CN113555120A - Biological state analysis method and device, storage medium, and electronic device - Google Patents
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Abstract
The disclosure belongs to the technical field of data processing, and relates to a biological state analysis method and device, a storage medium and electronic equipment. The method comprises the following steps: determining a predetermined state probability of the living being based on the predetermined state living being number and the average living being number corresponding to the predetermined age; acquiring a feature vector of a living being, and obtaining a risk parameter of the living being according to the feature vector based on a state risk model of the living being; correcting the predetermined state probability by using the risk parameters to obtain a target state probability; and obtaining individual state information of the living being based on at least the target state probability. The method provides a data base for individual state information estimation aiming at the individuation of the living beings, provides possibility for calculating the individual state information, has low complexity and understanding difficulty of state index calculation, and enriches the application scene of calculating the individual state information.
Description
Technical Field
The present disclosure relates to the field of data processing technologies, and in particular, to a biological state analysis method, a biological state analysis device, a computer-readable storage medium, and an electronic device.
Background
For any living being, the analysis of the biological state and the estimation of the health life expectancy are of great significance. For example, the traditional health life expectancy measurement and calculation are usually implemented by using a population life expectancy calculation method. The population life expectancy calculation method needs to track and investigate a group of people born at the same time, and respectively records the number of deaths of the group of people at each age until the life of the last person is over. Then, the average life span of the population is calculated according to the number of people living to each age group, so that the average life span of the population is used to assume the average life span of a generation of people, namely the average expected life span.
However, the population life expectancy calculation method can only obtain the average life as a group index, and does not include individual characteristics, nor can personalized individual status information, such as life expectancy assessment, be made for an individual.
In view of the above, there is a need in the art to develop a new method and apparatus for analyzing biological status.
It is to be noted that the information disclosed in the above background section is only for enhancement of understanding of the background of the present disclosure, and thus may include information that does not constitute prior art known to those of ordinary skill in the art.
Disclosure of Invention
An object of the present disclosure is to provide a biological state analysis method, a biological state analysis device, a computer-readable storage medium, and an electronic apparatus, thereby overcoming, at least to some extent, a technical problem that individual state information cannot be estimated due to limitations of related art.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows, or in part will be obvious from the description, or may be learned by practice of the disclosure.
According to an aspect of the present disclosure, there is provided a biological status analysis method, the organism having a predetermined age, the method comprising:
determining a predetermined state probability of the living being based on a predetermined state living being number and an average living being number corresponding to the predetermined age;
acquiring a feature vector of the living being, and obtaining a risk parameter of the living being according to the feature vector based on a state risk model of the living being;
correcting the preset state probability by using the risk parameters to obtain a target state probability;
and obtaining the individual state information of the living beings at least based on the target state probability.
In one exemplary embodiment of the present disclosure,
the obtaining of the target state probability by correcting the predetermined state probability by using the risk parameter includes:
carrying out correction calculation on the risk parameters and the predetermined state probability to obtain a correction calculation result, and acquiring a correction threshold corresponding to the correction calculation result;
comparing the correction calculation to the correction threshold to determine a target state probability.
In an exemplary embodiment of the present disclosure, the determining the target state probability includes:
when the correction calculation result is less than or equal to the correction threshold value, determining that the correction calculation result is a target state probability;
and when the correction calculation result is larger than the correction threshold value, determining that the correction threshold value is the target state probability.
In one exemplary embodiment of the present disclosure,
after said determining a predetermined state probability of said living being based on a predetermined state living being number and an average living being number corresponding to said predetermined age, said method further comprises:
based on the predetermined state probability, obtaining average state information of the living beings, and carrying out state profit and loss calculation on the individual state information and the average state information to obtain potential profit and loss information;
comparing the potential profit and loss information with profit and loss thresholds corresponding to the potential profit and loss information to determine a feature influence result of the feature vector on the individual state information.
In one exemplary embodiment of the present disclosure,
the determining the feature influence result of the feature vector on the individual state information includes:
determining that the feature vector has a negative feature impact result on the individual state information when the potential profit-and-loss information is greater than the profit-and-loss threshold;
determining that the feature vector has a positive feature impact result on the individual state information when the potential benefit information is less than or equal to the benefit threshold.
In one exemplary embodiment of the present disclosure,
the determining the predetermined state probability of the living being based on the predetermined state living being number and the average living being number corresponding to the predetermined age comprises:
determining a rough state probability according to the preset state biological quantity and the average biological quantity corresponding to the preset age;
determining a predetermined state probability of the living being based on the coarse state probability.
In one exemplary embodiment of the present disclosure,
the obtaining of the risk parameter of the living being according to the feature vector based on the state risk model of the living being includes:
constructing a state risk model of the organism by using a survival analysis algorithm;
and performing state risk calculation on the feature vectors by using the state risk model to obtain the risk parameters of the living beings.
In an exemplary embodiment of the present disclosure, the feature vector includes a physiological feature and a behavioral feature.
In an exemplary embodiment of the present disclosure, the predetermined state is a death state.
According to an aspect of the present disclosure, there is provided a biological status analysis device, the organism having a predetermined age, the device including:
a probability calculation module configured to determine a predetermined state probability of the living being based on a predetermined state living being number corresponding to the predetermined age and an average living being number;
a risk calculation module configured to obtain a feature vector of the living being and obtain a risk parameter of the living being according to the feature vector based on a state risk model of the living being;
the probability correction module is configured to correct the predetermined state probability by using the risk parameter to obtain a target state probability;
a state determination module configured to derive individual state information of the living being based at least on the target state probability.
According to an aspect of the present disclosure, there is provided an electronic device including: a processor and a memory; wherein the memory has stored thereon computer readable instructions which, when executed by the processor, implement the biological state analysis method of any of the above-described exemplary embodiments.
According to an aspect of the present disclosure, there is provided a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the biological status analysis method in any of the above-described exemplary embodiments.
As can be seen from the above technical solutions, the biological state analysis method, the biological state analysis apparatus, the computer storage medium and the electronic device in the exemplary embodiments of the present disclosure have at least the following advantages and positive effects:
in the method and the device provided by the exemplary embodiment of the disclosure, based on the state risk model, the corresponding risk parameter is obtained according to the feature vector of the living being, the individual feature of the living being can be added in the state information calculation process of the living being, and a data basis is provided for individual state information estimation aiming at the living being. Furthermore, the corrected target state probability is used for calculating the individual state information, the realization possibility is provided for calculating the individual state information, the method is a crucial progress in the state information calculation process, the complexity and the understanding difficulty of state index calculation are low, and the application scene of calculating the individual state information is enriched.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure. It is to be understood that the drawings in the following description are merely exemplary of the disclosure, and that other drawings may be derived from those drawings by one of ordinary skill in the art without the exercise of inventive faculty.
FIG. 1 schematically illustrates a flow diagram of a biological state analysis method in an exemplary embodiment of the disclosure;
FIG. 2 schematically illustrates a flow chart of a method of determining a probability of a predetermined state in an exemplary embodiment of the disclosure;
FIG. 3 schematically illustrates a flow chart of a method of deriving a risk parameter in an exemplary embodiment of the disclosure;
FIG. 4 schematically illustrates a flow chart of a method of correction processing in an exemplary embodiment of the disclosure;
FIG. 5 schematically illustrates a flow chart of a method of determining a target state probability in an exemplary embodiment of the disclosure;
FIG. 6 schematically illustrates a flow chart of a method of determining a feature impact result in an exemplary embodiment of the disclosure;
FIG. 7 schematically illustrates a flow chart of a method of further determining a feature impact result in an exemplary embodiment of the disclosure;
fig. 8 schematically shows a structural view of a biological state analysis device in an exemplary embodiment of the present disclosure;
fig. 9 schematically illustrates an electronic device for implementing a biological status analysis method in an exemplary embodiment of the present disclosure;
fig. 10 schematically illustrates a computer-readable storage medium for implementing a biological status analysis method in an exemplary embodiment of the present disclosure.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the subject matter of the present disclosure can be practiced without one or more of the specific details, or with other methods, components, devices, steps, and the like. In other instances, well-known technical solutions have not been shown or described in detail to avoid obscuring aspects of the present disclosure.
The terms "a," "an," "the," and "said" are used in this specification to denote the presence of one or more elements/components/parts/etc.; the terms "comprising" and "having" are intended to be inclusive and mean that there may be additional elements/components/etc. other than the listed elements/components/etc.; the terms "first" and "second", etc. are used merely as labels, and are not limiting on the number of their objects.
Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repetitive description will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities.
In view of the problems in the related art, the present disclosure proposes a biological state analysis method in which an organism has a predetermined age. Fig. 1 shows a flow chart of a biological state analysis method, which, as shown in fig. 1, comprises at least the following steps:
step S110, determining the probability of the predetermined state of the living beings based on the biological quantity of the predetermined state corresponding to the predetermined age and the average biological quantity.
And S120, acquiring a feature vector of the organism, and acquiring a risk parameter of the organism according to the feature vector based on the state risk model of the organism.
And S130, correcting the preset state probability by using the risk parameters to obtain the target state probability.
And S140, obtaining individual state information of the living beings at least based on the target state probability.
In the exemplary embodiment of the disclosure, based on the state risk model, the corresponding risk parameter is obtained according to the feature vector of the living being, the individual feature of the living being can be added in the state information calculation process of the living being, and a data basis is provided for individual state information estimation aiming at the living being. Furthermore, the corrected target state probability is used for calculating the individual state information, the realization possibility is provided for calculating the individual state information, the calculation is a crucial progress in the state information calculation process, and the complexity and the understanding difficulty of the state index calculation are low, so that the practical degree is high, and the application scenes are rich.
The respective steps of the biological state analyzing method are explained in detail below.
In step S110, a predetermined state probability of the living being is determined based on the predetermined state living being number and the average living being number corresponding to the predetermined age.
In an exemplary embodiment of the present disclosure, the living being may be a human being or any other animal, and this exemplary embodiment is not particularly limited thereto.
When the living being is a human, the predetermined age may be an age involved in a population living being programming process.
In an alternative embodiment, the predetermined state is a dead state.
Then, the predetermined-state living body number may be a set of basic data of the number of age-related deaths in the population life chart compilation, and the average living body number may be a set of basic data of the average living body number in the population life chart compilation in the same period as the number of age-related deaths.
Wherein, the population life table is used for researching the life process of a batch of people from birth to death. However, in actual tabulation, only real demographic data can be utilized to compile an assumed life table of one hundred thousand or million people initially on the basis of analyzing the number of people died at the age of the population and the gross death rate thereof.
Obtaining the number of organisms in a predetermined state and the average number of organisms, e.g. age-dead DxAnd age group average population P of the same periodxThereafter, a rough state probability of the living being may be derived from the predetermined state living being number and the average living being number to determine the predetermined state probability of the living being.
In an alternative embodiment, fig. 2 shows a flow chart of a method of determining a probability of a predetermined state, as shown in fig. 2, the method comprising at least the steps of: in step S210, a rough state probability is determined based on the predetermined state biomass and the average biomass corresponding to the predetermined age.
When the predetermined state probability is determined according to the predetermined state biomass and the average biomass, the rough state probability may be determined according to the predetermined state biomass and the average biomass, and the rough state probability may be obtained by performing state probability calculation on the predetermined state biomass and the average biomass corresponding to the predetermined age.
Specifically, the state probability calculation for the predetermined state biomass and the average biomass can be implemented according to formula (1):
wherein x is a predetermined age of the organism, DxIs age-dead, i.e. the number of organisms in a predetermined state, PxIs the average biomass, mxIs the coarse mortality, i.e., the coarse state probability.
In step S220, a predetermined state probability of the living being is determined based on the rough state probability.
After the coarse state probabilities are obtained, the coarse state probabilities can be subjected to probability transformation calculation according to equation (2), that is, the coarse state probabilities are transformed into corresponding predetermined state probabilities:
wherein q at this timexIs a predetermined state probability at a predetermined age of x, which may be an age death probability, axThe lifetime is counted.
In the exemplary embodiment, the state probability calculation and the probability transformation calculation are performed on the predetermined state biological quantity and the average biological quantity to obtain the corresponding rough state probability and age state probability, so that a data basis and a theoretical basis are provided for the subsequent calculation of the individual state information of the user.
Therein, the coarse state probability, i.e. the coarse death rate mxWith a predetermined state probability, i.e. the probability of age death qxHow and how the relationship between m will lead to gross mortalityxConversion into age mortality probability qxIs a subject of long-term research by human and oral scientists and statistical scientists. Thus, gross mortality mxDeath probability q with agexThe difference in expressions of (a) becomes a different way of compiling a population life sheet. For example, the probability mapping calculation may be performed on the coarse state probability and the predetermined state probability according to the formula for the probability of death of tulle.
Specifically, the probability mapping calculation may be performed on the coarse state probability and the predetermined state probability according to formula (3):
q at this timexA predetermined state probability for each year of age.
Wherein, the lifetime is counted
Then, the calculation formula of the probability of Valer death is formed:
in fact, they result from slight variations in gross mortality rates of
In step S120, a feature vector of the living being is obtained, and a risk parameter of the living being is obtained from the feature vector based on the state risk model of the living being.
In an exemplary embodiment of the present disclosure, a feature vector X composed of features of a current user is acquired.
In an alternative embodiment, the feature vector includes physiological features and behavioral features.
Wherein, the physiological characteristics include the age, sex, etc. of the living being; the behavior characteristics may include life habit characteristics, such as whether to drink alcohol and smoke, whether to suffer from serious diseases or chronic diseases, and other characteristics affecting individual status information. In some embodiments, a corresponding graphical user interface may be provided for the user to input these characteristics.
When generating the feature vector according to the features of the user, the feature may be converted into 0 or 1 to indicate whether the feature is provided, or some features may be converted into numerical representations from top to bottom, or other ways of obtaining the feature vector may also be provided, which is not particularly limited in this exemplary embodiment.
After the feature vector is obtained, the risk parameter of the living being can be obtained according to the feature vector.
In an alternative embodiment, fig. 3 shows a flow diagram of a method for obtaining a risk parameter, as shown in fig. 3, the method at least comprises the following steps: in step S310, a state risk model of the living being is constructed using a survival analysis algorithm.
The state risk model may be a health risk model. Specifically, for the death outcome event, the characteristics of the user can be collected, an observation queue research method is adopted, and the relationship between the risk factor and the death outcome is obtained by a survival analysis algorithm, such as a COX regression analysis algorithm. Therefore, a health risk model, i.e., a COX proportional hazards regression model (COX proportional hazards model), can be constructed using COX regression analysis algorithms.
The Cox risk proportional regression model is defined as shown in equation (4):
h(t)＝h0(t)exp(Xiβ) (4)
wherein h is0(t) is a baseline risk equation, which may be any non-negative equation for time t; xiIs the feature vector for example i and β is the coefficient vector, which is obtained by maximizing the cox partial likelihood.
When example i is substituted into equation (4) with male and female, respectively, and the ratio of female and male is found, e can be obtainedβNamely, the Ratio of risk function values (Hazzard Ratio, HR), referred to as the risk Ratio. Wherein the risk ratio HR must be greater than 0.
It should be noted that the constructed state risk model may be implemented by using other algorithms according to modeling requirements, and this exemplary embodiment is not particularly limited thereto.
In step S320, a state risk calculation is performed on the feature vector by using the state risk model to obtain a risk parameter of the living being.
The calculation method of HR may refer to formula (5):
HR＝exp(β1*x1+β2*x2+…+βn*xn)＝e<β*X> (5)
wherein β is a coefficient vector, β ═ β (β)1,β2,…,βn) X is a feature vector X ═ X1,x2,…,xn)，<β*X>Represents the inner product of β and X.
Therefore, after the feature vector is obtained, the corresponding risk parameter can be obtained by substituting the feature vector into the formula (5).
When the risk parameter is more than 1 and has statistical significance, the death risk of the individual with the user characteristic is higher than the average level of the research objects. In particular, the risk of death of the user is a multiple of the HR of the mean level of the study, i.e. a multiple of the risk parameter, and the user is therefore characterized as a risk factor.
A risk parameter of less than 1 is statistically significant, indicating that the risk of death for an individual characterized by this user is below the average for the study subject. In particular, the risk of death of the user is a multiple of the HR of the mean level of the study, i.e. a multiple of the risk parameter, and the user is therefore characterized as a protective factor.
Wherein, the statistical significance refers to the level of risk to be assumed by the rejected zero hypothesis, which is also called the probability level or the significance level, when the zero hypothesis is true. Significance means that any difference between the attitudes of the two populations is due to systematic factors, not to the influence of accidental factors. It is assumed that all other factors that may affect the difference between the two populations are controlled, and therefore the remaining explanation is the inferred factor, which cannot be guaranteed 100%, so there is a certain probability value, called significance level.
Otherwise, HR is not statistically significantly different from 1 and is not considered a risk factor or a protective factor.
In the case where the user has multiple features, the risk parameter is equal to the HR multiplication of the respective features.
In addition, the state risk model may obtain the death probability h (t) of the user at time t (time 0 from the start of the queue study), and h (t) ═ h (t) × HR. Where h (t) represents the mean probability of death of the subject at time t and HR is the individual's risk ratio.
In the exemplary embodiment, the constructed state risk model can be used to obtain corresponding risk parameters, so that a correction basis is provided for correcting the predetermined state probability, and the accuracy of the target state probability is ensured.
In step S130, the predetermined state probability is corrected by the risk parameter to obtain the target state probability.
In an exemplary embodiment of the present disclosure, after obtaining the risk parameter and the predetermined state probability, the predetermined state probability may be subjected to a correction process using the risk parameter.
In an alternative embodiment, fig. 4 shows a flow diagram of a method of correction processing, which, as shown in fig. 4, at least comprises the following steps: in step S410, a correction calculation result is obtained by performing a correction calculation on the risk parameter and the predetermined state probability, and a correction threshold corresponding to the correction calculation result is acquired.
Specifically, the mode of performing the correction calculation on the risk parameter and the predetermined state probability may be to perform a multiplication operation on the risk parameter and the predetermined state probability to obtain a correction calculation result. In addition, other correction calculation may be performed according to actual conditions, and this exemplary embodiment is not particularly limited to this.
Further, a correction threshold corresponding to the correction calculation result is acquired. In general, the correction threshold may be 1.0, or other values of the correction threshold may be set according to actual situations, which is not particularly limited in this exemplary embodiment.
In step S420, the correction calculation result is compared with a correction threshold to determine a target state probability.
To determine the target state probability, the correction calculation may be compared to the correction probability.
In an alternative embodiment, fig. 5 shows a flow chart of a method for determining a target state probability, as shown in fig. 5, the method at least comprises the following steps: in step S510, when the correction calculation result is equal to or less than the correction threshold value, the correction calculation result is determined to be the target state probability.
And when the comparison result of the correction calculation result and the correction probability is that the correction calculation result is less than or equal to the correction threshold, determining the correction calculation result as the target state probability. That is to say, the first and second electrodes,
wherein,
is the target state probability, qxIs a predetermined state probability, HR is a risk parameter.
In step S520, when the correction calculation result is larger than the correction threshold, the correction threshold is determined to be the target state probability.
And when the comparison result of the correction calculation result and the correction probability is that the correction calculation result is greater than the correction threshold value, determining the correction calculation result as the target state probability. That is to say, the first and second electrodes,
wherein,
for the target state probability, 1.0 is the correction threshold.
In the exemplary embodiment, the target state probability is obtained by correcting the predetermined state probability through the risk parameter, and the accuracy of the target state probability is better than that of the predetermined state probability, so that the possibility is provided for calculating the individual state information of the user, and the technical problem that the individual state information cannot be determined in various application scenarios is solved.
In step S140, individual state information of the living being is obtained based on at least the target state probability.
In an exemplary embodiment of the present disclosure, after obtaining the target state probability, the individual state information of the living being may be obtained by performing a state index calculation on the target state probability.
Specifically, when the living being is a human, the death probability refers to the probability that the user has not live to x +1 year after living to x year, and according to the definition, the theoretical calculation formula is as follows:
but the number of deaths dxNumber of surviving persons lxIs unknown, and therefore, to be converted into an expression that can be actually calculated, the kouer equation (3) is one of them.
Wherein the number of remaining persons lxThe number of people just entering x years of age, lx+1＝lx-dx。l0Assuming that the initial number of people in a batch is often hundreds of thousands or millions of people, taking l0＝100000。l0It does not matter what value is taken because of the definition of the life table. Assuming a group of people equal to the sum of the number of deaths from 0, 1, 2, …, omega-1 years (top age), it is a balance of life and death:
this is always true.
Number of deaths dxMeans that0This assumes that the population survived until age x and did not survive until age x +1, e.g., d0Means the number of deaths from birth to the full age, d0＝l0q0；d1Is the number of deaths from 1 year of age to 2 years of age, d1＝l1q1Etc. are equal to dx＝lxqx。
Thus, l can be obtainedx+1＝lx-dx＝lx*(1-qx)。
In addition, the average number of survivors LxThe average number of the years of survivors from x years to x + n years is a general level index for measuring the life length of the population, and the death level of the population at different age levels is different, so the calculation formula is as follows:
the average survival accumulated years downwards refer to the following formula:
finally, the individual status information of the user is as shown in equation (10):
wherein the individual status information
The user's personal predicted lifetime may be represented.
When the living being is a human, after obtaining the individual state information, the individual state information can be further used to determine a result of the characteristic influence of the characteristics of the user on the individual state information.
In an alternative embodiment, fig. 6 shows a flow chart of a method for determining a feature impact result, as shown in fig. 6, the method at least includes the following steps: in step S610, a state index calculation is performed on the predetermined state probability to obtain average state information, and a state benefit calculation is performed on the individual state information and the average state information to obtain potential benefit information.
After the predetermined state probability is obtained, the average state information, i.e., the average predicted lifetime, can be calculated according to equations (6) - (10).
Further, the individual state information and the average state information are subjected to state profit and loss calculation to obtain potential profit and loss information.
Specifically, the potential loss information is average _ expected _ life-index _ expected _ life. Wherein, the average _ expected _ life is average state information, and the individual _ expected _ life is individual state information.
In step S620, the potential profit and loss information and the profit and loss threshold corresponding to the potential profit and loss information are compared to determine a feature influence result of the feature vector on the individual state information.
After obtaining the potential profit-and-loss information, a profit-and-loss threshold corresponding to the potential profit-and-loss information may be obtained. Typically, the profit-and-loss threshold may be 0.
Further, to determine the result of the feature influence of the feature vector on the individual state information, the potential profit-and-loss information may be compared to a profit-and-loss threshold.
In an alternative embodiment, fig. 7 shows a schematic flow chart of a method for further determining a feature impact result, as shown in fig. 7, the method at least comprises the following steps: in step S710, when the potential profit-and-loss information is greater than the profit-and-loss threshold, it is determined that the feature vector has a negative feature impact result on the individual state information.
When the comparison result of the potential profit and loss information and the profit and loss threshold value is that the potential profit and loss information is greater than the profit and loss threshold value, it indicates that the user features included in the feature vector have an adverse effect on the personal predicted life of the user, that is, the individual state information. Thus, the feature vectors have a negative feature impact result on the individual state information.
In step S720, when the potential profit-and-loss information is less than or equal to the profit-and-loss threshold, it is determined that the feature vector has a positive feature impact result on the individual state information.
When the comparison result of the potential profit-and-loss information and the profit-and-loss threshold is that the potential profit-and-loss information is less than or equal to the profit-and-loss threshold, it indicates that the user features included in the feature vector have a favorable influence on the personal predicted life of the user, i.e., the individual state information. Therefore, the feature vector has a positive feature influence result on the individual state information, and the individual state information such as the personal predicted lifetime of the user can be extended.
In the exemplary embodiment, through the potential profit and loss information between the individual state information and the average state information, the result of the feature influence of the feature vector on the individual state information can be determined, which is helpful for the user to grasp the difference between the individual state information and the average state information, and has the function of reminding or warning the user.
In addition, when the user wants to determine the feature influence result of some feature or some features on the state information of the living being, the determination may be made by acquiring a partial feature vector in step S120.
Specifically, when a part of feature vectors is selected from feature vectors of a living organism, a vector corresponding to a feature that can be changed by the living organism may be selected. When the living being is a person, the characteristics that can be changed by the user include the characteristics of the lifestyle habits such as whether to smoke or not and whether to drink or not. Correspondingly, features such as whether the user has had a significant illness or whether there is a genetic disorder before are features that the user cannot change.
Further, the risk parameters corresponding to the feature vectors of the part are determined according to the calculation manner in step S120, which is not described herein again.
When calculating the risk parameters of the part of feature vectors, the feature vectors consisting of the age and the sex of the user and the lifestyle features such as smoking and drinking can be used.
Further, the corresponding target state information is determined according to the correction processing manner in step S130, which is not described herein again.
Then, the potential benefit information of the partial feature vectors is calculated according to the state benefit calculation method in step S610, which is not described herein again.
Further, the potential benefit and benefit information of the partial feature vectors is compared according to the benefit and benefit threshold in step S620, and a result of the feature influence of the partial feature vectors on the individual state information is determined, and the determination method is the same, and is not described herein again.
In some embodiments, after the feature influence result of the feature on the individual state information is obtained, corresponding auxiliary information, such as lifestyle improvement suggestions and the like, may also be provided to the user based on the corresponding feature influence result.
In the exemplary embodiment, the characteristic influence result of the characteristic vector concerned by the user on the individual state information can be determined by selecting part of the characteristic vectors, so that the user can change the state information of the user or other living beings independently, the guiding effect of the part of the characteristic vectors on living habits and other modes of the living beings is exerted, and the health state of the living beings is improved.
For example, exemplary embodiments of the present disclosure may be applied in an application scenario for quantitative analysis of the impact of individual-oriented health risks on life expectancy. For example, if a user is male, 30 years of age, has smoked for ten years. The user is added without smoking cessation and his life expectancy is reduced by a factor of less than the level of no smoking. This problem cannot be solved using population life expectancy calculations, which do not give personalized life expectancy estimates to individuals. However, the use of the biological state analysis method in the present disclosure enables accurate prediction of the expected age of the user individual, that is, individual state information.
Exemplary embodiments of the present disclosure may also be applied in the context of quantitative simulation of public health policies for social life expectancy. In order to improve the life expectancy of society, public health policies such as smoking control, alcohol consumption limitation, promotion of the popularity of full-name fitness, and promotion of the level of health care services are generally adopted. The quantitative impact of each policy on social life expectancy cannot be addressed by the population life expectancy algorithms. However, assuming a policy that 10% of the existing 60 year old smoking population are converted into non-smoking population, the biological state analysis method in the present disclosure can be used to calculate the characteristic influence result caused by the smoking user characteristic.
In the exemplary embodiment of the disclosure, based on the state risk model, the corresponding risk parameter is obtained according to the feature vector of the living being, the individual feature of the living being can be added in the calculation process of the state information of the living being, and a data basis is provided for individual state information estimation aiming at the living being. Furthermore, the corrected target state probability is used for calculating the individual state information, the realization possibility is provided for calculating the individual state information, the method is a crucial progress in the state information calculation process, the complexity and the understanding difficulty of state index calculation are low, and the application scene of calculating the individual state information is enriched.
Further, in an exemplary embodiment of the present disclosure, there is also provided a biological status analysis device, the living being having a predetermined age. Fig. 8 shows a schematic configuration diagram of the biological state analysis apparatus, and as shown in fig. 8, the biological state analysis apparatus 800 may include: probability calculation module 810, risk calculation module 820, probability correction module 830, and state determination module 840. Wherein:
a probability calculation module 810 configured to determine a predetermined state probability of the living being based on the predetermined state living being number and the average living being number corresponding to the predetermined age; a risk calculation module 820 configured to obtain a feature vector of the living being and obtain a risk parameter of the living being according to the feature vector based on a state risk model of the living being; a probability correction module 830 configured to correct the predetermined state probability by using the risk parameter to obtain a target state probability; an index calculation module 840 configured to derive individual state information of the living being based at least on the target state probability.
In an exemplary embodiment of the present disclosure, the obtaining of the target state probability by performing a correction process on the predetermined state probability using the risk parameter includes:
carrying out correction calculation on the risk parameters and the predetermined state probability to obtain a correction calculation result, and acquiring a correction threshold corresponding to the correction calculation result;
the correction calculation is compared to a correction threshold to determine a target state probability.
In an exemplary embodiment of the present disclosure, determining the target state probability includes:
when the correction calculation result is less than or equal to the correction threshold value, determining that the correction calculation result is the target state probability;
and when the correction calculation result is larger than the correction threshold value, determining the correction threshold value as the target state probability.
In an exemplary embodiment of the present disclosure, after determining the predetermined state probability of the living being based on the predetermined state living being number and the average living being number corresponding to the predetermined age, the biological state analysis method further includes:
obtaining average state information based on the predetermined state probability, and performing state profit and loss calculation on the individual state information and the average state information to obtain potential profit and loss information;
and comparing the potential profit and loss information with profit and loss thresholds corresponding to the potential profit and loss information to determine a characteristic influence result of the characteristic vector on the individual state information.
In an exemplary embodiment of the present disclosure, determining a feature influence result of a feature vector on individual state information includes:
when the potential profit and loss information is larger than the profit and loss threshold, determining that the feature vector has a negative feature influence result on the individual state information;
when the potential profit-and-loss information is less than or equal to the profit-and-loss threshold, determining that the feature vector has a positive feature impact result on the individual state information.
In an exemplary embodiment of the present disclosure, determining the predetermined state probability of the living being based on the predetermined state living being number corresponding to the predetermined age and the average living being number includes:
determining a rough state probability according to the preset state biological quantity and the average biological quantity corresponding to the preset age;
from the rough state probabilities, predetermined state probabilities of the living being are determined.
In an exemplary embodiment of the present disclosure, the obtaining a risk parameter of a living being from a feature vector based on a state risk model of the living being includes:
constructing a state risk model of the organism by using a survival analysis algorithm;
and performing state risk calculation on the feature vectors by using the state risk model to obtain the risk parameters of the organisms.
In an exemplary embodiment of the present disclosure, the feature vector includes physiological features and behavioral features.
In one exemplary embodiment of the present disclosure, the predetermined state is a death state.
The details of the above-mentioned biological state analysis device have been described in detail in the corresponding biological state analysis method, and thus are not described herein again.
It should be noted that although several modules or units of the biological state analysis device 800 are mentioned in the above detailed description, such division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit, according to embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units.
In addition, in an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided.
An electronic device 900 according to such an embodiment of the invention is described below with reference to fig. 9. The electronic device 900 shown in fig. 9 is only an example and should not bring any limitations to the function and scope of use of the embodiments of the present invention.
As shown in fig. 9, the electronic device 900 is embodied in the form of a general purpose computing device. Components of electronic device 900 may include, but are not limited to: the at least one processing unit 910, the at least one storage unit 920, a bus 930 connecting different system components (including the storage unit 920 and the processing unit 910), and a display unit 940.
Wherein the storage unit stores program code that is executable by the processing unit 910 to cause the processing unit 910 to perform steps according to various exemplary embodiments of the present invention described in the above section "exemplary methods" of the present specification.
The storage unit 920 may include readable media in the form of volatile memory units, such as a random access memory unit (RAM)921 and/or a cache memory unit 922, and may further include a read only memory unit (ROM) 923.
The electronic device 900 may also communicate with one or more external devices 1100 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 900, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 900 to communicate with one or more other computing devices. Such communication may occur via input/output (I/O) interface 950. Also, the electronic device 900 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN) and/or a public network, such as the Internet) via the network adapter 960. As shown, the network adapter 940 communicates with the other modules of the electronic device 900 over the bus 930. It should be appreciated that although not shown, other hardware and/or software modules may be used in conjunction with the electronic device 900, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.
In an exemplary embodiment of the present disclosure, there is also provided a computer-readable storage medium having stored thereon a program product capable of implementing the above-described method of the present specification. In some possible embodiments, aspects of the invention may also be implemented in the form of a program product comprising program code means for causing a terminal device to carry out the steps according to various exemplary embodiments of the invention described in the above-mentioned "exemplary methods" section of the present description, when said program product is run on the terminal device.
Referring to fig. 10, a program product 1000 for implementing the above method according to an embodiment of the present invention is described, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited in this regard and, in the present document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
A computer readable signal medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
Claims (12)
1. A method of analyzing the condition of a living being, said living being having a predetermined age, said method comprising:
determining a predetermined state probability of the living being based on a predetermined state living being number and an average living being number corresponding to the predetermined age;
acquiring a feature vector of the living being, and obtaining a risk parameter of the living being according to the feature vector based on a state risk model of the living being;
correcting the preset state probability by using the risk parameters to obtain a target state probability;
and obtaining the individual state information of the living beings at least based on the target state probability.
2. The biological state analysis method according to claim 1, wherein the obtaining of the target state probability by performing the correction processing on the predetermined state probability using the risk parameter includes:
carrying out correction calculation on the risk parameters and the predetermined state probability to obtain a correction calculation result, and acquiring a correction threshold corresponding to the correction calculation result;
comparing the correction calculation to the correction threshold to determine a target state probability.
3. The biological state analysis method of claim 2, wherein the determining a target state probability comprises:
when the correction calculation result is less than or equal to the correction threshold value, determining that the correction calculation result is a target state probability;
and when the correction calculation result is larger than the correction threshold value, determining that the correction threshold value is the target state probability.
4. The biological state analysis method according to claim 1, wherein after the determining the predetermined state probability of the living being based on the predetermined state living being number and the average living being number corresponding to the predetermined age, the method further comprises:
based on the predetermined state probability, obtaining average state information of the living beings, and carrying out state profit and loss calculation on the individual state information and the average state information to obtain potential profit and loss information;
comparing the potential profit and loss information with profit and loss thresholds corresponding to the potential profit and loss information to determine a feature influence result of the feature vector on the individual state information.
5. The method of claim 4, wherein the determining the feature influence result of the feature vector on the individual status information comprises:
determining that the feature vector has a negative feature impact result on the individual state information when the potential profit-and-loss information is greater than the profit-and-loss threshold;
determining that the feature vector has a positive feature impact result on the individual state information when the potential benefit information is less than or equal to the benefit threshold.
6. The biological state analysis method according to claim 1, wherein the determining the predetermined state probability of the living being based on the predetermined state living being number and the average living being number corresponding to the predetermined age comprises:
determining a rough state probability according to the preset state biological quantity and the average biological quantity corresponding to the preset age;
determining a predetermined state probability of the living being based on the coarse state probability.
7. The method of claim 1, wherein the obtaining the risk parameter of the living being from the feature vector based on the state risk model of the living being comprises:
constructing a state risk model of the organism by using a survival analysis algorithm;
and performing state risk calculation on the feature vectors by using the state risk model to obtain the risk parameters of the living beings.
8. The biological state analysis method of claim 1, wherein the feature vector includes a physiological feature and a behavioral feature.
9. The biological state analysis method according to claim 1, wherein the predetermined state is a death state.
10. A biological condition analyzing apparatus, said organism having a predetermined age, comprising:
a probability calculation module configured to determine a predetermined state probability of the living being based on a predetermined state living being number corresponding to the predetermined age and an average living being number;
a risk calculation module configured to obtain a feature vector of the living being and obtain a risk parameter of the living being according to the feature vector based on a state risk model of the living being;
the probability correction module is configured to correct the predetermined state probability by using the risk parameter to obtain a target state probability;
a state determination module configured to derive individual state information of the living being based at least on the target state probability.
11. A computer-readable storage medium on which a computer program is stored, the computer program, when being executed by a transmitter, implementing the biological status analysis method according to any one of claims 1 to 9.
12. An electronic device, comprising:
a transmitter;
a memory for storing executable instructions of the transmitter;
wherein the transmitter is configured to perform the biological status analysis method of any one of claims 1-9 via execution of the executable instructions.
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