the child symptom coefficient, , Indicates that the child currently has Symptoms, Indicates that the child currently does not have Symptoms, represents the sum of the total number of children's symptoms, Indicates the total number of symptoms.

[0020] Children's physiological risk factor The calculation expression is as follows: ; in, represents the child symptom coefficient, represents the child motion coefficient, Indicates the children's sleep coefficient, the children's data analysis module in the children's physiological risk coefficient The specific steps for executing the physiological risk warning instruction when the physiological risk threshold is exceeded are: issuing a warning signal and reading the current child's mouth breathing duration , duration of apnea , snoring duration , any symptoms the child has, respiratory rate , maximum vital capacity at rest and the time it takes for breathing rate to recover after exercise To make a display; The child data analysis module obtains the number of times children are in respiratory risk places based on the child environment database and risk values ​​for respiratory risk sites , and based on the number of times children are exposed to respiratory risk and risk values ​​for respiratory risk sites Analyze and obtain the children's environmental risk factor ; Number of times children were exposed to respiratory risk and risk values ​​for respiratory risk sites The steps to obtain it are as follows: Step C1: Get all the activity locations of the current child in a week and count the number of places where the child has been active. Risk value in locations The specific expression is as follows: ; in, Indicates The first risk, by The risk is compared with the preset risk situation. If they are consistent, the output If they are inconsistent, then output , Indicates that children The time at a location, represents the total number of risks, Indicates Appeared in locations The risks are summed up when the child has Risk value in locations When the set regional risk threshold is exceeded, the number of times the child is in a respiratory risk location Add one, as shown in Table 2 below, which is an example of risk value of the present invention: Table 2 Event Location Value at Risk Activity duration (hours) Risk Factors kindergarten 2 6 Indoor activities with good ventilation garden 2 2 Outdoor activities, natural ventilation, low pollen levels Playground 4 3 Outdoor activities, crowded, occasional smoking Relatives and friends 5 2 Family gathering, poor ventilation, people smoking shopping center 6 2 Indoor space, poor air circulation, people smoking Swimming pool 4 1 Indoors, high humidity, occasional smoking Step C2: Calculate the risk value for respiratory risk sites , the specific expression is as follows: ; in, Indicates the first The risk value in each location, Indicates the total number of locations; Children's environmental risk factor The specific expression of the calculation is as follows: ; in, Indicates the number of times children were exposed to respiratory risk. Indicates the risk value of respiratory risk sites; The child data analysis module will calculate the child's physiological risk coefficient when the physiological risk warning instruction is not executed. and children's environmental risk factors Comprehensively obtain the comprehensive risk factor , the child data analysis module converts the child's physiological risk coefficient and children's environmental risk factors Comprehensively obtain the comprehensive risk factor The specific expression is as follows: ; in, and Respectively represent two weight coefficients whose sum is 1; The auxiliary warning module is used to combine the comprehensive risk coefficient output by the child data analysis module , determine whether to output the warning instruction, the specific execution steps of the warning instruction are: read the comprehensive risk coefficient of the current child Similarity value The oldest patient with the same age is shown to the doctor. The calculation expression is as follows: ; in, Indicates The comprehensive risk factor of children in each patient.

[0021] The present invention also provides an intelligent diagnosis assistance method for children's respiratory diseases, which uses the above-mentioned intelligent diagnosis assistance system for children's respiratory diseases and includes the following steps: Step 1: Collect children's motor data and clinical data respectively, measure children's motor ability and clinical parameters, and collect their sleep parameters when they are sleeping to form a physiological database; Step 2: Collect the parameters of children's activity points and places exposed to respiratory risks through questionnaires and GPS positioning, and form a children's environment database; Step 3: Obtain children's sleep coefficient based on physiological database , Children's exercise coefficient and child symptom coefficient , and the sleep index of children , Children's exercise coefficient and child symptom coefficient Analyze and obtain children's physiological risk coefficient ; Step 4: Obtain the number of times children are in respiratory risk places based on the children's environment database and risk values ​​for respiratory risk sites , and based on the number of times children are exposed to respiratory risk and risk values ​​for respiratory risk sites Analyze and obtain the children's environmental risk factor ; Step 5: Children's physiological risk factors and children's environmental risk factors Comprehensively obtain the comprehensive risk factor , combined with the comprehensive risk factor output by the child data analysis module , determine whether to output a warning instruction.

[0022] 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. An intelligent diagnostic assistance system for children's respiratory diseases, characterized by: It includes physiological data collection module, environmental data collection module, child data analysis module and auxiliary warning module; The physiological data acquisition module is used to collect children's physiological data, which includes children's movement data and children's clinical data. By measuring children's movement ability and clinical parameters and collecting their sleep parameters when they are sleeping, a physiological database is formed; The environmental data collection module collects parameters of children's activity points and places exposed to respiratory risks through questionnaires and GPS positioning, and forms a children's environmental database; The child data analysis module obtains the child's sleep coefficient based on the physiological database , Children's exercise coefficient and child symptom coefficient , and the sleep index of children , Children's exercise coefficient and child symptom coefficient Analyze and obtain children's physiological risk coefficient , in children's physiological risk factors When the physiological risk threshold is exceeded, the auxiliary warning module is called to execute the physiological risk warning instruction; The child data analysis module obtains the number of times the child is in a respiratory risk location based on the child environment database. and risk values ​​for respiratory risk sites , and based on the number of times children are exposed to respiratory risk and risk values ​​for respiratory risk sites Analyze and obtain the children's environmental risk factor ; The child data analysis module calculates the child's physiological risk coefficient when not executing the physiological risk warning instruction. and children's environmental risk factors Comprehensively obtain the comprehensive risk factor ; The auxiliary warning module is used to combine the comprehensive risk coefficient output by the child data analysis module , determine whether to output a warning instruction.

2. The intelligent diagnosis assistance system for children's respiratory diseases according to claim 1 is characterized in that: The sleep factor of children The steps to obtain are as follows: Step A1: The duration of the child's mouth breathing collected by the home sleep monitoring device or manually entered during the child's sleep , duration of apnea and duration of snoring , and stored in the physiological database; Step A2: Based on the duration of the child's mouth breathing , duration of apnea and duration of snoring Calculate the children's sleep coefficient , the specific expression is as follows: ； in, Indicates the initial value of sleep quality, Indicates the total sleep time. Indicates the sleep coefficient of children.

3. The intelligent diagnosis assistance system for children's respiratory diseases according to claim 1 is characterized in that: The child movement coefficient The steps to obtain are as follows: Step B1: Collect the child's breathing rate after exercise , resting vital capacity and the time it takes for breathing rate to recover after exercise ; Step B2: Based on breathing rate , maximum vital capacity at rest and the time it takes for breathing rate to recover after exercise Calculating the motion coefficient for children , the specific expression is as follows: ； in, represents the child motion coefficient, It represents the average respiratory rate of children of the same age as the current child during exercise. Indicates the minimum value of resting vital capacity of children of the same age as the current child.

4. The intelligent diagnosis assistance system for children's respiratory diseases according to claim 1, characterized in that: The children's symptom index The obtained expression is as follows: ； in, represents the child symptom coefficient, , Indicates that the child currently has Symptoms, Indicates that the child currently does not have Symptoms, represents the sum of the total number of children's symptoms, Indicates the total number of symptoms.

5. The intelligent diagnosis assistance system for children's respiratory diseases according to claim 1 is characterized in that: The physiological risk factor of the child The calculation expression is as follows: ； in, represents the child symptom coefficient, represents the child motion coefficient, represents the child's sleep coefficient, and the child data analysis module is used in the child's physiological risk coefficient The specific steps for executing the physiological risk warning instruction when the physiological risk threshold is exceeded are: issuing a warning signal and reading the current child's mouth breathing duration , duration of apnea , snoring duration , any symptoms the child has, respiratory rate , maximum vital capacity at rest and the time it takes for breathing rate to recover after exercise for display.

6. The intelligent diagnosis assistance system for children's respiratory diseases according to claim 1, characterized in that: Number of times the child was exposed to respiratory risk and risk values ​​for respiratory risk sites The steps to obtain it are as follows: Step C1: Get all the activity locations of the current child in a week and count the number of places where the child has been active. Risk value in locations The specific expression is as follows: ； in, Indicates The first risk, by The risk is compared with the preset risk situation. If they are consistent, the output If they are inconsistent, then output , Indicates that children The time at a location, Indicates The common The risks are summed up. The total number of risks is expressed as Risk value in locations When the set regional risk threshold is exceeded, the number of times the child is in a respiratory risk location plus one; Step C2: Calculate the risk value for respiratory risk sites , the specific expression is as follows: ； in, Indicates the first The risk value in each location, Indicates the total number of locations.

7. The intelligent diagnosis assistance system for children's respiratory diseases according to claim 6, characterized in that: The environmental risk factor for children The specific expression of the calculation is as follows: ； in, Indicates the number of times children were exposed to respiratory risk. Indicates the risk value of respiratory risk locations.

8. The intelligent diagnosis assistance system for children's respiratory diseases according to claim 1 is characterized in that: The child data analysis module calculates the child's physiological risk coefficient and children's environmental risk factors Comprehensively obtain the comprehensive risk factor The specific expression is as follows: ； in, and They represent two weight coefficients whose sum is 1.

9. The intelligent diagnosis assistance system for children's respiratory diseases according to claim 6, characterized in that: The specific execution steps of the early warning instruction are: reading the comprehensive risk coefficient of the current child Similarity value The oldest patient with the same age is shown to the doctor. The calculation expression is as follows: ； in, Indicates The comprehensive risk factor of children in each patient.

10. An intelligent diagnosis assistance method for children's respiratory diseases, using the intelligent diagnosis assistance system for children's respiratory diseases according to any one of claims 1 to 9, characterized in that: The following steps are involved: Step 1: Collect children's motor data and clinical data respectively, measure children's motor ability and clinical parameters, and collect their sleep parameters when they are sleeping to form a physiological database; Step 2: Collect the parameters of children's activity points and places exposed to respiratory risks through questionnaires and GPS positioning, and form a children's environment database; Step 3: Obtain children's sleep coefficient based on physiological database , Children's exercise coefficient and child symptom coefficient , and the sleep index of children , Children's exercise coefficient and child symptom coefficient Analyze and obtain children's physiological risk coefficient ; Step 4: Obtain the number of times children are in respiratory risk places based on the children's environment database and risk values ​​for respiratory risk sites , and based on the number of times children are exposed to respiratory risk and risk values ​​for respiratory risk sites Analyze and obtain the children's environmental risk factor ; Step 5: Children's physiological risk factors and children's environmental risk factors Comprehensively obtain the comprehensive risk factor , combined with the comprehensive risk factor output by the child data analysis module , determine whether to output a warning instruction.

Images