CN114052670A - Health risk assessment method and disease early warning system - Google Patents
Health risk assessment method and disease early warning system Download PDFInfo
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- CN114052670A CN114052670A CN202111361944.8A CN202111361944A CN114052670A CN 114052670 A CN114052670 A CN 114052670A CN 202111361944 A CN202111361944 A CN 202111361944A CN 114052670 A CN114052670 A CN 114052670A
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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
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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
- A61B5/02—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
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
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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
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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
Abstract
The application discloses a health risk assessment method and a disease early warning system, relates to the technical field of health risk assessment, and solves the technical problems that the existing health assessment scheme is complex in operation, small in application range and inaccurate in assessment result; the intelligent terminal comprises a central processing unit, an edge processor, a data acquisition module, a data storage module and an intelligent terminal; according to the method, the environmental data, the body characterization data and the user physiological data are combined, and the well-established mapping relation is combined to evaluate the health state of the user; compared with the prior art, the health assessment scheme with wider application range is provided, and the efficiency and the accuracy of health assessment are improved; the method and the device are not limited to personal health assessment, the target area is established through the actual data, and the target area is assessed in a regional mode, so that the accuracy of the health assessment can be guaranteed, a targeted regional health report can be formed, and a data base is laid for further regional health assessment.
Description
Technical Field
The application belongs to the technical field of health risk assessment, and particularly relates to a health risk assessment method and a disease early warning system.
Background
With the improvement of living standard, the life style of people is changed greatly, and the health problems caused by poor life styles such as obesity, insomnia, lack of exercise and the like are more and more; therefore, how to know the health status in time becomes an urgent problem to be solved.
Most of the existing schemes carry out health assessment through blood or certain cell state in human body; such as health assessment by bioenergy health coefficient sequences and microribonucleic acids. The bioenergy health coefficient sequence determines the reference bioenergy state of the patient by measuring the mitochondrial respiration function level of peripheral blood cells; MicroRNAs are non-coding RNAs, and abnormal microRNAs are closely related to many diseases and can be used as the basis for the early diagnosis of diseases.
However, the process of health assessment in the existing scheme is complex, and only specific diseases or a few diseases can be predicted and assessed, so that the application range and the applicable population are small; therefore, a health risk assessment system with a wide application range and scientific and accurate assessment results is needed.
Disclosure of Invention
The application provides a health risk assessment method and a disease early warning system, which are used for solving the technical problems of complex operation, small application range and inaccurate assessment result of the existing health assessment scheme.
The purpose of the application can be realized by the following technical scheme: a health risk assessment method, comprising:
acquiring body characterization data and environmental data in real time, and marking the data as initial data after data integration and data preprocessing; wherein the data pre-processing comprises inserting user physiological data;
analyzing the initial data through a mapping relation to obtain a health coefficient sequence, and early warning according to the health coefficient sequence; the health coefficient sequence comprises a health label and a disease probability, and the mapping relation comprises the corresponding relation between the initial data and the health label and the disease probability;
and performing regional evaluation on the health coefficient sequence of the user in the target region.
Preferably, the physical characterization data includes heart rate, body temperature, and electrodermal; the user physiological data includes gender and age.
Preferably, the environmental data includes ambient temperature, air pressure, ambient light intensity and ambient humidity.
Preferably, the data integration is to connect the body characteristic data and the environment data into a data sequence; wherein, in the data sequence, the body characteristic data is prior and the environment data is subsequent, or the environment data is prior and the body characteristic data is subsequent.
Preferably, the data preprocessing is to insert the user physiological data into the head end of the data sequence to form a new data sequence, i.e. the initial data.
Preferably, the mapping relationship is established by a health assessment model, and the establishment of the health assessment model includes:
acquiring standard training data, manually marking the standard training data to acquire a standard database; the content in the standard training data is consistent with the content attribute of the initial data, and the standard training data in the standard database corresponds to the artificially labeled health coefficient sequences one by one;
selecting N pieces of data from the standard database and integrating the N pieces of data into a training data set; wherein N is a constant and is not less than 1000;
constructing an artificial intelligence model; wherein the artificial intelligence model comprises a deep convolutional neural network and an RBF neural network;
and training the artificial intelligence model through the training data set, and marking the trained artificial intelligence model as a health assessment model.
Preferably, the values of the health label include 0, 1 and 2; when the health label is 0, the health state corresponding to the initial data or the standard training data is healthy, when the health label is 1, the health state corresponding to the initial data or the standard training data is sub-healthy, and when the health label is 2, the health state corresponding to the initial data or the standard training data is abnormal.
Preferably, the target area is generated by modeling according to a design drawing or vector data.
Preferably, the regional assessment according to the target region includes:
dividing the target area into a plurality of sub-areas;
acquiring a health coefficient sequence of a user in each sub-area, and forming a regional health report according to the health coefficient sequence; wherein the regional health report comprises the proportion of users with abnormal health states and the environmental data.
A disease early warning system comprises a central processing unit, an edge processor, a data acquisition module, a data storage module and an intelligent terminal;
the data acquisition module acquires initial data and respectively sends the initial data to the corresponding edge processor and the data storage module;
the edge processor combines the mapping relation with the initial data for analysis to obtain a health coefficient sequence;
the central processing unit is used for acquiring the mapping relation and distributing the mapping relation to the edge processor, and meanwhile, the mapping relation is updated periodically;
the data storage module is used for storing data.
Preferably, the central processor is in communication and/or electrical connection with the data storage module and the plurality of edge processors; each edge processor is in communication and/or electrical connection with at least one of the data acquisition modules; the intelligent terminal is respectively in communication and/or electrical connection with the central processing unit, the edge processor and the data acquisition module; the intelligent terminal comprises an intelligent bracelet, an intelligent mobile phone and a notebook computer.
Compared with the prior art, the beneficial effects of this application are:
1. according to the method, the environmental data, the body characterization data and the user physiological data are combined, and the well-established mapping relation is combined to evaluate the health state of the user; compared with the prior art, the health assessment scheme with a wider application range is provided, and the efficiency and the accuracy of health assessment are improved.
2. The method and the device are not limited to personal health assessment, the target area is established through the actual data, and the target area is assessed in a regional mode, so that the accuracy of the health assessment can be guaranteed, a targeted regional health report can be formed, and a data base is laid for further regional health assessment.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic diagram of the process steps of the present application;
fig. 2 is a schematic diagram of the system of the present application.
Detailed Description
The technical solutions of the present application will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The terminology used herein is for the purpose of describing embodiments and is not intended to be limiting and/or limiting of the present disclosure; it should be noted that the singular forms "a," "an," and "the" include the plural forms as well, unless the context clearly indicates otherwise; also, although the terms first, second, etc. may be used herein to describe various elements, the elements are not limited by these terms, which are only used to distinguish one element from another.
Referring to fig. 1, the present application provides a health risk assessment method, including:
acquiring body characterization data and environmental data in real time, and marking the data as initial data after data integration and data preprocessing;
analyzing the initial data through the mapping relation to obtain a health coefficient sequence, and early warning according to the health coefficient sequence;
and performing regional evaluation on the health coefficient sequence of the user in the target region.
The environmental data comprises environmental data which can affect the health state of the user, such as environmental temperature, air pressure, environmental light intensity, environmental humidity, harmful gas concentration and the like; it can be understood that it is unreasonable to directly evaluate the health status through the body characterization data by neglecting the influence of the environmental data on the user, and the health status of the user can be evaluated by combining the environmental data and the body characterization data, so that the accuracy of health evaluation can be ensured, and the misjudgment caused by the data can be avoided.
The user physiological data comprises data such as gender, age and the like which can represent the actual physiological state of the user; it can be known that many health problems are closely related to age or gender, and the physiological data of the user is taken into the basic data of the health assessment, so that the health assessment result is more reasonable and accurate.
Body characterization data can directly or indirectly express user health status's health data including rhythm of the heart, body temperature, skin electricity etc. and body characterization data gathers through equipment such as intelligent bracelet, portable sensor, can enough guarantee the rationality of data, can improve the range of application of this application again. It is to be understood that the body characterization data does not only include the data mentioned in the present application.
The method and the device have certain rationality by combining environmental data, user physiological data, body characterization data and the like as basic data for health assessment; for example, the heart rate abnormality of the user can be used as the basis for judging diseases such as palpitation and heart stuffiness; the application range of the application can be improved by early warning common diseases through the most common and easily acquired data.
After body characterization data and environmental data are acquired, data integration is needed; the data integration specifically comprises splicing the body representation data and the environment data into a data sequence; in another preferred implementation, the body characteristic data may be preceded and the environment data may be succeeded to form a data sequence.
After data integration, data preprocessing is needed; the data preprocessing specifically inserts the user physiological data into the head end of the data sequence to form initial data.
According to the health risk assessment method, the initial data are analyzed through the mapping relation, the health coefficient sequence is obtained, and early warning is carried out according to the health coefficient sequence.
The health label in the health coefficient sequence represents the health state expressed according to the current body data of the user, when the health label is 0, a health signal is sent to the intelligent terminal of the user, and meanwhile, the disease probability is displayed on the intelligent terminal; when the health label is 1 or 2, the sub-health signal or the health abnormal signal is sent to the intelligent terminal, and meanwhile, the disease probability is displayed on the intelligent terminal.
The disease probability in the health coefficient sequence of the application indicates the probability of predicting that a user suffers from a certain disease according to the current body data of the user, and even if the body health state is normal, the disease risk is possible to exist, so that the health label indicates the current state and the disease risk indicates the future state; the relationship between the health label and the disease state may be independently associable.
When the disease probability is greater than the probability threshold, sending a disease probability early warning signal to the intelligent terminal, wherein the value range of the probability threshold is [0, 1 ]; and if the probability threshold is 0.2 and the morbidity probability is 0.25, sending a morbidity probability early warning signal and a corresponding disease to the intelligent terminal, such as a palpitation probability of 0.25.
The health coefficient sequence in the application comprises a health label and a disease probability; the mapping relation refers to the corresponding relation between the initial data and the health label and the disease probability.
Values of the health label include 0, 1 and 2; when the health label is 0, the health state corresponding to the initial data or the standard training data is healthy, when the health label is 1, the health state corresponding to the initial data or the standard training data is sub-healthy, and when the health label is 2, the health state corresponding to the initial data or the standard training data is abnormal.
In this embodiment, the mapping relationship is specifically a health assessment model based on an artificial intelligence model, and in some other preferred implementations, the mapping relationship may also be a mathematical model or a lookup table.
In the present application, the construction of the health assessment model includes:
acquiring standard training data, manually marking the standard training data to acquire a standard database;
selecting N pieces of data from a standard database and integrating the N pieces of data into a training data set;
constructing an artificial intelligence model;
and training the artificial intelligence model through the training data set, and marking the trained artificial intelligence model as a health assessment model.
The content in the standard training data is consistent with the content attribute of the initial data, namely the content contained in the standard training data is the same as that contained in the initial data, and the difference is only the size of the data; and aiming at each standard training data, a corresponding health coefficient sequence is configured through manual marking.
The training data set comprises N pieces of standard training data and corresponding health coefficient sequences, and the training data set can meet the training requirements of the artificial intelligence model; in this embodiment, N is not less than 1000.
The artificial intelligence model comprises a deep convolution neural network, an RBF neural network and other models with strong nonlinear fitting capacity; after the training data sets are screened out, the corresponding relation between the data in the training data sets is found out through the artificial intelligence model, the operation is simple, and the accuracy and the obtaining efficiency of the mapping relation can be guaranteed.
In the embodiment, firstly, an artificial intelligence model is constructed, such as an input layer, a hidden layer and an output layer of a deep convolutional neural network and an RBF neural network;
and (3) carrying out the following steps of (1) carrying out N standard training data in the training data set and corresponding health coefficient sequences according to the sequence of (2): 1: 1, dividing the ratio into a training set, a test set and a check set;
taking standard training data as input data of the artificial intelligence model, taking a corresponding health coefficient sequence as output data of the artificial intelligence model, completing training on the artificial intelligence model according to a training set, a test set and a check set in sequence, and marking the trained artificial intelligence model as a health evaluation model when a training result meets requirements; it should be noted that the basic construction and training modes of the deep convolutional neural network and the RBF neural network belong to the prior art, and are not described herein again.
It is to be noted that the mapping relationship (health assessment model) obtained by the present application is not a uniform one, and it is necessary to continuously supplement and update the standard training data and periodically obtain the mapping relationship to ensure the reasonable accuracy of the mapping relationship.
According to the health risk assessment method, the health coefficient sequence of the user in the target area is assessed in a partitioned mode.
The target area is generated by modeling according to a design drawing or vector data and can be understood as a two-dimensional or three-dimensional image established according to actual data; when small-range areas such as a factory area need to be monitored, establishing a target area through a design drawing; when a certain administrative region needs to be monitored, a target region is generated through vector data of the administrative region.
Taking a target area determined by a county-level administrative area as an example, how to perform regional evaluation according to the target area is described in detail, and the method comprises the following steps:
dividing a target area into a plurality of ballast administrative areas; the area of the ballast administrative region is smaller than that of the target region, and the sum of the areas of all the ballast administrative regions is equal to that of the target region;
and acquiring a health coefficient sequence of the user in each sub-region, and generating a region health report according to the health coefficient sequence statistics.
In this embodiment, the regional health report includes the proportion of users whose health states are abnormal and the environmental data of the users, and can determine the problem of the environment where the users are located according to the regional health report and make a relevant environmental remediation task; periodic health services may also be provided to users in the area based on the area health reports.
Referring to fig. 2, the present application provides a disease early warning system, which includes a central processing unit, an edge processor, a data acquisition module, a data storage module, and an intelligent terminal;
the data acquisition module acquires body representation data of a user through the intelligent terminal and respectively sends the body representation data to the corresponding edge processor and the data storage module;
the edge processor analyzes the body characterization data of the user through a health assessment model to obtain a health coefficient sequence; each subarea is at least provided with an edge processor for data processing, so that the data processing efficiency is ensured;
the central processor is used for training the health evaluation model, distributing the health evaluation model to the edge processor and updating the health evaluation model periodically;
the data storage module is used for storing data.
The central processor is in communication and/or electrical connection with the data storage module and the plurality of edge processors; each edge processor is at least in communication and/or electrical connection with one data acquisition module; the intelligent terminal is respectively communicated and/or electrically connected with the central processing unit, the edge processor and the data acquisition module; the data acquisition module is connected with the data storage module; the intelligent terminal comprises an intelligent bracelet, an intelligent mobile phone and a notebook computer.
The working principle of the application is as follows:
the method comprises the steps of acquiring body representation data and environment data in real time, connecting the body representation data and the environment data into a data sequence, and simultaneously inserting user physiological data into the head end of the data sequence to form a new data sequence and marking the new data sequence as initial data.
And acquiring standard training data, manually marking to acquire a standard database, selecting N pieces of data from the standard database, integrating the N pieces of data into a training data set, and acquiring a mapping relation through the training data set.
Analyzing the initial data through the mapping relation to obtain a health coefficient sequence, carrying out early warning according to the health coefficient sequence, and simultaneously carrying out regional evaluation on the health coefficient sequence of the users in the target region.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing is merely exemplary and illustrative of the structure of the application and various modifications or additions may be made to the described embodiments by persons skilled in the art or may be substituted in a similar manner without departing from the structure or exceeding the scope of the claims as defined in the appended claims.
Claims (10)
1. A health risk assessment method, comprising:
acquiring body characterization data and environmental data in real time, and marking the data as initial data after data integration and data preprocessing; wherein the data pre-processing comprises inserting user physiological data;
analyzing the initial data through a mapping relation to obtain a health coefficient sequence, and early warning according to the health coefficient sequence; the health coefficient sequence comprises a health label and a disease probability, and the mapping relation comprises the corresponding relation between the initial data and the health label and the disease probability;
and performing regional evaluation on the health coefficient sequence of the user in the target region.
2. The health risk assessment method of claim 1, wherein said physical characteristic data comprises heart rate, body temperature and skin current; the user physiological data comprises gender and age; the environmental data includes ambient temperature, air pressure, ambient light intensity, and ambient humidity.
3. The health risk assessment method according to claim 1, wherein said data integration is linking the physical representation data and the environmental data into a data sequence; wherein, in the data sequence, the body characteristic data is prior and the environment data is subsequent, or the environment data is prior and the body characteristic data is subsequent.
4. The health risk assessment method according to claim 1, wherein the data preprocessing is to insert the physiological data of the user into the head of the data sequence to form a new data sequence.
5. The health risk assessment method according to claim 1, wherein the mapping relationship is established by a health assessment model, and the construction of the health assessment model comprises:
acquiring standard training data, manually marking the standard training data to acquire a standard database; the content in the standard training data is consistent with the content attribute of the initial data, and the standard training data in the standard database corresponds to the artificially labeled health coefficient sequences one by one;
selecting N pieces of data from the standard database and integrating the N pieces of data into a training data set; wherein N is a constant and is not less than 1000;
constructing an artificial intelligence model;
and training the artificial intelligence model through the training data set, and marking the trained artificial intelligence model as a health assessment model.
6. The health risk assessment method according to claim 1, wherein the target area is generated by modeling according to a design drawing or vector data.
7. The health risk assessment method according to claim 1, wherein performing regional assessment according to the target region comprises:
dividing the target area into a plurality of sub-areas;
acquiring a health coefficient sequence of a user in each sub-area, and forming a regional health report according to the health coefficient sequence; wherein the regional health report comprises the proportion of users with abnormal health states and the environmental data.
8. The health risk assessment method according to claim 7, wherein the shape of the plurality of sub-regions is circular or rectangular, and the total area of the plurality of sub-regions is equal to or larger than the target region.
9. The health risk assessment method of claim 5, wherein said artificial intelligence model comprises at least one of a deep convolutional neural network, an RBF neural network and a support vector machine model.
10. The disease early warning system for executing the health risk assessment method according to any one of claims 1 to 9, comprising a central processor, an edge processor, a data acquisition module, a data storage module and an intelligent terminal;
the data acquisition module acquires initial data and respectively sends the initial data to the corresponding edge processor and the data storage module;
the edge processor combines the mapping relation with the initial data for analysis to obtain a health coefficient sequence;
the central processing unit is used for acquiring the mapping relation and distributing the mapping relation to the edge processor, and meanwhile, the mapping relation is updated periodically;
the data storage module is used for storing data.
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