CN114496264A - Health index analysis method, device, equipment and medium based on multi-dimensional data - Google Patents
Health index analysis method, device, equipment and medium based on multi-dimensional data Download PDFInfo
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Abstract
The invention relates to an artificial intelligence technology, and discloses a health index analysis method based on multidimensional data, which comprises the following steps: classifying a user group into a plurality of disease categories according to medical history data; performing time sequence analysis on the medical history data corresponding to each disease category to obtain disease causes; acquiring monitoring index data of each user in a user group, carrying out time sequence correlation on the monitoring index data and the medical history data, and carrying out curve trend analysis to obtain an index change trend corresponding to each disease category; acquiring user index data of a target user in a preset time period, and identifying the numerical value change trend of the user index data; calculating the potential risk value of the target user to each disease category according to the value change trend and the index change trend; and calculating the comprehensive health index of the target user according to the disease causes and the potential risk values. The invention also provides a health index analysis device, equipment and medium based on the multidimensional data. The invention can improve the reasonability of the analysis of the health condition.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a health index analysis method and device based on multidimensional data, electronic equipment and a computer readable storage medium.
Background
With the gradual improvement of living standard, people pay more attention to their health conditions. People adopt intelligent devices such as intelligent bracelets and multifunctional watches to monitor self health data, analyze the monitored data and try to judge self health condition from the data.
Most of the existing health condition analysis methods in the market are alarm analysis based on current data, namely, the current heart rate, blood pressure and other data of a user are monitored through intelligent equipment, and a response prompt is given by combining the current data. However, in practical application, the health condition is often influenced by long-term factors, and the method only relies on currently monitored data for analysis, which results in hysteresis and low accuracy of the analysis result.
Disclosure of Invention
The invention provides a health index analysis method and device based on multidimensional data and a computer readable storage medium, and mainly aims to solve the problem of low reasonableness in health condition analysis.
In order to achieve the above object, the present invention provides a health index analysis method based on multidimensional data, including:
acquiring medical history data of each user in a preset user group, and classifying the user group into a plurality of disease categories according to the medical history data;
performing time sequence analysis on the medical history data corresponding to the user of each disease category to obtain a disease cause corresponding to each disease category;
acquiring monitoring index data of each user in the user group, and performing time sequence association on the monitoring index data and the medical history data according to the time sequence;
carrying out curve trend analysis on the monitoring index data after the time-series correlation to obtain index change trends corresponding to each disease category;
acquiring user index data of a target user in a preset time period, and identifying the numerical value change trend of the user index data;
calculating a potential risk value of the target user for each disease category according to the value change trend and the index change trend;
and calculating the comprehensive health index of the target user according to the disease causes and the potential risk values.
Optionally, the classifying the user population into a plurality of disease categories according to the medical history data includes:
acquiring a disease name table, and compiling a regular expression according to the disease name in the disease name table;
disease name recalling is carried out on the medical history data by utilizing the regular expression to obtain the disease name of each user in the user group;
classifying each user within the user population into a plurality of disease categories according to the disease name.
Optionally, the performing time-series analysis on the medical history data corresponding to the user of each disease category to obtain a disease cause corresponding to each disease category includes:
selecting one disease category from the disease categories one by one as a target category, and mapping the disease occurrence time of the user of the target category to a preset coordinate system;
judging whether the data distribution of the disease occurrence time in the coordinate system conforms to white noise distribution;
if the data distribution of the disease onset time in the coordinate system accords with white noise distribution, determining that the disease inducement of the target class is irrelevant to time;
if the data distribution of the disease occurrence time in the coordinate system does not accord with white noise distribution, extracting the distribution density of the data distributer of the disease occurrence time in the coordinate system, and determining the disease cause of the target category according to the distribution density.
Optionally, the extracting a distribution density of a data distributor of the disease onset time in the coordinate system, and determining the disease cause of the target category according to the distribution density includes:
counting the data density of the disease occurrence time in each preset interval in the coordinate system;
selecting a preset interval with the data density larger than a preset density threshold value as a high-density interval;
and determining the time period corresponding to the high-density interval in the coordinate system as the disease cause of the target category.
Optionally, the performing time-series association between the monitoring index data and the medical history data according to a chronological order includes:
counting the monitoring time of each data in the monitoring index data;
counting the generation time of each data in the medical history data;
and performing associated storage on the monitoring index data and the medical history data of which the monitoring time is consistent with the generation time, and completing the time sequence association of the monitoring index data and the medical history data.
Optionally, the performing curve trend analysis on the monitoring index data after the time-series correlation to obtain an index change trend corresponding to each disease category includes:
performing curve fitting on the monitoring index data after time sequence correlation to obtain a curve graph corresponding to each disease category;
performing convolution processing and pooling processing on the curve graph for preset times to obtain curve characteristics of the curve graph;
and calculating the distance values between the curve characteristics and a plurality of preset trend labels one by one, and determining the trend label with the minimum distance value as the index change trend of the monitoring index data after the time sequence correlation.
Optionally, the calculating, according to the numerical trend and the index trend, a potential risk numerical value of the target user for each disease category includes:
drawing a data distribution map of the user index data;
selecting the disease category with the index variation trend being the same as the numerical value variation trend as a potential disease category;
and calculating the matching degree between the curve graph corresponding to each category in the potential suffering categories and the data distribution graph one by one, and determining the matching degree as the potential risk value of the target user for each disease category.
In order to solve the above problems, the present invention also provides a health index analyzing apparatus based on multidimensional data, the apparatus comprising:
the data analysis module is used for acquiring medical history data of each user in a preset user group, classifying the user group into a plurality of disease categories according to the medical history data, and performing time sequence analysis on the medical history data corresponding to the user of each disease category to obtain a disease incentive corresponding to each disease category;
the time sequence correlation module is used for acquiring monitoring index data of each user in the user group and carrying out time sequence correlation on the monitoring index data and the medical history data according to the time sequence;
the trend analysis module is used for carrying out curve trend analysis on the monitoring index data after time sequence correlation to obtain index change trends corresponding to each disease category, acquiring user index data of a target user in a preset time period, and identifying the numerical change trend of the user index data;
and the numerical value calculation module is used for calculating a potential risk numerical value of the target user for each disease category according to the numerical value change trend and the index change trend, and calculating a comprehensive health index of the target user according to the disease cause and the potential risk numerical value.
In order to solve the above problem, the present invention also provides an electronic device, including:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method for multidimensional data based health index analysis described above.
In order to solve the above problem, the present invention further provides a computer-readable storage medium, in which at least one computer program is stored, and the at least one computer program is executed by a processor in an electronic device to implement the above health index analysis method based on multidimensional data.
According to the embodiment of the invention, the disease causes corresponding to different disease categories are obtained by classifying and analyzing the time sequence of the medical history data of each user in the preset user group, so that the potential causes of diseases of different disease categories can be reasonably analyzed by using big data; meanwhile, the monitoring index data of each user in the user group is subjected to time sequence correlation with the medical history data, trend analysis is further performed on the monitoring index data subjected to time sequence correlation, potential risks of the target user to different types of diseases are analyzed on a long-term time line, and finally the health condition of the target user is comprehensively judged by combining the disease inducement and the potential risks, so that the health condition of the user is analyzed and evaluated in a long-term and multi-dimensional mode, and the reasonability of a final analysis result is improved. Therefore, the health index analysis method, the health index analysis device, the electronic equipment and the computer readable storage medium based on the multidimensional data can solve the problem of low reasonableness during health condition analysis.
Drawings
Fig. 1 is a schematic flow chart of a health index analysis method based on multidimensional data according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a process for calculating a matching value according to an embodiment of the present invention;
FIG. 3 is a schematic flow chart illustrating selection of a second user representation according to an embodiment of the present invention;
FIG. 4 is a functional block diagram of a health index analysis apparatus based on multidimensional data according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device implementing the multidimensional data based health index analysis method according to an embodiment of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The embodiment of the application provides a health index analysis method based on multi-dimensional data. The executing subject of the health index analysis method based on multidimensional data includes, but is not limited to, at least one of electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiments of the present application. In other words, the health index analysis method based on multidimensional data may be performed by software or hardware installed in a terminal device or a server device. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like. The server may be an independent server, or may be a cloud server that provides basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a Network service, cloud communication, a middleware service, a domain name service, a security service, a Content Delivery Network (CDN), a big data and artificial intelligence platform, and the like.
Fig. 1 is a schematic flow chart of a health index analysis method based on multidimensional data according to an embodiment of the present invention. In this embodiment, the method for analyzing a health index based on multidimensional data includes:
s1, acquiring medical history data of each user in a preset user group, and classifying the user group into a plurality of disease categories according to the medical history data.
In the embodiment of the invention, the preset group comprises a plurality of users, and the medical history data refers to historical disease data authorized to be uploaded by each user in the user group, and records a plurality of diseases suffered by each user in a historical time period, the disease time of each disease and other data.
In detail, in order to implement detailed analysis of the medical history data of the user group in a classified manner, the user group may be classified according to the medical history data to obtain a plurality of disease categories corresponding to the user group.
In an embodiment of the present invention, the classifying the user group into a plurality of disease categories according to the medical history data includes:
acquiring a disease name table, and compiling a regular expression according to the disease name in the disease name table;
disease name recalling is carried out on the medical history data by utilizing the regular expression to obtain the disease name of each user in the user group;
classifying each user in the user population into a plurality of disease categories according to the disease name.
In detail, the disease name table includes a plurality of disease names, and preset characters can be compiled into a regular expression according to the disease names in the disease name table, where the regular expression is a sentence that can be used to match and recall a specific format field in a text.
Specifically, the medical history data may be processed by using the regular expression, and then a field identical to the disease name in the disease name table is extracted from the medical history data, so as to obtain the disease name of each user in the user group.
Further, users with the same disease name can be divided into a group to realize classification of the users, and users with multiple disease categories are obtained.
And S2, performing time sequence analysis on the medical history data corresponding to the user of each disease type to obtain the disease cause corresponding to each disease type.
In one practical application scenario of the present invention, the occurrence of different diseases is not only determined by the current health condition of the user, but also needs to consider factors such as age, environment, and living habits, wherein due to the increasing degree of integration of the society, the environments and living habits of most groups are similar, and therefore, time (i.e. age) becomes an important consideration factor for the occurrence of different diseases.
In detail, the medical history data corresponding to the user of each disease category may be analyzed in a time sequence, so as to obtain the disease causes corresponding to each disease category from the medical history data through a time relationship, where the disease causes include a development trend of each disease along with time, and an induction trend of diseases subsequently developed by the user on the premise that the user develops a certain disease.
In an embodiment of the present invention, the performing a time series analysis on the medical history data corresponding to the user of each disease category to obtain a disease cause corresponding to each disease category includes:
selecting one disease category from the disease categories one by one as a target category, and mapping the disease occurrence time of the user of the target category to a preset coordinate system;
judging whether the data distribution of the disease occurrence time in the coordinate system accords with white noise distribution;
if the data distribution of the disease onset time in the coordinate system accords with white noise distribution, determining that the disease inducement of the target class is irrelevant to time;
if the data distribution of the disease onset time in the coordinate system does not accord with white noise distribution, extracting the distribution density of the data distribution symbols of the disease onset time in the coordinate system, and determining the disease cause of the target type according to the distribution density.
In detail, the coordinates (i.e. data distribution) of the disease onset time in the coordinate system can be counted, a preset white noise curve is fitted according to the coordinates, and when the coordinates are matched with the white noise curve within a preset error range, it is determined that the data distribution of the disease onset time in the coordinate system conforms to the white noise distribution, that is, it is determined that the disease inducement of the target category is independent of time.
Further, when the coordinate is not matched with the white noise curve within a preset error range, it is determined that the data distribution of the disease occurrence time in the coordinate system does not conform to the white noise distribution, that is, it is determined that the disease cause of the target category is time-dependent, and the distribution density of the data distribution symbols of the disease occurrence time in the coordinate system can be extracted, thereby determining the disease cause of the target category.
In an embodiment of the present invention, the extracting a distribution density of a data distributor of the disease onset time in the coordinate system, and determining the disease cause of the target category according to the distribution density includes:
counting the data density of the disease occurrence time in each preset interval in the coordinate system;
selecting a preset interval with the data density larger than a preset density threshold value as a high-density interval;
and determining the corresponding time period of the high-density interval in the coordinate system as the disease cause of the target category.
In detail, an axis representing time in the coordinate system may be divided according to a preset interval length, and a data amount in each preset interval is counted, and the data amount is used as a data density corresponding to each preset interval.
Specifically, a time period corresponding to a high-density interval with data density greater than a preset density threshold value in the coordinate system may be set as a disease cause of the target category; for example, if the time of prevalence (high density interval) of a target class of disease is concentrated between 20 and 25 years of age, it can be determined that the disease predisposition for the target class of disease includes the time period between 20 and 25 years of age.
And S3, acquiring monitoring index data of each user in the user group, and performing time sequence association on the monitoring index data and the medical history data according to the time sequence.
In the embodiment of the present invention, the monitoring index data includes data obtained by monitoring health indexes such as heart rate, blood pressure, blood oxygen content, and the like of each user in the user group.
In detail, data acquisition equipment such as a smart watch and a bracelet worn by a user can be used for monitoring and recording health indexes of the user in real time, and then monitoring index data of each user in the user group are obtained.
Further, in order to implement the joint analysis of the monitoring index data and the medical history data, the monitoring index data and the medical history data may be temporally associated according to a chronological order, so as to improve the accuracy of subsequent analysis.
In the embodiment of the present invention, the performing time sequence association on the monitoring index data and the medical history data according to a time sequence includes:
counting the monitoring time of each data in the monitoring index data;
counting the generation time of each data in the medical history data;
and performing associated storage on the monitoring index data and the medical history data of which the monitoring time is consistent with the generation time, and completing the time sequence association of the monitoring index data and the medical history data.
In detail, each data in the monitoring index data and each data in the medical history data can be associated and stored according to the corresponding monitoring time and the corresponding generation time, so that the time sequence association of the monitoring index data and the medical history data is realized, and the reasonability and the accuracy of the subsequent data analysis are improved conveniently.
And S4, carrying out curve trend analysis on the monitoring index data after the time-series correlation to obtain the index change trend corresponding to each disease category.
In one practical application scenario of the invention, in order to analyze how the monitored indexes of the user change before diseases of different disease types are suffered and sent, curve trend analysis can be performed on the monitoring index data after time-series correlation so as to observe the potential relation between the monitoring index data and the diseases.
In detail, a preset trend analysis model can be used for performing curve trend analysis on the time-series correlated monitoring index data, wherein the trend analysis model includes but is not limited to a convolutional neural network model.
In an embodiment of the present invention, the performing curve trend analysis on the monitoring index data after the time-series correlation to obtain an index change trend corresponding to each disease category includes:
performing curve fitting on the monitoring index data after time sequence correlation to obtain a curve graph corresponding to each disease category;
performing convolution processing and pooling processing on the curve graph for preset times to obtain curve characteristics of the curve graph;
and calculating the distance values between the curve characteristics and a plurality of preset trend labels one by one, and determining the trend label with the minimum distance value as the index change trend of the monitoring index data after the time sequence correlation.
In detail, the monitoring index data after time sequence correlation can be subjected to curve fitting by using an interpolation method, so that the efficiency of generating the curve graph is improved.
Specifically, the graph may be subjected to convolution processing and pooling processing for a preset number of times by using a convolution layer and a pooling layer of a preset convolutional neural network, so as to extract curve features from the graph.
Further, the distance values between the curve feature and the preset plurality of trend labels may be calculated one by one using a preset distance value algorithm, wherein the distance value algorithm includes, but is not limited to, a cosine distance algorithm and a euclidean distance algorithm.
S5, user index data of the target user in a preset time period are obtained, and the numerical value change trend of the user index data is identified.
In the embodiment of the present invention, the user index data refers to data of each index obtained by detecting a target user within a preset time period.
For example, the target user is detected in the past month to obtain various index data such as heart rate, blood pressure, blood oxygen content and the like.
In one practical application scenario of the invention, since the human bodies of the diseases of the same disease category have similar or identical self-reactions before the diseases are suffered and sent, the numerical change trend of the acquired user index data of the target user in a preset time period can be analyzed, so that the potential suffering and sending risks of the target user to the diseases of different categories can be accurately judged in the follow-up process.
In the embodiment of the present invention, the step of identifying the numerical variation trend of the user index data is consistent with the step of performing curve trend analysis on the monitoring index data after the time-series association in step S4 to obtain the index variation trend corresponding to each disease category, and details are not repeated here.
And S6, calculating the potential risk value of the target user for each disease category according to the value change trend and the index change trend.
In the embodiment of the present invention, since the numerical variation trend and the index variation trend are only single labels, in order to improve the accuracy of analyzing the numerical variation trend and the index variation trend, the potential risk value of the target user for each disease category may be calculated in detail.
In an embodiment of the present invention, the calculating a value of the potential risk of the target user for each disease category according to the value variation trend and the index variation trend includes:
drawing a data distribution map of the user index data;
selecting the disease category with the index variation trend being the same as the numerical value variation trend as a potential disease category;
and calculating the matching degree between the curve graph corresponding to each category in the potential suffering categories and the data distribution graph one by one, and determining the matching degree as the potential risk value of the target user for each disease category.
In detail, the step of drawing the data distribution map of the user index data is consistent with the step of performing curve fitting on the monitoring index data associated with the time sequence in S4 to obtain a curve graph corresponding to each disease category, which is not repeated herein.
Specifically, the calculating the matching degree between the graph corresponding to each of the potential hair-suffering categories and the data distribution map one by one includes:
calculating the matching degree between the curve graph corresponding to each category in the potential hair-suffering categories and the data distribution graph one by utilizing the following matching degree algorithm:
wherein the content of the first and second substances,
in order to be said degree of matching,
is the first in the data distribution diagram
The coordinates of the individual pieces of data,
is as follows
The first curve diagram corresponding to the potential hair-suffering category
Coordinates of each data.
S7, calculating the comprehensive health index of the target user according to the disease causes and the potential risk values.
In an embodiment of the present invention, the calculating the comprehensive health index of the target user according to the disease cause and the risk potential value includes:
acquiring the user medical history of the target user, selecting one disease category from the plurality of disease categories one by one, and determining the disease incentive number of the target user to the selected disease category according to the user medical history;
calculating to obtain a health index of the target user to the selected disease category according to the potential risk value and the number of the disease inducers by using a preset weight algorithm;
and summing the health indexes of all disease categories of the target user to obtain a comprehensive health index of the target user.
In detail, the user medical history refers to medical history data of the target user, and the number of the disease causes of the selected disease category by the target user can be determined according to the disease causes and the user medical history.
Specifically, the weighting algorithm is as follows:
wherein the content of the first and second substances,
a health index for the target user for the selected disease category,
for the value of the potential risk to be mentioned,
for the number of disease causes of the target user to the selected disease category,
、
is a preset weight coefficient.
According to the embodiment of the invention, the disease causes corresponding to different disease categories are obtained by classifying and analyzing the time sequence of the medical history data of each user in the preset user group, so that the potential causes of diseases of different disease categories can be reasonably analyzed by using big data; meanwhile, the monitoring index data of each user in the user group is subjected to time sequence correlation with the medical history data, trend analysis is further performed on the monitoring index data subjected to time sequence correlation, potential risks of the target user to different types of diseases are analyzed on a long-term time line, and finally the health condition of the target user is comprehensively judged by combining the disease inducement and the potential risks, so that the health condition of the user is analyzed and evaluated in a long-term and multi-dimensional mode, and the reasonability of a final analysis result is improved. Therefore, the health index analysis method based on the multidimensional data can solve the problem of low reasonableness in health condition analysis.
Fig. 4 is a functional block diagram of a health index analysis apparatus based on multidimensional data according to an embodiment of the present invention.
The health index analyzing apparatus 100 based on multidimensional data according to the present invention may be installed in an electronic device. According to the implemented functions, the health index analysis apparatus 100 based on multidimensional data may include a data analysis module 101, a time sequence correlation module 102, a trend analysis module 103, and a numerical value calculation module 104. The module of the present invention, which may also be referred to as a unit, refers to a series of computer program segments that can be executed by a processor of an electronic device and that can perform a fixed function, and that are stored in a memory of the electronic device.
In the present embodiment, the functions regarding the respective modules/units are as follows:
the data analysis module 101 is configured to acquire medical history data of each user in a preset user group, classify the user group into a plurality of disease categories according to the medical history data, and perform time sequence analysis on the medical history data corresponding to the user of each disease category to obtain a disease cause corresponding to each disease category;
the time sequence correlation module 102 is configured to acquire monitoring index data of each user in the user group, and perform time sequence correlation on the monitoring index data and the medical history data according to a time sequence;
the trend analysis module 103 is configured to perform curve trend analysis on the monitoring index data after the time series correlation to obtain an index change trend corresponding to each disease category, obtain user index data of a target user in a preset time period, and identify a numerical change trend of the user index data;
the numerical calculation module 104 is configured to calculate a potential risk numerical value of the target user for each disease category according to the numerical variation trend and the index variation trend, and calculate a comprehensive health index of the target user according to the disease cause and the potential risk numerical value.
In detail, when the modules in the health index analysis apparatus 100 based on multidimensional data according to the embodiment of the present invention are used, the same technical means as the health index analysis method based on multidimensional data described in fig. 1 to 3 are adopted, and the same technical effects can be produced, which is not described herein again.
Fig. 5 is a schematic structural diagram of an electronic device implementing a health index analysis method based on multidimensional data according to an embodiment of the present invention.
The electronic device 1 may comprise a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may further comprise a computer program, such as a health index analysis program based on multidimensional data, stored in the memory 11 and executable on the processor 10.
In some embodiments, the processor 10 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same function or different functions, and includes one or more Central Processing Units (CPUs), a microprocessor, a digital Processing chip, a graphics processor, a combination of various control chips, and the like. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects various components of the whole electronic device by using various interfaces and lines, and executes various functions and processes data of the electronic device by running or executing programs or modules (for example, executing a health index analysis program based on multidimensional data, etc.) stored in the memory 11 and calling data stored in the memory 11.
The memory 11 includes at least one type of readable storage medium including flash memory, removable hard disks, multimedia cards, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disks, optical disks, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device, for example a removable hard disk of the electronic device. The memory 11 may also be an external storage device of the electronic device in other embodiments, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the electronic device. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device. The memory 11 may be used not only to store application software installed in the electronic device and various types of data, such as codes of a health index analysis program based on multidimensional data, etc., but also to temporarily store data that has been output or will be output.
The communication bus 12 may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is arranged to enable connection communication between the memory 11 and at least one processor 10 or the like.
The communication interface 13 is used for communication between the electronic device and other devices, and includes a network interface and a user interface. Optionally, the network interface may include a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), which are typically used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a Display (Display), an input unit such as a Keyboard (Keyboard), and optionally a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, or the like. The display, which may also be referred to as a display screen or display unit, is suitable, among other things, for displaying information processed in the electronic device and for displaying a visualized user interface.
Only electronic devices having components are shown, and those skilled in the art will appreciate that the structures shown in the figures do not constitute limitations on the electronic devices, and may include fewer or more components than shown, or some components in combination, or a different arrangement of components.
For example, although not shown, the electronic device may further include a power supply (such as a battery) for supplying power to each component, and preferably, the power supply may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management and the like are realized through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The electronic device may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.
It is to be understood that the embodiments described are illustrative only and are not to be construed as limiting the scope of the claims.
The memory 11 in the electronic device 1 stores a health index analysis program based on multidimensional data, which is a combination of a plurality of instructions that, when executed in the processor 10, can implement:
acquiring medical history data of each user in a preset user group, and classifying the user group into a plurality of disease categories according to the medical history data;
performing time sequence analysis on the medical history data corresponding to the user of each disease category to obtain a disease cause corresponding to each disease category;
acquiring monitoring index data of each user in the user group, and performing time sequence association on the monitoring index data and the medical history data according to the time sequence;
carrying out curve trend analysis on the monitoring index data after the time series correlation to obtain index change trend corresponding to each disease category;
acquiring user index data of a target user in a preset time period, and identifying the numerical value change trend of the user index data;
calculating a potential risk value of the target user for each disease category according to the value change trend and the index change trend;
and calculating the comprehensive health index of the target user according to the disease causes and the potential risk values.
Specifically, the specific implementation method of the instruction by the processor 10 may refer to the description of the relevant steps in the embodiment corresponding to the drawings, which is not described herein again.
Further, the integrated modules/units of the electronic device 1, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. The computer readable storage medium may be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying said computer program code, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM).
The present invention also provides a computer-readable storage medium, storing a computer program which, when executed by a processor of an electronic device, may implement:
acquiring medical history data of each user in a preset user group, and classifying the user group into a plurality of disease categories according to the medical history data;
performing time sequence analysis on the medical history data corresponding to the user of each disease category to obtain a disease cause corresponding to each disease category;
acquiring monitoring index data of each user in the user group, and performing time sequence association on the monitoring index data and the medical history data according to the time sequence;
carrying out curve trend analysis on the monitoring index data after the time-series correlation to obtain index change trends corresponding to each disease category;
acquiring user index data of a target user in a preset time period, and identifying the numerical value change trend of the user index data;
calculating a potential risk value of the target user for each disease category according to the value change trend and the index change trend;
and calculating the comprehensive health index of the target user according to the disease causes and the potential risk values.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof.
The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The embodiment of the application can acquire and process related data based on an artificial intelligence technology. Among them, Artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result.
Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the system claims may also be implemented by one unit or means in software or hardware. The terms first, second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (10)
1. A method for health index analysis based on multidimensional data, the method comprising:
acquiring medical history data of each user in a preset user group, and classifying the user group into a plurality of disease categories according to the medical history data;
performing time sequence analysis on the medical history data corresponding to the user of each disease category to obtain a disease cause corresponding to each disease category;
acquiring monitoring index data of each user in the user group, and performing time sequence association on the monitoring index data and the medical history data according to the time sequence;
carrying out curve trend analysis on the monitoring index data after the time-series correlation to obtain index change trends corresponding to each disease category;
acquiring user index data of a target user in a preset time period, and identifying the numerical value change trend of the user index data;
calculating a potential risk value of the target user for each disease category according to the value change trend and the index change trend;
and calculating the comprehensive health index of the target user according to the disease causes and the potential risk values.
2. The method of multidimensional data based health index analysis as recited in claim 1, wherein said classifying said user population into a plurality of disease categories according to said medical history data comprises:
acquiring a disease name table, and compiling a regular expression according to the disease name in the disease name table;
disease name recalling is carried out on the medical history data by utilizing the regular expression to obtain the disease name of each user in the user group;
classifying each user in the user population into a plurality of disease categories according to the disease name.
3. The method for analyzing a health index based on multidimensional data as claimed in claim 1, wherein the performing a time series analysis on the medical history data corresponding to the user of each disease category to obtain the disease cause corresponding to each disease category comprises:
selecting one disease category from the disease categories one by one as a target category, and mapping the disease occurrence time of the users of the target category to a preset coordinate system;
judging whether the data distribution of the disease occurrence time in the coordinate system conforms to white noise distribution;
if the data distribution of the disease onset time in the coordinate system accords with white noise distribution, determining that the disease inducement of the target class is irrelevant to time;
if the data distribution of the disease onset time in the coordinate system does not accord with white noise distribution, extracting the distribution density of the data distribution symbol of the disease onset time in the coordinate system, and determining the disease cause of the target category according to the distribution density.
4. The method as claimed in claim 3, wherein the step of extracting a distribution density of data distribution symbols of the disease onset time in the coordinate system and determining the disease cause of the target category according to the distribution density comprises:
counting the data density of the disease occurrence time in each preset interval in the coordinate system;
selecting a preset interval with the data density larger than a preset density threshold value as a high-density interval;
and determining the corresponding time period of the high-density interval in the coordinate system as the disease cause of the target category.
5. The method for analyzing a health index based on multidimensional data according to claim 1, wherein the time-series association of the monitoring index data with the medical history data according to chronological order comprises:
counting the monitoring time of each data in the monitoring index data;
counting the generation time of each data in the medical history data;
and performing associated storage on the monitoring index data and the medical history data of which the monitoring time is consistent with the generation time, and completing the time sequence association of the monitoring index data and the medical history data.
6. The method for analyzing health index based on multidimensional data as claimed in claim 1, wherein the analyzing the curve trend of the monitoring index data after time series association to obtain the index change trend corresponding to each disease category comprises:
performing curve fitting on the monitoring index data after time sequence correlation to obtain a curve graph corresponding to each disease category;
performing convolution processing and pooling processing on the curve graph for preset times to obtain curve characteristics of the curve graph;
and calculating the distance values between the curve characteristics and a plurality of preset trend labels one by one, and determining the trend label with the minimum distance value as the index change trend of the monitoring index data after the time sequence correlation.
7. The method for analyzing health index based on multi-dimensional data as claimed in any one of claims 1 to 6, wherein the calculating the value of the potential risk of the target user for each disease category according to the value trend and the index trend comprises:
drawing a data distribution map of the user index data;
selecting the disease category with the index variation trend being the same as the numerical value variation trend as a potential disease category;
and calculating the matching degree between the curve graph corresponding to each category in the potential suffering categories and the data distribution graph one by one, and determining the matching degree as the potential risk value of the target user for each disease category.
8. A health index analysis apparatus based on multidimensional data, the apparatus comprising:
the data analysis module is used for acquiring medical history data of each user in a preset user group, classifying the user group into a plurality of disease categories according to the medical history data, and performing time sequence analysis on the medical history data corresponding to the user of each disease category to obtain a disease incentive corresponding to each disease category;
the time sequence correlation module is used for acquiring monitoring index data of each user in the user group and carrying out time sequence correlation on the monitoring index data and the medical history data according to the time sequence;
the trend analysis module is used for carrying out curve trend analysis on the monitoring index data after time sequence correlation to obtain index change trends corresponding to each disease category, acquiring user index data of a target user in a preset time period, and identifying the numerical value change trend of the user index data;
and the numerical value calculation module is used for calculating a potential risk numerical value of the target user for each disease category according to the numerical value change trend and the index change trend, and calculating a comprehensive health index of the target user according to the disease cause and the potential risk numerical value.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of multi-dimensional data based health index analysis of any one of claims 1 to 7.
10. A computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, carries out a method for multidimensional data-based health index analysis as claimed in any one of claims 1 to 7.
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