CN114403806A - Dangerous disease early warning and informing system - Google Patents
Dangerous disease early warning and informing system Download PDFInfo
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7271—Specific aspects of physiological measurement analysis
- A61B5/7275—Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B10/00—Other methods or instruments for diagnosis, e.g. instruments for taking a cell sample, for biopsy, for vaccination diagnosis; Sex determination; Ovulation-period determination; Throat striking implements
- A61B10/0045—Devices for taking samples of body liquids
- A61B10/007—Devices for taking samples of body liquids for taking urine samples
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/0002—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network
- A61B5/0015—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network characterised by features of the telemetry system
- A61B5/0022—Monitoring a patient using a global network, e.g. telephone networks, internet
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/01—Measuring temperature of body parts ; Diagnostic temperature sensing, e.g. for malignant or inflamed tissue
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
- A61B5/0205—Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
- A61B5/02055—Simultaneously evaluating both cardiovascular condition and temperature
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/48—Other medical applications
- A61B5/4806—Sleep evaluation
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
- A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
- A61B5/6802—Sensor mounted on worn items
- A61B5/681—Wristwatch-type devices
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
- A61B5/7267—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/74—Details of notification to user or communication with user or patient ; user input means
- A61B5/746—Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/74—Details of notification to user or communication with user or patient ; user input means
- A61B5/7465—Arrangements for interactive communication between patient and care services, e.g. by using a telephone network
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D21/00—Measuring or testing not otherwise provided for
- G01D21/02—Measuring two or more variables by means not covered by a single other subclass
Abstract
The invention relates to the field of disease prevention systems, in particular to a dangerous disease early warning and informing system. The method is characterized in that a data collection end, an information display end, a wireless network and a cloud server are arranged to cooperate, a dangerous disease judgment model is established in advance, the dangerous grade of a disease factor is judged by collecting and analyzing daily human body sign parameter data, calculation and analysis are combined with a weighting algorithm, dangerous diseases are predicted, and relevant notifications are sent to a user. The acquired data has various types, wide sources and high analysis and prediction accuracy, so that a user can know the physical condition in time conveniently, and can obtain the suggestion of a doctor in time, and finally the aim of preventing or delaying the occurrence of dangerous diseases is fulfilled.
Description
Technical Field
The invention relates to the field of disease prevention systems, in particular to a dangerous disease early warning and informing system.
Background
The monitoring is a monitoring method for continuously and systematically collecting and analyzing data of occurrence frequency of specific disease clinical syndrome, and discovering abnormal aggregation of diseases on time and space distribution in time so as to carry out early exploration, early warning and quick response on the diseases. Generally, before the disease really occurs, professional monitoring at home is difficult. When the dangerous diseases occur, the patient usually misses the best treatment time when the patient is sent to the hospital. Therefore, a dangerous disease early warning and informing system is needed.
Disclosure of Invention
Aiming at the problems in the background technology, a dangerous disease early warning and informing system is provided.
The invention provides a dangerous disease early warning and informing system, which comprises a data collecting end, an information displaying end, a wireless network and a cloud server, wherein the data collecting end is connected with the information displaying end; the data collection end, the information display end and the cloud server are in communication connection through a wireless network.
The data collection end is used for collecting daily human body sign parameter data, classifying, converting and summarizing the data to obtain a data set corresponding to each sign parameter data, and the data set is transmitted to the cloud server; the data collection end comprises a parameter collection module, a data processing module and a communication module I; the physical sign parameter collecting module comprises a urine detection unit, a sleep quality detection unit, a basic physical sign detection unit and an environmental parameter detection unit.
A dangerous disease judgment model is arranged on the cloud server, corresponding score correlation is carried out on each disease factor in advance, the category of the disease factor is judged by analyzing the data sets collected every day, and the disease factors are grouped to obtain a disease factor group set of the day; inputting the disease factor group set into a dangerous disease judgment model, calculating and analyzing by combining a weighting algorithm according to the dangerous level of the disease factor, predicting the dangerous level of the disease, and sending a relevant notice to an information display end; the cloud server comprises a dangerous disease judgment module, a control module, a communication module II and a data storage module; the dangerous disease judgment module comprises a data analysis unit, a disease danger level judgment unit and a notification unit.
The information display end is used for displaying the relevant information of the physical health condition for 24 hours, providing online contact with doctors and carrying out health consultation, and comprises a body information display module, a historical information storage module, a disease early warning and informing module, a health consultation module and a communication module.
Preferably, the data collection end comprises data acquisition equipment; data acquisition equipment includes intelligent closestool, intelligent bracelet and intelligent air purifier.
Preferably, the urine detection unit is arranged on the intelligent closestool, and the detected physical sign parameters comprise bilirubin, urobilinogen, ketone bodies, ascorbic acid, glucose, protein (albumin), blood cells and pH.
Preferably, sleep quality detecting element and basic sign detecting element all set up on intelligent bracelet, and the sign parameter that detects includes that length of time, respiratory rate, rhythm of the heart, blood pressure and body temperature sleep.
Preferably, the environmental parameter detecting unit is arranged on the intelligent air purifier, and the detected environmental parameters comprise air humidity, temperature, contents of gaseous pollutants, solid particles, air natural bacteria and conditional pathogenic bacteria.
Preferably, the dangerous disease judgment model adopts a machine learning method, the missing data filling is carried out on the model through mean filling, median filling, linear regression and expectation maximization, the feature selection is carried out on the model through forward feature selection, L1 regularization and conservative mean feature selection (mu-sigma), and then the SVM is used for prediction.
Preferably, the risk disease judgment model is a convolutional neural network model.
Preferably, the system works as follows:
s1, establishing a dangerous disease judgment model through data completion, feature representation, feature selection and predictive modeling;
s2, establishing a learning set database in advance, and inputting the learning set database into a dangerous disease judgment model; the original features are transformed in a multilayer way through a convolutional neural network, and are mapped into a new space, so that the classification, judgment and prediction capabilities of the model are continuously improved;
s3, comprehensively and primarily collecting human body sign parameters of a prediction object in a questionnaire, instrument detection and doctor inquiry mode, recording the parameters as an original information set a, and summarizing and marking abnormal data;
s4, collecting daily human body sign parameter data, classifying, converting and summarizing the data to obtain the number corresponding to each sign parameter dataData set bi(i 1, 2, 3.. n, which is the sequential number of the collection dates), and transmitting the data set to a cloud server;
s5, mixing biInputting a dangerous disease judgment model, and performing corresponding score association on each disease factor in advance; by comparing b collected dailyiB, analyzing the disease factors with the a to judge the risk level of the disease factors, and calculating and analyzing a disease factor group set of the current day by combining a weighting algorithm to calculate a body health score c of the current day;
s6, predicting the risk level of the disease according to the score c, and sending a relevant notice to an information display end;
and S7, displaying the relevant information of the physical health condition for 24h by the information display terminal, sending a warning notice when abnormal data occur, and contacting a doctor on line by the user through the information display terminal to perform health consultation.
Compared with the prior art, the invention has the following beneficial technical effects:
the method is characterized in that a data collection end, an information display end, a wireless network and a cloud server are arranged to cooperate, a dangerous disease judgment model is established in advance, the dangerous grade of a disease factor is judged by collecting and analyzing daily human body sign parameter data, calculation and analysis are combined with a weighting algorithm, dangerous diseases are predicted, and relevant notifications are sent to a user. The acquired data has various types, wide sources and high analysis and prediction accuracy, so that a user can know the physical condition in time conveniently, and can obtain the suggestion of a doctor in time, and finally the aim of preventing or delaying the occurrence of dangerous diseases is fulfilled.
Drawings
Fig. 1 is a schematic system structure according to an embodiment of the present invention.
Detailed Description
Example one
As shown in fig. 1, the system for early warning and notifying a dangerous disease provided by the present invention includes a data collection end, an information display end, a wireless network and a cloud server; the data collection end, the information display end and the cloud server are in communication connection through a wireless network.
The data collection end is used for collecting daily human body sign parameter data, classifying, converting and summarizing the data to obtain a data set corresponding to each sign parameter data, and the data set is transmitted to the cloud server; the data collection end comprises a parameter collection module, a data processing module and a communication module I; the physical sign parameter collecting module comprises a urine detection unit, a sleep quality detection unit, a basic physical sign detection unit and an environmental parameter detection unit.
A dangerous disease judgment model is arranged on the cloud server, corresponding score correlation is carried out on each disease factor in advance, the category of the disease factor is judged by analyzing the data sets collected every day, and the disease factors are grouped to obtain a disease factor group set of the day; inputting the disease factor group set into a dangerous disease judgment model, calculating and analyzing by combining a weighting algorithm according to the dangerous level of the disease factor, predicting the dangerous level of the disease, and sending a relevant notice to an information display end; the cloud server comprises a dangerous disease judgment module, a control module, a communication module II and a data storage module; the dangerous disease judgment module comprises a data analysis unit, a disease danger level judgment unit and a notification unit.
The information display end is used for displaying the relevant information of the physical health condition for 24 hours, providing online contact with doctors and carrying out health consultation, and comprises a body information display module, a historical information storage module, a disease early warning and informing module, a health consultation module and a communication module.
The method is characterized in that a data collection end, an information display end, a wireless network and a cloud server are arranged to cooperate, a dangerous disease judgment model is established in advance, the dangerous grade of a disease factor is judged by collecting and analyzing daily human body sign parameter data, calculation and analysis are combined with a weighting algorithm, dangerous diseases are predicted, and relevant notifications are sent to a user. The acquired data has various types, wide sources and high analysis and prediction accuracy, so that a user can know the physical condition in time conveniently, and can obtain the suggestion of a doctor in time, and finally the aim of preventing or delaying the occurrence of dangerous diseases is fulfilled.
Example two
As shown in fig. 1, the system for early warning and notifying a dangerous disease provided by the present invention includes a data collection end, an information display end, a wireless network and a cloud server; the data collection end, the information display end and the cloud server are in communication connection through a wireless network.
The data collection end is used for collecting daily human body sign parameter data, classifying, converting and summarizing the data to obtain a data set corresponding to each sign parameter data, and the data set is transmitted to the cloud server; the data collection end comprises a parameter collection module, a data processing module and a communication module I; the physical sign parameter collecting module comprises a urine detection unit, a sleep quality detection unit, a basic physical sign detection unit and an environmental parameter detection unit.
Further, the data collection end comprises data acquisition equipment; data acquisition equipment includes intelligent closestool, intelligent bracelet and intelligent air purifier. The urine detection unit is arranged on the intelligent closestool, and the detected physical sign parameters comprise bilirubin, urobilinogen, ketone bodies, ascorbic acid, glucose, protein (albumin), blood cells and pH. When a user goes to the toilet for the first time in the morning, the urine detection unit of the intelligent closestool is activated, and the urine is detected through the detection end. Sleep quality detecting element and basic sign detecting element all set up on intelligent bracelet, and the sign parameter that detects includes that length of time, respiratory rate, rhythm of the heart, blood pressure and body temperature sleep. The user wears the intelligent bracelet all day. The bracelet collects each item parameter of health. The environmental parameter detecting unit is arranged on the intelligent air purifier, and the detected environmental parameters comprise air humidity, temperature, and the content of gaseous pollutants, solid particles, air natural bacteria and conditioned pathogens. The environment has certain influence on the health, so the body health condition needs to be analyzed in combination with the environmental data to ensure the accuracy of the data. And the influence of air quality problems on body health is also avoided.
A dangerous disease judgment model is arranged on the cloud server, corresponding score correlation is carried out on each disease factor in advance, the category of the disease factor is judged by analyzing the data sets collected every day, and the disease factors are grouped to obtain a disease factor group set of the day; inputting the disease factor group set into a dangerous disease judgment model, calculating and analyzing by combining a weighting algorithm according to the dangerous level of the disease factor, predicting the dangerous level of the disease, and sending a relevant notice to an information display end; the cloud server comprises a dangerous disease judgment module, a control module, a communication module II and a data storage module; the dangerous disease judgment module comprises a data analysis unit, a disease danger level judgment unit and a notification unit.
Further, the dangerous disease judgment model adopts a machine learning method, missing data filling is carried out on the model through mean filling, median filling, linear regression and expectation maximization, feature selection is carried out on the model through forward feature selection, L1 regularization and conservative mean feature selection (mu-sigma), and then prediction is carried out through the SVM.
Further, the dangerous disease judgment model is a convolutional neural network model.
The information display end is used for displaying the relevant information of the physical health condition for 24 hours, providing online contact with doctors and carrying out health consultation, and comprises a body information display module, a historical information storage module, a disease early warning and informing module, a health consultation module and a communication module.
EXAMPLE III
As shown in fig. 1, the system for early warning and notifying a dangerous disease provided by the present invention includes a data collection end, an information display end, a wireless network and a cloud server; the data collection end, the information display end and the cloud server are in communication connection through a wireless network.
The data collection end is used for collecting daily human body sign parameter data, classifying, converting and summarizing the data to obtain a data set corresponding to each sign parameter data, and the data set is transmitted to the cloud server; the data collection end comprises a parameter collection module, a data processing module and a communication module I; the physical sign parameter collecting module comprises a urine detection unit, a sleep quality detection unit, a basic physical sign detection unit and an environmental parameter detection unit.
Further, the data collection end comprises data acquisition equipment; data acquisition equipment includes intelligent closestool, intelligent bracelet and intelligent air purifier.
Further, the urine detection unit is arranged on the intelligent closestool, and the detected physical sign parameters comprise bilirubin, urobilinogen, ketone bodies, ascorbic acid, glucose, protein (albumin), blood cells and pH.
Further, sleep quality detecting element and basic sign detecting element all set up on intelligent bracelet, and the sign parameter that detects includes that length of time, respiratory rate, rhythm of the heart, blood pressure and body temperature sleep.
Further, environmental parameter detecting element sets up on intelligent air purifier, and the environmental parameter that detects includes air humidity, temperature and gaseous pollutant, solid-state particulate matter, the natural fungus of air, conditional pathogen's content.
A dangerous disease judgment model is arranged on the cloud server, corresponding score correlation is carried out on each disease factor in advance, the category of the disease factor is judged by analyzing the data sets collected every day, and the disease factors are grouped to obtain a disease factor group set of the day; inputting the disease factor group set into a dangerous disease judgment model, calculating and analyzing by combining a weighting algorithm according to the dangerous level of the disease factor, predicting the dangerous level of the disease, and sending a relevant notice to an information display end; the cloud server comprises a dangerous disease judgment module, a control module, a communication module II and a data storage module; the dangerous disease judgment module comprises a data analysis unit, a disease danger level judgment unit and a notification unit.
Further, the dangerous disease judgment model adopts a machine learning method, missing data filling is carried out on the model through mean filling, median filling, linear regression and expectation maximization, feature selection is carried out on the model through forward feature selection, L1 regularization and conservative mean feature selection (mu-sigma), and then prediction is carried out through the SVM.
Further, the dangerous disease judgment model is a convolutional neural network model.
The information display end is used for displaying the relevant information of the physical health condition for 24 hours, providing online contact with doctors and carrying out health consultation, and comprises a body information display module, a historical information storage module, a disease early warning and informing module, a health consultation module and a communication module.
The working method of the system is as follows:
s1, establishing a dangerous disease judgment model through data completion, feature representation, feature selection and predictive modeling;
s2, establishing a learning set database in advance, and inputting the learning set database into a dangerous disease judgment model; the original features are transformed in a multilayer way through a convolutional neural network, and are mapped into a new space, so that the classification, judgment and prediction capabilities of the model are continuously improved;
s3, comprehensively and primarily collecting human body sign parameters of a prediction object in a questionnaire, instrument detection and doctor inquiry mode, recording the parameters as an original information set a, and summarizing and marking abnormal data;
s4, collecting daily human body sign parameter data, classifying, converting and summarizing the data to obtain a data set b corresponding to each sign parameter datai(i 1, 2, 3.. n, which is the sequential number of the collection dates), and transmitting the data set to a cloud server;
s5, mixing biInputting a dangerous disease judgment model, and performing corresponding score association on each disease factor in advance; by comparing b collected dailyiB, analyzing the disease factors with the a to judge the risk level of the disease factors, and calculating and analyzing a disease factor group set of the current day by combining a weighting algorithm to calculate a body health score c of the current day;
s6, predicting the risk level of the disease according to the score c, and sending a relevant notice to an information display end;
and S7, displaying the relevant information of the physical health condition for 24h by the information display terminal, sending a warning notice when abnormal data occur, and contacting a doctor on line by the user through the information display terminal to perform health consultation.
The system in this embodiment performs missing data filling on the model by means of mean filling, median filling, linear regression, and expectation maximization using a machine learning method, performs feature selection on the model by means of forward feature selection, L1 regularization, and conservative mean feature selection (μ - σ), and predicts the missing data by using an SVM. The data type is comprehensive, and the data source is extensive, and intelligence, practicality are strong, and the accuracy is high.
The embodiments of the present invention have been described in detail with reference to the drawings, but the present invention is not limited thereto, and various changes can be made within the knowledge of those skilled in the art without departing from the gist of the present invention.
Claims (8)
1. A dangerous disease early warning and informing system is characterized in that the system comprises a data collecting end, an information displaying end, a wireless network and a cloud server; the data collection end, the information display end and the cloud server are in communication connection through a wireless network;
the data collection end is used for collecting daily human body sign parameter data, classifying, converting and summarizing the data to obtain a data set corresponding to each sign parameter data, and the data set is transmitted to the cloud server; the data collection end comprises a parameter collection module, a data processing module and a communication module I; the physical sign parameter collecting module comprises a urine detection unit, a sleep quality detection unit, a basic physical sign detection unit and an environmental parameter detection unit;
a dangerous disease judgment model is arranged on the cloud server, corresponding score correlation is carried out on each disease factor in advance, the category of the disease factor is judged by analyzing the data sets collected every day, and the disease factors are grouped to obtain a disease factor group set of the day; inputting the disease factor group set into a dangerous disease judgment model, calculating and analyzing by combining a weighting algorithm according to the dangerous level of the disease factor, predicting the dangerous level of the disease, and sending a relevant notice to an information display end; the cloud server comprises a dangerous disease judgment module, a control module, a communication module II and a data storage module; the dangerous disease judgment module comprises a data analysis unit, a disease danger level judgment unit and a notification unit;
the information display end is used for displaying the relevant information of the physical health condition for 24 hours, providing online contact with doctors and carrying out health consultation, and comprises a body information display module, a historical information storage module, a disease early warning and informing module, a health consultation module and a communication module.
2. The system of claim 1, wherein the data collection end comprises a data collection device; data acquisition equipment includes intelligent closestool, intelligent bracelet and intelligent air purifier.
3. The system of claim 2, wherein the urine detection unit is disposed on the intelligent toilet, and the detected parameters include bilirubin, urobilinogen, ketone bodies, ascorbic acid, glucose, protein (albumin), blood cells and pH.
4. The system of claim 2, wherein the sleep quality detection unit and the basic sign detection unit are both disposed on the smart band, and the detected sign parameters include sleep duration, respiratory rate, heart rate, blood pressure, and body temperature.
5. The system of claim 2, wherein the environmental parameter detection unit is disposed on the intelligent air purifier, and the detected environmental parameters include air humidity, temperature, and contents of gaseous pollutants, solid particles, natural air bacteria, and conditional pathogenic bacteria.
6. The dangerous disease early warning and informing system according to claim 1, wherein the dangerous disease judgment model adopts a machine learning method, missing data filling is performed on the model through mean filling, median filling, linear regression and expectation maximization, feature selection is performed on the model through forward feature selection, L1 regularization and conservative mean feature selection (μ - σ), and then prediction is performed by using SVM.
7. The system of claim 6, wherein the risk assessment model is a convolutional neural network model.
8. The system for early warning and notification of dangerous diseases according to any one of claims 1 to 7, wherein the working method is as follows:
s1, establishing a dangerous disease judgment model through data completion, feature representation, feature selection and predictive modeling;
s2, establishing a learning set database in advance, and inputting the learning set database into a dangerous disease judgment model; the original features are transformed in a multilayer way through a convolutional neural network, and are mapped into a new space, so that the classification, judgment and prediction capabilities of the model are continuously improved;
s3, comprehensively and primarily collecting human body sign parameters of a prediction object in a questionnaire, instrument detection and doctor inquiry mode, recording the parameters as an original information set a, and summarizing and marking abnormal data;
s4, collecting daily human body sign parameter data, classifying, converting and summarizing the data to obtain a data set b corresponding to each sign parameter datai(i 1, 2, 3.. n, which is the sequential number of the collection dates), and transmitting the data set to a cloud server;
s5, mixing biInputting a dangerous disease judgment model, and performing corresponding score association on each disease factor in advance; by comparing b collected dailyiB, analyzing the disease factors with the a to judge the risk level of the disease factors, and calculating and analyzing a disease factor group set of the current day by combining a weighting algorithm to calculate a body health score c of the current day;
s6, predicting the risk level of the disease according to the score c, and sending a relevant notice to an information display end;
and S7, displaying the relevant information of the physical health condition for 24h by the information display terminal, sending a warning notice when abnormal data occur, and contacting a doctor on line by the user through the information display terminal to perform health consultation.
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| CN114582511A (en) * | 2022-05-07 | 2022-06-03 | 中国人民解放军总医院第八医学中心 | Bronchiectasis acute exacerbation early warning method, device, equipment and medium |
| CN116598006A (en) * | 2023-07-18 | 2023-08-15 | 中国医学科学院北京协和医院 | Sepsis early warning device and application system |
| CN116598006B (en) * | 2023-07-18 | 2023-10-17 | 中国医学科学院北京协和医院 | Sepsis early warning device and application system |
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