Health archive management system and method for personal full life cycle

Abstract

The invention discloses a health record management system and method for a personal full life cycle, and belongs to the technical field of health record management. Obtaining verified physical examination information of previous individuals; obtaining an abnormal amplitude I of the current personal health information in combination with the environment dense amplitude; according to the disease approximation parameters corresponding to the current individual and each previous individual, selecting an approximate previous individual; according to the address information, analyzing the arrangement dense aggregation attributes of the current person and the similar previous person, and obtaining an abnormal amplitude II of the health information of the current person; according to the abnormal amplitude I and the abnormal amplitude II corresponding to the current individual, the health information of the current individual is efficiently verified, so that the defects that the efficiency of verifying the health information of the individual is insufficient and the efficiency of collecting address information and disease information of each individual into an individual health record is insufficient in the prior art are effectively avoided.

Description

Technical Field

[0001] The present invention belongs to the technical field of health record management, and in particular relates to a health record management system and method for an individual throughout his or her life cycle. Background Art

[0002] A health record is a file that records all changes in a person's vital signs from birth to death, as well as all health-related behaviors and events that the person has engaged in.

[0003] In practical applications, the current personal health record management technology for the entire life cycle often uses the existing technical solution mentioned in the patent publication number "CN113345583", which requires each individual's address information and disease information to be collected into the personal health record. The personal health information is the personal address information and disease information, and the personal disease information includes the process information of the occurrence, development, treatment and prognosis of the individual's disease.

[0004] In order to collect each individual's address information and disease information into personal health records, it is often necessary to verify the individual's health information before collection. Personal health information verification is a very important stage. It is necessary to ensure the accuracy, availability and efficiency of the health information, so as to improve the quality of collecting each individual's address information and disease information into personal health records.

[0005] At present, personal health information verification often uses definition-introduction standards to achieve initial verification, such as information format, scope constraints, etc. During the definition of the standards, complex field research must be carried out to determine the numerical scope and format of different information, making the definition process complicated and time-consuming; and when performing verification later, the entire information byte reference is often required to determine the verified information, making the verification inefficient; and verification is often accompanied by manual execution, which also makes it inefficient to collect each individual's address information and disease information into the personal health record. Summary of the invention

[0006] To address the defects in the prior art, the present invention proposes a health record management system and method for an individual throughout his or her entire life cycle, which obtains the health information of the current individual and verified past individuals; obtains the physical examination information of verified past individuals; obtains the abnormal amplitude one of the health information of the current individual by combining the environmental density amplitude; selects approximate past individuals based on the disease approximate parameters corresponding to the current individual and each past individual; analyzes the arrangement density attributes of the current individual and the approximate past individuals based on the address information, and obtains the abnormal amplitude two of the health information of the current individual; and efficiently verifies the health information of the current individual based on the abnormal amplitude one and the abnormal amplitude two corresponding to the current individual, thereby effectively avoiding the defects of the prior art of insufficient efficiency in verifying the health information of an individual and insufficient efficiency in aggregating the address information and disease information of each individual into the personal health record.

[0007] The present invention utilizes the following technical solutions.

[0008] A health record management method for individuals throughout their life cycle, including:

[0009] The verified health information formed by verifying each individual's address information and disease information is collected into the personal health record. The personal health information is the individual's address information and disease information;

[0010] Methods for verifying each individual's address information and disease information include:

[0011] Step 1, obtain the health information of the current individual and the verified past individuals; obtain the verified past individual's physical examination information;

[0012] Step 2, based on the approximate attributes of the disease information of the current individual and the disease information of the past individuals, obtain the disease approximate parameters corresponding to the current individual and each past individual; based on the address information of the current individual and the address information of the past individuals, obtain the corresponding past individuals when the health information of the current individual is transmitted; based on the approximate attributes of the physical examination information between the past individuals, obtain the environmental concentration amplitude; based on the disease approximate parameters corresponding to the current individual and each past individual, combined with the environmental concentration amplitude, obtain the abnormal amplitude of the health information of the current individual -1;

[0013] Step 3, based on the disease approximate parameters of the current individual and each past individual, select the approximate past individuals; based on the address information, analyze the arrangement and clustering attributes of the current individual and the approximate past individuals to obtain the abnormal amplitude 2 of the health information of the current individual;

[0014] Step 4, based on the current individual's corresponding abnormal amplitude 1 and abnormal amplitude 2, the current individual's health information is efficiently verified.

[0015] Furthermore, in Step 1, the health information is the address information and disease information, and the previous personal physical examination information is obtained synchronously. The physical examination information includes the personal blood pressure, blood sugar, heart rate, electrocardiogram, urine routine, and cholesterol information.

[0016] Furthermore, in Step 2, all phrases obtained after performing NLP processing on the current individual's disease information through the bigram model are regarded as the phrase group of the current individual's disease information, and all phrases obtained after performing NLP processing on the past individual's disease information through the bigram model are regarded as the phrase group of the past individual's disease information. The Pearson correlation coefficient between the phrase group of the current individual's disease information and the phrase group of the past individual's disease information is calculated and regarded as the approximate parameter of the current individual's and the corresponding past individual's disease.

[0017] Further, in Step 2, the current individual's address information is the GPS coordinate value of the current individual's address, and the past individual's address information is the GPS coordinate value of the past individual's address; the current individual's address information is used as the midpoint, and the pre-defined reference quantity is used as the diameter, and the past individuals whose address information is covered within the scope of the midpoint and the diameter are regarded as nearby past individuals;

[0018] When the number of past individuals corresponding to the current individual is lower than a pre-defined critical number of nearby numbers, the pre-defined base number is increased by a pre-defined incremental span until the number of past individuals initially covered within the defined range is no lower than a pre-defined critical number of nearby numbers.

[0019] Furthermore, Step 2 specifically includes:

[0020] Step 2-1, obtain the physical examination information of the individuals in the past period defined in advance when the health information of the current individual is transmitted, and treat it as the physical examination information of the individuals in the past period to be analyzed; perform clustering on each physical examination information to be analyzed using the same method to obtain clustered physical examination information;

[0021] Step 2-2, according to the fluctuation approximate attributes of all the clustered physical examination information of a random pair of nearby past individuals in the same order, obtain the disease approximate parameters of the corresponding pair of nearby past individuals;

[0022] Step 2-3, obtain the environmental density amplitude based on the disease approximate parameters between all nearby past individuals; the disease approximate parameters and the environmental density amplitude are proportional.

[0023] Furthermore, in Step 2-1, the pre-defined time period uses the moment when the current individual's health information is transmitted via the health business information system as the end moment, and the time period is 180 minutes. That is, when the current individual's health information is transmitted, the physical examination information of the nearby individuals 180 minutes before the moment when the information was transmitted is used as the physical examination information to be analyzed.

[0024] Furthermore, in Step 2-2, the computational equation for constructing the disease approximate parameters is:

[0025]

[0026] Here, n and p are the order codes of the individuals in the past period, n≠p; s n,p represents the disease approximate parameters of the nth nearby past individual and the pth nearby past individual; l is the order code of the clustered physical examination information; L is the number of clustered physical examination information; e is the Euler number; e n,l represents the change amplitude of the physical examination information of the nth individual in the past period, which is the full range of the physical examination information of the cluster; e p,l Represents the change amplitude of the physical examination information of the p-th individual in the past.

[0027] Furthermore, in Step 2-3, the average of the disease approximate parameters of all past individuals near the current individual is taken as the corresponding environmental density amplitude of the current individual.

[0028] Furthermore, in Step 2, the operational equation of the abnormal amplitude 1 is:

[0029]

[0030] Here, j represents the order code of the current individual; k represents the order code of the previous individual near the current individual; Q1 j The abnormal amplitude representing the current personal health information is one; j represents the current environmental density amplitude of individual j; E j represents the number of individuals in the past period who are close to the current individual j; d jk represents the disease approximate parameter corresponding to the current individual j and the kth nearby past individual; b represents the predefined additional quantity to prevent the divisor from being zero, b = 1; Represents the use of Z-score method to Implement standardization.

[0031] Furthermore, in Step 3, when the disease approximation parameters corresponding to the past individual and the current individual are greater than a pre-defined critical value of similarity, the corresponding past individual is confirmed as an approximate past individual.

[0032] Furthermore, in Step 3, the current individual's address information is associated with the address information of individuals in the similar past periods into a Cartesian system;

[0033] In the Cartesian system, the arrangement and clustering properties of the current individual and the individuals in the similar past are analyzed to obtain the abnormal amplitude 2 of the health information of the current individual, as follows:

[0034] The GPS coordinates of the personal address information are obtained as the personal Cartesian coordinates, and the current personal address body and the personal address information of the past are associated with the Cartesian coordinates;

[0035] The address information of individuals in the Cartesian system is grouped; and the anomaly amplitude 2 is obtained according to the distance between the current address information of the individual and the centroid of the group to which the individual belongs.

[0036] Further, in Step 4, based on the approximate properties of the abnormal amplitude 1 and the abnormal amplitude 2 corresponding to the current individual, the abnormal amplitude 1 and the abnormal amplitude 2 are combined to obtain the verification accuracy of the current individual's health information;

[0037] The calculation equation for verification accuracy is:

[0038]

[0039] Here, j represents the current individual's order code; M j represents the verification accuracy of the current health information of individual j; represents the abnormal amplitude of the current health information of individual j; Q2 j represents the abnormal amplitude of the current health information of individual j; represents the average of the abnormal amplitude 1 and the abnormal amplitude 2 of the current individual health information j; b represents a predefined additional quantity that prevents the divisor from being zero, b=1.

[0040] Further, in Step 4, when the verification accuracy rate of the current individual's health information is higher than a predefined verification threshold, it is confirmed that the initial verification of the current individual's health information is correct;

[0041] When the initial verification is correct, the current individual medical record number is compared with the correct individual medical record number stored in advance to extract the different bytes, and the ratio of the different bytes to all bytes of the medical record number is obtained. When the corresponding ratio of the different bytes is lower than the pre-defined ratio threshold, it is confirmed that the final verification of the current individual health information is correct, and accordingly the current individual health information is the verified health information;

[0042] When the current personal health information of the initial verification or the final verification is not correct, the current personal health information is corrected by the query correction method to obtain the verified health information.

[0043] A health record management system for individuals throughout their life cycle, including:

[0044] A verification module is used to obtain health information of the current individual and verified past individuals; obtain physical examination information of verified past individuals;

[0045] The abnormal module is used to obtain the disease approximate parameters corresponding to the current individual and each past individual based on the approximate attributes of the disease information of the current individual and the disease information of the past individual; obtain the corresponding nearby past individuals when the health information of the current individual is transmitted based on the address information of the current individual and the address information of the past individual; obtain the environmental density amplitude based on the approximate attributes of the physical examination information between the nearby past individuals; obtain the abnormal amplitude of the health information of the current individual based on the disease approximate parameters corresponding to the current individual and each nearby past individual, combined with the environmental density amplitude;

[0046] The parsing module is used to select similar past individuals based on the disease approximate parameters corresponding to the current individual and each past individual; and to parse the arrangement and clustering attributes of the current individual and the similar past individuals based on the address information to obtain the abnormal amplitude 2 of the health information of the current individual;

[0047] The amplitude module is used to efficiently verify the health information of the current individual based on the abnormal amplitude one and the abnormal amplitude two corresponding to the current individual.

[0048] The beneficial effects of the present invention are that, compared with the prior art, the technical effects of the present invention include:

[0049] First, the health information of the current individual and the verified past individuals transmitted through the health and wellness business information system, as well as the physical examination information of the past individuals, is obtained, avoiding the complicated definition process of the verification standard, and the abnormal attributes of the health information of the current individual are analyzed based on the past information; then, the disease approximate parameters corresponding to the current individual and each past individual are obtained, representing the approximate attributes of the disease information of the current individual and the disease information of the past individual, so as to subsequently analyze the approximate attributes of the disease information of the current individual and the nearby past individuals; then, the nearby past individuals corresponding to the time when the health information of the current individual is transmitted are obtained, so as to subsequently analyze the environmental attributes around the current individual and the approximate attributes of the disease information of the current individual and the nearby past individuals; then, based on the approximate attributes of the physical examination information between the nearby past individuals, the environmental density amplitude is obtained, and the disease approximate parameters corresponding to the current individual and each nearby past individual are combined to obtain the abnormal amplitude of the health information of the current individual. Value one, concentrates on the dense attributes of the environment and the distinguishing attributes of the disease information, and more accurately infers the abnormal condition of the current individual's health information; then selects similar past individuals, and analyzes the arrangement dense attributes of the current individual and the similar past individuals based on the address information, and obtains the abnormal amplitude two of the current individual's health information. Through the arrangement dense dimensions of the current individual and the similar past individuals, a larger amount of estimation basis is obtained to improve the accuracy of efficient verification; finally, based on the corresponding abnormal amplitude one and abnormal amplitude two of the current individual, the health information of the current individual is efficiently verified. The present invention performs abnormal analysis on the health information of the current individual with the help of past information, and infers the abnormal attributes of the current individual's health information through the approximate dimensions of the disease information and the arrangement dense dimensions of the similar individuals, and efficiently verifies the health information of the current individual, reduces the complexity of information verification and improves the efficiency of verification, and also improves the efficiency of aggregating the address information and disease information of each individual into the personal health file. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a flow chart of the health record management method for an individual throughout his / her life cycle described in the present invention;

[0051] Figure 2 It is a partial structural diagram of the health record management system for an individual's entire life cycle described in the present invention. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely expressed in combination with the drawings in the embodiments of the present invention. The embodiments expressed in this application are only partial embodiments of the present invention, not all embodiments. According to the spirit of the present invention, other embodiments obtained by technicians in this field without creative work are all within the protection scope of the present invention.

[0053] like Figure 1 As shown, the present invention provides a health record management method for an individual throughout his or her life cycle, comprising:

[0054] The verified health information formed by verifying each individual's address information and disease information is collected into the personal health record. The personal health information is the personal address information and disease information. The personal disease information includes the process information of the occurrence, development, treatment and prognosis of the individual's disease;

[0055] Methods for verifying each individual's address information and disease information include:

[0056] Step 1, obtain the health information of the current individual and the verified past individuals transmitted through the health and wellness business information system; the health information includes the address information and disease information transmitted through the health and wellness business information system; obtain the verified past individual physical examination information;

[0057] In a preferred but non-restrictive embodiment of the present invention, in this application, it is necessary to avoid the complicated definition process of the verification specification, and construct an information database based on past health information that has been verified by humans, and also contain past personal physical examination information that has been verified by humans, and use the past information to perform analysis on the abnormal attributes of the current individual's health information; it involves the interval plan and environmental uniqueness, and there are similar disease conditions in the same planned interval. To use this attribute, in Step 1, the health information is the address information and disease information transmitted via the health and health business information system, and the past personal physical examination information is obtained synchronously. The physical examination information includes the individual's blood pressure, blood sugar, heart rate, electrocardiogram, urine routine, and cholesterol information, so as to provide more information analysis dimensions; so the current individual and the verified past individual's health information transmitted via the health and health business information system is initially obtained; the health information at least includes the address information and disease information transmitted via the health and health business information system; the past personal physical examination information is obtained to create better conditions for subsequent analysis.

[0058] In a preferred but non-limiting embodiment of the present invention, in Step 1, a unique identification code is also defined for each individual.

[0059] Step 2, based on the approximate attributes of the disease information of the current individual and the disease information of the past individual, obtain the disease approximate parameters corresponding to the current individual and each past individual; based on the address information of the current individual and the address information of the past individual, obtain the corresponding nearby past individuals when the health information of the current individual is transmitted by the health and wellness business information system; based on the approximate attributes of the physical examination information between the nearby past individuals, obtain the environmental density amplitude; based on the disease approximate parameters corresponding to the current individual and each nearby past individual, combined with the environmental density amplitude, obtain the abnormal amplitude of the health information of the current individual -;

[0060] The closer the disease information of the current individual is to the disease information of past individuals, the more reasonable the disease information of the current individual is, and the more compliant the disease condition reported by the current individual is. Therefore, based on the approximate attributes of the disease information of the current individual and the disease information of past individuals, the corresponding disease approximate parameters of the current individual and each past individual are obtained, which provides a basis for subsequently analyzing the approximate attributes of the disease information of the current individual and nearby past individuals and obtaining the abnormal amplitude.

[0061] However, disease information often contains huge amounts of text information, which cannot be directly recognized and processed by machines. Therefore, NLP processing must be performed on the disease information first, which can decompose continuous text into phrases with separate meanings, so as to better understand and process this information; the approximate factor between phrase groups involving a pair of disease information can simplify and accurately represent the approximate attributes between a pair of disease information, thereby obtaining the approximate factor between the phrase group of the current individual's disease information and the phrase group of the past individual's disease information, which can be used as the disease approximation parameter of the current individual and the corresponding past individual.

[0062] In a preferred but non-restrictive embodiment of the present invention, in Step 2, all phrases obtained after NLP processing of the current individual's disease information via the bigram model are regarded as the phrase group of the current individual's disease information, and all phrases obtained after NLP processing of the past individual's disease information via the bigram model are regarded as the phrase group of the past individual's disease information. The Pearson correlation coefficient of the phrase group of the current individual's disease information and the phrase group of the past individual's disease information is calculated and regarded as the approximate disease parameter of the current individual and the corresponding past individual.

[0063] In an area with a high concentration of diseases, the overlap of individual disease categories is very high (mainly due to the high synchronization of diseases caused by living habits, disease sources, and climate in the region). If the current individual's disease information is more different from the disease information of other individuals in the nearby area, it means that the probability of the individual's disease information being abnormal is not small, and the probability of the information being abnormal is not low. Synchronization also needs to involve the density of the environment (that is, the address) in the nearby area, so as to determine the amplitude of the effect of distinctiveness on the probability of information anomaly. The larger the density amplitude of the environment in the nearby area, the greater the accuracy of distinctiveness in representing the probability of information anomaly. Under normal circumstances, the more dense the environment is, the more similar the changes in personal health information in the area are. Just as the larger the density amplitude of the environment, the higher the synchronization of living habits, disease sources, and climate-induced diseases in the area, and the higher the overlap of disease categories. Therefore, based on the current individual's address information and the past individual's address information, the corresponding nearby past individuals when the current individual's health information was transmitted are obtained; based on the similar attributes of the health information between nearby past individuals, the environmental density amplitude is obtained, and the environmental density attributes and disease information differentiation attributes are concentrated in the future to more accurately infer the abnormality of the current individual's health information and lay the foundation for efficient verification.

[0064] In a preferred but non-limiting embodiment of the present invention, in Step 2, the current individual's address information is the GPS coordinate value of the current individual's address, and the past individual's address information is the GPS coordinate value of the past individual's address; the current individual's address information is used as the midpoint, and the pre-defined reference quantity is used as the diameter, and the past individuals under the address information covered within the scope of the midpoint and the diameter are regarded as nearby past individuals;

[0065] When the number of nearby past individuals involved is too low, it is not appropriate to highlight the abnormal attributes of the current individual. Therefore, when the number of nearby past individuals corresponding to the current individual is lower than the pre-defined critical number of nearby numbers, the pre-defined baseline quantity is increased with a pre-defined incremental span until the number of past individuals covered for the first time within the defined scope is not lower than the pre-defined critical number of nearby numbers.

[0066] Just as the pre-defined baseline is 200m, the pre-defined incremental span is 20m, and the pre-defined critical number of nearby persons is ten, using the current individual's address information as the midpoint and the pre-defined baseline as the diameter, gradually increase the pre-defined baseline, that is, gradually increase the diameter with the pre-defined incremental span, until the number of past individuals within the circle is more than ten for the first time; if the scope defined with the pre-defined baseline as the diameter already contains more than ten past individuals, there is no need to increase the pre-defined baseline.

[0067] In a preferred but non-limiting embodiment of the present invention, Step 2 specifically comprises:

[0068] Step 2-1, obtain the physical examination information of the individuals in the past period defined in advance when the health information of the current individual is transmitted, and treat it as the physical examination information of the individuals in the past period to be analyzed; perform clustering on each physical examination information to be analyzed using the same method to obtain clustered physical examination information;

[0069] To restrict the parsing scope of physical examination information, the physical examination information of the past individual within a pre-defined time period near when the current individual's health information is transmitted is obtained, and used as the physical examination information of the past individual to be parsed. In a preferred but non-restrictive implementation of the present invention, in Step 2-1, the pre-defined time period uses the moment when the current individual transmits the information via the health business information system as the end time, and the time period is 180 minutes. That is, when the current individual's health information is transmitted, the physical examination information of the past individual 180 minutes before the moment when the information was transmitted is obtained as the physical examination information to be parsed.

[0070] It involves clustering the physical examination information, which is more suitable for obtaining the distributed fluctuation attributes of the physical examination information and more suitable for analyzing the similar attributes between different personal physical examination information. Therefore, clustering is performed on each physical examination information to be analyzed to obtain clustered physical examination information.

[0071] Just as the time period of the physical examination information to be analyzed is 180 minutes, the time period of each cluster of physical examination information is ten minutes.

[0072] Step 2-2, according to the fluctuation approximate attributes of all the clustered physical examination information of a random pair of nearby past individuals in the same order, obtain the disease approximate parameters of the corresponding pair of nearby past individuals;

[0073] The more similar the fluctuations of clustered physical examination information of a pair of nearby past individuals in the same order are, the more similar the disease attributes of the corresponding pair of nearby past individuals are. Therefore, based on the similarity of the fluctuations of clustered physical examination information of a pair of nearby past individuals in the same order, the similarity parameters of the disease of the corresponding pair of nearby past individuals can be obtained.

[0074] Since different individuals have different physical conditions, the numerical variation range of disease information of disease conditions is different. In order to analyze the variation approximation of physical examination information, the physical examination information to be analyzed of each nearby individual in the past is standardized using the Z-score method, and the physical examination information is pre-processed; the closer the variation amplitude between clustered physical examination information is, the closer the fluctuation of clustered physical examination information is. In a preferred but non-restrictive embodiment of the present invention, in Step 2-2, the computational equation for constructing the disease approximation parameter is:

[0075]

[0076] Here, n and p are the order codes of the individuals in the past period, n≠p; s n,p represents the approximate disease parameters of the nth nearby past individual and the pth nearby past individual; l is the order code of the clustered physical examination information (the order code defined for each cluster is incremented one by one in the order of the clustering); L is the number of clustered physical examination information; e is the Euler number; e n,l represents the change amplitude of the physical examination information of the nth individual in the past period, which is the full range of the physical examination information of the cluster; e p,l Represents the change amplitude of the physical examination information of the p-th individual in the past.

[0077] In the calculation equation of the approximate parameter, L=180, the full range of the clustered physical examination information represents the variation amplitude of the clustered physical examination information, and the modulus of the quantity obtained by subtracting the variation amplitude between the clustered physical examination information represents the difference between the clustered physical examination information, and then the inverse correlation is performed to represent the fluctuation approximate property of the variation amplitude between the clustered physical examination information, |e n,l -e p,l The lower | is, the lower the difference in the amplitude of changes between clustered physical examination information is, the higher the fluctuation approximation attribute is, and the higher the disease approximation parameter is.

[0078] The clustered medical examination information is arranged according to the time when the first medical examination information in the cluster is transmitted.

[0079] Step 2-3, obtain the environmental density amplitude based on the disease approximate parameters between all nearby past individuals; the disease approximate parameters and the environmental density amplitude are proportional.

[0080] The higher the disease similarity parameter between nearby past individuals, the more similar the disease patterns of past individuals around the current individual are, and the larger the environmental density amplitude is. Therefore, the environmental density amplitude is obtained based on the disease similarity parameters between all nearby past individuals; the disease similarity parameter is directly proportional to the environmental density amplitude.

[0081] In a preferred but non-limiting implementation of the present invention, in Step 2-3, the average of the disease approximate parameters of all past individuals near the current individual is taken as the corresponding environmental density amplitude of the current individual.

[0082] After inferring the environmental density amplitude around the current individual, we can obtain the abnormal amplitude of the current individual's health information based on the corresponding approximate disease parameters of the current individual and each nearby past individual, combined with the environmental density amplitude, and concentrate on the environmental density attributes and the distinguishing attributes of the disease information to more accurately infer the abnormal situation of the current individual's health information, laying the foundation for the final efficient verification.

[0083] By comparing the current individual with a past individual, the control value shows independence. According to the corresponding disease approximate parameters of the current individual and all nearby past individuals, the abnormal amplitude of the disease information of the current individual is obtained, which improves the accuracy and reliability of the abnormal analysis of the current individual. The higher the corresponding disease approximate parameters between the current individual and nearby past individuals, the more reasonable the disease information of the current individual is. Therefore, the disease approximate parameters and the abnormal amplitude of the disease information are inversely proportional.

[0084] The larger the amplitude of the environmental density, the greater the credibility of the approximate disease parameters compared between the current individual and the nearby past individuals, the greater the credibility of the abnormal amplitude of the disease information, and the more it can reflect the abnormal condition of the current individual. Therefore, based on the abnormal amplitude of the current individual's disease information and the environmental density amplitude, the current individual's abnormal amplitude of one is obtained; the abnormal amplitude of the disease information and the environmental density amplitude are both proportional to the abnormal amplitude of one.

[0085] In a preferred but non-limiting embodiment of the present invention, in Step 2, the operation equation of the abnormal amplitude 1 is:

[0086]

[0087] Here, j represents the order code of the current individual (the order code is the order code defined for each current individual and incremented by one for each current individual); k represents the order code of the current individual's nearby past individuals (the order code is the order code defined for each current individual's nearby past individuals and incremented by one for each current individual's nearby past individuals); Q1 j The abnormal amplitude representing the current personal health information is one; j represents the current environmental density amplitude of individual j; E j represents the number of individuals in the past period who are close to the current individual j; d jk represents the disease approximate parameter corresponding to the current individual j and the kth nearby past individual; b represents the predefined additional quantity to prevent the divisor from being zero, b = 1; Represents the use of Z-score method to Implement standardization; Represents the abnormal amplitude of the current individual's disease information.

[0088] In the calculation equation of the abnormal amplitude one, the mean of the disease approximate parameters corresponding to the current individual and all nearby past individuals is represented as the reasonable amplitude of the disease information of the current individual by taking the average method, and then the inverse correlation is performed by dividing by one, and the dividend is prevented from being zero by relying on the pre-defined additional quantity, so as to obtain the abnormal amplitude of the disease information; the lower the mean of the disease approximate parameters corresponding to the current individual and all nearby past individuals, the more abnormal the disease information of the current individual is, the larger the abnormal amplitude is, and the higher the abnormal amplitude one is; the higher the synchronous environmental density amplitude is, the denser the environment around the current individual is, and the greater the credibility of the abnormal amplitude of the disease information is.

[0089] Step 3, based on the disease approximate parameters of the current individual and each past individual, select the approximate past individuals; based on the address information, analyze the arrangement and clustering attributes of the current individual and the approximate past individuals to obtain the abnormal amplitude 2 of the health information of the current individual;

[0090] Involving the clustering properties of the environment and the planned layout of each area, similar individuals are often in a dense area, such as a community, a neighborhood, an apartment, etc.; therefore, more estimation evidence can be obtained through the distribution clustering dimension of the current individual and similar past individuals to improve the accuracy of efficient verification, and thus similar past individuals are selected based on the corresponding disease parameters of the current individual and each past individual; based on the address information, the distribution clustering properties of the current individual and similar past individuals are analyzed to obtain the abnormal amplitude of the current individual's health information.

[0091] In a preferred but non-limiting embodiment of the present invention, in Step 3, the higher the disease approximation parameters corresponding to the past individual and the current individual, the more similar the past individual and the current individual are. Therefore, when the disease approximation parameters corresponding to the past individual and the current individual are greater than a pre-defined critical value of similarity, the corresponding past individual is confirmed as an approximate past individual.

[0092] In a preferred but non-limiting embodiment of the present invention, in Step 3, the current individual's address information is associated with the approximately past individual's address information in a Cartesian system;

[0093] In the Cartesian system, the arrangement and clustering properties of the current individual and the individuals in the similar past are analyzed to obtain the abnormal amplitude 2 of the health information of the current individual, as follows:

[0094] The GPS coordinates of the personal address information are obtained as the personal Cartesian coordinates, and the current personal address body and the personal address information of the past are associated with the Cartesian coordinates;

[0095] The involved grouping can efficiently and reliably identify geographically densely distributed groups of individuals. The larger the distance between the current individual and the centroid, the more isolated the current individual is and the more anomaly will occur. Therefore, the address information of individuals in the Cartesian system is grouped; the anomaly amplitude 2 is obtained based on the distance between the current individual's address information and the centroid of the group to which it belongs; the distance between the current individual's address information and the centroid of the group to which it belongs is proportional to the anomaly amplitude 2.

[0096] Just as the Clarans method is used to perform grouping, the number of groups is determined to be the same as the number of pre-defined regional divisions; the distance between the current individual's address information and the centroid of the group to which he belongs is standardized using the Z-score method and is taken as the current individual's anomaly amplitude 2, maintaining the same numerical level as anomaly amplitude 1.

[0097] In order to limit the size of the area analyzed by the Cartesian system, the current personal address is used as the midpoint, and only the approximate past individuals within 50 kilometers of the personal address are analyzed, which reduces the grouping scope and improves the efficiency of information verification.

[0098] Step 4, based on the current individual's corresponding abnormal amplitude 1 and abnormal amplitude 2, the current individual's health information is efficiently verified.

[0099] The abnormal amplitude 1 obtained in Step 2 is used to infer the abnormal properties of the current individual's health information through the approximate aspects of the disease information of the current individual and the nearby past individuals; the abnormal amplitude 2 obtained in Step 3 is used to infer the abnormal properties of the current individual's health information through the arrangement density dimension of the current individual and the similar past individuals. Therefore, the abnormal amplitude 1 and the abnormal amplitude 2 are finally aggregated, and the abnormal amplitude 1 and the abnormal amplitude 2 are used to refer to each other. According to the corresponding abnormal amplitude 1 and the abnormal amplitude 2 of the current individual, the health information of the current individual is efficiently verified.

[0100] The closer the anomaly amplitude one and anomaly amplitude two are, the more similar the anomaly attributes of the current individual's health information obtained through different dimensional analysis are, and the more accurate the anomaly inference of the current individual's health information is; the higher the synchronized anomaly amplitude one and anomaly amplitude two are, the more abnormal the health information is, and the lower the verification accuracy is; the individual's medical record number is the individual's main identification code, so the accuracy of the medical record number must be combined to obtain the verification value.

[0101] In a preferred but non-limiting embodiment of the present invention, in Step 4, based on the approximate properties of the abnormal amplitude one and the abnormal amplitude two corresponding to the current individual, the abnormal amplitude one and the abnormal amplitude two are combined to obtain the verification accuracy of the current individual's health information; the approximate properties of the abnormal amplitude one and the abnormal amplitude two are proportional to the verification accuracy; the abnormal amplitude one and the abnormal amplitude two are both inversely proportional to the verification accuracy.

[0102] The health information also includes the individual's medical record number; the verification value is obtained based on the current verification accuracy of the individual's health information and the accuracy of the medical record number.

[0103] The calculation equation for verification accuracy is:

[0104]

[0105] Here, j represents the current individual's order code; M j represents the verification accuracy of the current health information of individual j; represents the abnormal amplitude of the current health information of individual j; Q2 j represents the abnormal amplitude of the current health information of individual j; represents the average of the abnormal amplitude 1 and the abnormal amplitude 2 of the current individual health information j; b represents a predefined additional quantity that prevents the divisor from being zero, b=1.

[0106] In the calculation equation for verifying the accuracy, the modulus method of the quantity obtained by subtracting the abnormal amplitude 1 from the abnormal amplitude 2 is used to characterize the different attributes of the abnormal amplitude 1 and the abnormal amplitude 2. Then, the inverse correlation is performed by dividing by 1 to represent the approximate attributes of the abnormal amplitude 1 and the abnormal amplitude 2. The closer the abnormal amplitude 1 and the abnormal amplitude 2 are, the more similar the abnormal attributes of the current personal health information obtained through different dimensional analysis are. The greater the availability; the abnormal amplitude 1 and the abnormal amplitude 2 are represented by the mean. The lower the abnormal amplitude 1 and the abnormal amplitude 2, the lower the corresponding mean, the lower the abnormal amplitude of personal health information, and the synchronized |Q1 j -Q2 j The lower it is, the more similar the abnormal attributes of the current personal health information obtained through analysis of different dimensions are, and the higher the verification accuracy is.

[0107] In a preferred but non-limiting embodiment of the present invention, in Step 4, the higher the verification accuracy, the more reasonable the current personal health information is, and the lower the probability of abnormality. Therefore, when the verification accuracy of the current personal health information is higher than the pre-defined verification threshold, it is confirmed that the initial verification of the current personal health information is correct;

[0108] The lower the amount of different bytes in the medical record number of the current individual, the more accurate the medical record number is. Therefore, when the initial verification is correct, the different bytes are extracted by comparing the current individual medical record number with the correct medical record number of the individual stored in advance, and the ratio of the different bytes to all bytes of the medical record number is obtained. When the corresponding ratio of the different bytes is lower than the pre-defined ratio threshold, it is confirmed that the final verification of the health information of the current individual is correct, and the health information of the current individual is the verified health information.

[0109] When the current personal health information of the initial verification or the final verification is not correct, the current personal health information is corrected by the query correction method to obtain the verified health information.

[0110] The pre-defined verification threshold may be seventy-nine percent; the pre-defined ratio threshold may be four ten-thousandths.

[0111] like Figure 2 As shown, the health record management system for an individual's entire life cycle described in the present invention includes:

[0112] A verification module is used to obtain health information of the current individual and verified past individuals; obtain physical examination information of verified past individuals;

[0113] The abnormal module is used to obtain the disease approximate parameters corresponding to the current individual and each past individual based on the approximate attributes of the disease information of the current individual and the disease information of the past individual; obtain the corresponding nearby past individuals when the health information of the current individual is transmitted based on the address information of the current individual and the address information of the past individual; obtain the environmental density amplitude based on the approximate attributes of the physical examination information between the nearby past individuals; obtain the abnormal amplitude of the health information of the current individual based on the disease approximate parameters corresponding to the current individual and each nearby past individual, combined with the environmental density amplitude;

[0114] The parsing module is used to select similar past individuals based on the disease approximate parameters corresponding to the current individual and each past individual; and to parse the arrangement and clustering attributes of the current individual and the similar past individuals based on the address information to obtain the abnormal amplitude 2 of the health information of the current individual;

[0115] The amplitude module is used to efficiently verify the health information of the current individual based on the abnormal amplitude one and the abnormal amplitude two corresponding to the current individual.

[0116] The beneficial effects of the present invention are that, compared with the prior art, the technical effects of the present invention include:

[0117] First, the health information of the current individual and the verified past individuals transmitted through the health and wellness business information system, as well as the physical examination information of the past individuals, is obtained, avoiding the complicated definition process of the verification standard, and the abnormal attributes of the health information of the current individual are analyzed based on the past information; then, the disease approximate parameters corresponding to the current individual and each past individual are obtained, representing the approximate attributes of the disease information of the current individual and the disease information of the past individual, so as to subsequently analyze the approximate attributes of the disease information of the current individual and the nearby past individuals; then, the nearby past individuals corresponding to the time when the health information of the current individual is transmitted are obtained, so as to subsequently analyze the environmental attributes around the current individual and the approximate attributes of the disease information of the current individual and the nearby past individuals; then, based on the approximate attributes of the physical examination information between the nearby past individuals, the environmental density amplitude is obtained, and the disease approximate parameters corresponding to the current individual and each nearby past individual are combined to obtain the abnormal amplitude of the health information of the current individual. Value one, concentrates on the dense attributes of the environment and the distinguishing attributes of the disease information, and more accurately infers the abnormal condition of the current individual's health information; then selects similar past individuals, and analyzes the arrangement dense attributes of the current individual and the similar past individuals based on the address information, and obtains the abnormal amplitude two of the current individual's health information. Through the arrangement dense dimensions of the current individual and the similar past individuals, a larger amount of estimation basis is obtained to improve the accuracy of efficient verification; finally, based on the corresponding abnormal amplitude one and abnormal amplitude two of the current individual, the health information of the current individual is efficiently verified. The present invention performs abnormal analysis on the health information of the current individual with the help of past information, and infers the abnormal attributes of the current individual's health information through the approximate dimensions of the disease information and the arrangement dense dimensions of the similar individuals, and efficiently verifies the health information of the current individual, reduces the complexity of information verification and improves the efficiency of verification, and also improves the efficiency of aggregating the address information and disease information of each individual into the personal health file.

[0118] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not deviate from the spirit and scope of the present invention should be covered within the protection space of the claims of the present invention.

Claims

1. A health record management method for an individual throughout his or her life cycle, characterized in that: include: The verified health information formed by verifying each individual's address information and disease information is collected into the personal health record. The personal health information is the individual's address information and disease information; Methods for verifying each individual's address information and disease information include: Step 1, obtain the health information of the current individual and the verified past individuals; obtain the verified past individual's physical examination information; Step 2, based on the approximate attributes of the disease information of the current individual and the disease information of the past individuals, obtain the disease approximate parameters corresponding to the current individual and each past individual; based on the address information of the current individual and the address information of the past individuals, obtain the corresponding past individuals when the health information of the current individual is transmitted; based on the approximate attributes of the physical examination information between the past individuals, obtain the environmental concentration amplitude; based on the disease approximate parameters corresponding to the current individual and each past individual, combined with the environmental concentration amplitude, obtain the abnormal amplitude of the health information of the current individual -1; Step 3, based on the disease approximate parameters of the current individual and each past individual, select the approximate past individuals; based on the address information, analyze the arrangement and clustering attributes of the current individual and the approximate past individuals to obtain the abnormal amplitude 2 of the health information of the current individual; Step 4, based on the current individual's corresponding abnormal amplitude 1 and abnormal amplitude 2, the current individual's health information is efficiently verified.

2. The method for managing health records for an individual throughout his or her life cycle according to claim 1, characterized in that: In Step 1, the health information is the address information and disease information, and the previous personal physical examination information is obtained synchronously. The physical examination information includes the personal blood pressure, blood sugar, heart rate, electrocardiogram, urine routine, and cholesterol information.

3. The method for managing health records for an individual throughout his or her life cycle according to claim 2, characterized in that: In Step 2, all phrases obtained after NLP processing of the disease information of the current individual through the bigram model are regarded as the phrase group of the disease information of the current individual, and all phrases obtained after NLP processing of the disease information of the past individual through the bigram model are regarded as the phrase group of the disease information of the past individual, and the Pearson correlation coefficient of the phrase group of the disease information of the current individual and the phrase group of the disease information of the past individual is calculated as the disease approximation parameter of the current individual and the corresponding past individual; In Step 2, the current individual's address information is the GPS coordinate value of the current individual's address, and the past individual's address information is the GPS coordinate value of the past individual's address; the current individual's address information is used as the midpoint, and the pre-defined reference quantity is used as the diameter, and the past individuals whose address information is covered within the scope of the midpoint and the diameter are regarded as nearby past individuals; When the number of past individuals corresponding to the current individual is lower than a pre-defined critical number of nearby numbers, the pre-defined base number is increased by a pre-defined incremental span until the number of past individuals initially covered within the defined range is no lower than a pre-defined critical number of nearby numbers.

4. The method for managing health records for an individual throughout his or her life cycle according to claim 3, characterized in that: Step 2 specifically includes: Step 2-1, obtain the physical examination information of the individuals in the past period defined in advance when the health information of the current individual is transmitted, and treat it as the physical examination information of the individuals in the past period to be analyzed; perform clustering on each physical examination information to be analyzed using the same method to obtain clustered physical examination information; Step 2-2, according to the fluctuation approximate attributes of all clustered physical examination information of a random pair of nearby past individuals in the same order, obtain the disease approximate parameters of the corresponding pair of nearby past individuals; Step 2-3, obtain the environmental density amplitude based on the disease approximate parameters between all nearby past individuals; the disease approximate parameters and the environmental density amplitude are proportional.

5. The method for managing health records for an individual throughout his or her life cycle according to claim 4, characterized in that: In Step 2-1, the pre-defined time period uses the time when the current individual is transmitted through the health service information system as the end time, and the time period is 180 minutes, that is, when the current individual's health information is transmitted, the physical examination information of the past individual 180 minutes before the time when the information is transmitted is used as the physical examination information to be analyzed; In Step 2-2, the computational equation for constructing the approximate disease parameters is: Here, n and p are the order codes of the individuals in the past period, n≠p; s n,p represents the disease approximate parameters of the nth nearby past individual and the pth nearby past individual; l is the order code of the clustered physical examination information; L is the number of clustered physical examination information; e is the Euler number; e n,l represents the change amplitude of the physical examination information of the nth individual in the past period, which is the full range of the physical examination information of the cluster; e p,l Represents the change amplitude of the physical examination information of the pth individual in the past period of the l-cluster; In Step 2-3, the average of the disease approximate parameters of all past individuals near the current individual is taken as the corresponding environmental density amplitude of the current individual.

6. The method for managing health records for an individual throughout his or her life cycle according to claim 5, characterized in that: In Step 2, the operational equation of the abnormal amplitude 1 is: Here, j represents the order code of the current individual; k represents the order code of the previous individual near the current individual; Q1 j The abnormal amplitude representing the current personal health information is one; j represents the current environmental density amplitude of individual j; E j represents the number of individuals in the past period who are close to the current individual j; d jk represents the disease approximate parameter corresponding to the current individual j and the kth nearby past individual; b represents the predefined additional quantity to prevent the divisor from being zero, b = 1; Represents the use of Z-score method to Implement standardization.

7. The method for managing health records for an individual throughout his or her life cycle according to claim 6, characterized in that: In Step 3, when the disease approximation parameters of the past individual and the current individual are greater than a pre-defined critical value of similarity, the corresponding past individual is confirmed as an approximate past individual.

8. The method for managing health records for an individual throughout his or her life cycle according to claim 7, characterized in that: In Step 3, the current individual's address information is associated with the address information of individuals in the past in a Cartesian system; In the Cartesian system, the arrangement and clustering properties of the current individual and the individuals in the similar past are analyzed to obtain the abnormal amplitude 2 of the health information of the current individual, as follows: The GPS coordinates of the personal address information are obtained as the personal Cartesian coordinates, and the current personal address body and the personal address information of the past are associated with the Cartesian coordinates; The address information of individuals in the Cartesian system is grouped; and the anomaly amplitude 2 is obtained according to the distance between the current address information of the individual and the centroid of the group to which the individual belongs.

9. The method for managing health records for an individual throughout his or her life cycle according to claim 8, characterized in that: In Step 4, based on the approximate properties of the abnormal amplitude 1 and the abnormal amplitude 2 corresponding to the current individual, the abnormal amplitude 1 and the abnormal amplitude 2 are combined to obtain the verification accuracy of the current individual's health information; The calculation equation for verification accuracy is: Here, j represents the current individual's order code; M j represents the verification accuracy rate of the current health information of individual j; represents the abnormal amplitude of the current health information of individual j; Q2 j represents the abnormal amplitude 2 of the current health information of individual j; represents the average of the abnormal amplitude 1 and the abnormal amplitude 2 of the current individual health information j; b represents the predefined additional amount to prevent the dividend from being zero; In Step 4, when the verification accuracy rate of the current individual's health information is higher than the pre-defined verification threshold, it is confirmed that the initial verification of the current individual's health information is correct; When the initial verification is correct, the current individual medical record number is compared with the correct individual medical record number stored in advance to extract the different bytes, and the ratio of the different bytes to all bytes of the medical record number is obtained. When the corresponding ratio of the different bytes is lower than the pre-defined ratio threshold, it is confirmed that the final verification of the current individual health information is correct, and accordingly the current individual health information is the verified health information; When the current personal health information of the initial verification or the final verification is not correct, the current personal health information is corrected by the query correction method to obtain the verified health information.

10. A health record management system for an individual's entire life cycle, characterized in that: include: A verification module is used to obtain health information of the current individual and verified past individuals; obtain physical examination information of verified past individuals; The abnormal module is used to obtain the disease approximate parameters corresponding to the current individual and each past individual based on the approximate attributes of the disease information of the current individual and the disease information of the past individual; obtain the corresponding nearby past individuals when the health information of the current individual is transmitted based on the address information of the current individual and the address information of the past individual; obtain the environmental density amplitude based on the approximate attributes of the physical examination information between the nearby past individuals; obtain the abnormal amplitude of the health information of the current individual based on the disease approximate parameters corresponding to the current individual and each nearby past individual, combined with the environmental density amplitude; The parsing module is used to select similar past individuals based on the disease approximate parameters corresponding to the current individual and each past individual; and to parse the arrangement and clustering attributes of the current individual and the similar past individuals based on the address information to obtain the abnormal amplitude 2 of the health information of the current individual; The amplitude module is used to efficiently verify the health information of the current individual based on the abnormal amplitude one and the abnormal amplitude two corresponding to the current individual.

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