Medical health information closed-loop management method and system

Abstract

The invention discloses a medical health information closed-loop management method and system, and relates to the technical field of medical information management, and the system comprises an identification detection module, a folding storage module, and a hidden calling module. And when the type of the verified medical health information is judged, a judgment weight parameter # imgabs0 # in a data modal judgment formula needs to be dynamically updated according to a judgment updating formula, so that the accuracy of judging the correct medical health information is enhanced. And the hidden calling module obtains a calling parameter # imgabs2 # through the modification rate # imgabs1 #, and realizes random confusion calling of data in the first storage library, the second storage library and the standby storage library according to the calling parameter # imgabs3 # and a virtual calling formula. When the medical health information is recognized, the recognized medical health information is input into the folding storage module, the folding storage module stores text type medical health information in a first storage library, and the folding storage module encrypts numerical type medical health information through a sum encryption formula and then stores the encrypted numerical type medical health information in a second storage library.

Description

Technical Field

[0001] The present invention relates to the technical field of medical information management, and in particular to a medical health information closed-loop management method and system. Background Art

[0002] In the process of medical and health information management, the corresponding data is often recorded incorrectly due to mistakes made by the recorders; if the wrong data is entered and saved in the repository, it will lead to subsequent medical disputes. At the same time, it is also more troublesome to modify the data in the repository later. There is also the risk of medical and health data leakage and malicious tampering; therefore, medical and health information management is required to detect errors in a timely manner during the identification and entry process.

[0003] In the existing technology, the recording and management of medical and health information generally rely mainly on manual work, which leads to the following problems in the existing medical and health information management methods: (1) All recorded information needs to be manually reviewed and proofread. This review and proofreading method is time-consuming and laborious. Especially after working for a long time, the staff is easily fatigued and cannot do careful verification, which greatly reduces the accuracy of the verification; (2) The recorded data is easy to be cracked by people with ulterior motives. At the same time, the existing medical and health information management method cannot effectively encrypt the stored information. At the same time, the encrypted data will become more bloated, which will cause the storage space to be consumed too quickly; (3) In the process of data retrieval and output, it is easy to be intercepted by hackers. At the same time, the existing protection method may cause the encrypted data to become larger, making the transmission speed slower. (4) The information positions between multiple batches of medical and health information may be different. The existing medical and health information management method adopts a fixed conversion method, which may lead to errors in the classification of medical and health information. At the same time, the existing recognition and classification method requires characters to be recognized one by one, and the recognition efficiency is low. Summary of the invention

[0004] The purpose of the present invention is to provide a closed-loop management method and system for medical health information, aiming to solve technical problems existing in the prior art, such as how to quickly find erroneous data, how to quickly identify and classify medical health information, how to encrypt medical health information while saving storage space, and how to achieve fast and convenient encryption when retrieving data.

[0005] In view of the above technical problems, the technical solution adopted by the present invention is: a medical health information closed-loop management method, comprising the following steps: Step S1: Input the original medical and health information form of each medical institution into the information input module of the recognition and detection module, and then the information input module transmits the original medical and health information form to the enhanced recognition module. The enhanced recognition module determines the original medical and health information through the data mode determination formula, and divides the original medical and health information into numerical type and text type. For the original medical and health information that cannot be determined, it will be manually verified, and the wrong medical and health information will be corrected to obtain the verified medical and health information; Step S2: When judging the type of the verified medical and health information, the judgment weight parameter in the data mode judgment formula It needs to be dynamically updated according to the judgment update formula to enhance the accuracy of judging the correct medical and health information; Step S3: Then, the enhanced recognition module further classifies the numerical medical and health information through a numerical classification formula, and classifies the numerical medical and health information into patient age information, patient height information, patient weight information, and medical card number information; Step S4: Then, the enhanced recognition module further classifies the characters in the text-based medical and health information, and classifies the text-based medical and health information into patient name information, patient disease information, patient place of origin information, and patient gender information; Step S5: The identified medical health information is input into the folding storage module, which stores the text-type medical health information in the first storage library. The folding storage module also encrypts the numerical medical health information through a sum encryption formula and then stores it in the second storage library. Step S6: After step 1 is completed, data that is manually verified and indeed contains errors will be marked as error data, and the folding storage module will orderly combine multiple different types of error data into false medical and health information, and then the folding storage module will store the false medical and health information in the backup storage library; Step S7: After step 1 is completed, the enhanced recognition module will first calculate a column of data Modification rate , and then the modification rate calculated this time is Change rate compared to the past Compare and check whether there are any abnormal manual modifications to the bill, and then hide the modification rate of the module. Get the call parameters , and then according to the call parameters And a virtual call formula is used to realize random confusion call of data in the first repository, the second repository and the backup repository.

[0006] Preferably, after the original medical and health information table is transmitted to the enhanced recognition module, the enhanced recognition module determines the original medical and health information through the data mode determination formula, and divides the original medical and health information into numerical type and text type; assuming that a column of data in the original medical and health information is ,in, represents the i-th column in the original medical and health information, =1,2,3,4,5,6,7,8, Represents a column of data The specific data of row L in , , Represents a set of positive integers; the data mode determination formula for a column of data is as follows: ; Where: Represents a column of data is the probability of numerical medical and health information; Represents the judgment weight parameter; Represents a column of data The Lth row of ; It also indicates a column of data The total number of rows; Represents a digital judgment function, when When the first character in is an Arabic numeral, =1, when When the first character in is not an Arabic numeral, =0; Represents the Chinese-English judgment function, when When the first character in is a Chinese character or English, =1, when When the first character in is not a Chinese character or English, =0; represents an infinite coefficient; express The decimal Unicode code point corresponding to the first character in; Indicates the floor symbol; represents a natural constant; Indicates the number of selections, when =0, Express Whether it is an uppercase letter is judged. =1, Express Determine whether it is a lowercase letter; In order to enhance the accuracy of judging medical and health information, the judgment weight parameter in the data modality judgment formula It needs to be dynamically updated according to the determination update formula; the determination update formula is as follows: ; In the formula: min means taking the minimum value in a set of values; Represents the probability correction coefficient. When the number of columns that have been determined to be text-type medical and health information is equal to the number of columns that have been determined to be numerical medical and health information, =1, when the number of columns that have been determined to be text-type medical and health information is not equal to the number of columns that have been determined to be numerical-type medical and health information, =1.2.

[0007] Preferably, when ≥0.65, the corresponding column of data It is numerical medical and health information; ≤0.4, the corresponding column of data For text-based medical and health information; when 0.4< <0.65, the corresponding column of data Manual verification is required; if a column of data If it is judged to be numerical medical and health information, then middle =0 corresponds to Manual verification is required; if a column of data If it is judged to be text-based medical and health information, then middle =0 corresponds to Manual verification is required; after the manual verification is completed, the verified medical health information will be re-entered into the information input module.

[0008] Preferably, the enhanced recognition module then further classifies the numerical medical and health information through a numerical classification formula, and classifies the numerical medical and health information into patient age information, patient height information, patient weight information, and medical card number information; the numerical classification formula is as follows: ; Where: represents the classification score; Indicates the preset threshold; Indicates the first Column; when =1, represents the preset threshold of age, =47; when =2, represents the preset threshold value of height, =160, where height is in centimeters; =3, Indicates the preset threshold value of body weight, =55, where weight is in kilograms; =4, Indicates the preset threshold of the medical card number. =10 9 ;make Equal to 1, 2, 3, 4 respectively, and get four classification scores ;when =2, and the classification score is is the lowest, then the corresponding column of data is the patient's height information; =4, and the classification score is is the lowest, then the corresponding column of data For medical card number information; =1, and the classification score is is the lowest, then the corresponding column of data is the patient's age information or the patient's weight information; =3, and the classification score is is the lowest, then the corresponding column of data It is the patient's age information or the patient's weight information; after judgment, a column of data can be accurately determined Is it the patient's weight information or the medical card number information, and then compare the sum of the remaining two columns of data Size, sum The largest column of data For patient weight information, total The smallest column of data Patient age information.

[0009] Preferably, when the enhanced recognition module reclassifies the characters in the text-based medical and health information, a column of data When the probability of the place name keyword text appearing is greater than 70%, the corresponding column of data For the patient's place of origin information; when a column of data When the probability of occurrence of the disease keyword text is greater than 70%, the corresponding column of data For patient disease information; when a column of data When the probability of the gender keyword is greater than 80%, the corresponding column of data is the patient's gender information; after determining the patient's gender information, disease information, and place of origin information, the remaining column The patient's name information.

[0010] Preferably, the folding storage module encrypts the numerical medical and health information through a sum encryption formula to obtain a numerical encryption result. and the numerical encryption key , and then the folding storage module will encrypt the value result and the numerical encryption key Stored in the first database; the sum encryption formula is as follows: ; Then fold the storage module to Add 0 in front of Fill it up to five digits to get the first temporary key of five digits , and then all Arrange them in order to form a string, and then use the first 20 digits of the string as the second temporary key , followed by the first temporary key and the second temporary key Through mathematical operations, the numerical encryption key is obtained by adding : ; When the hidden retrieval module retrieves the numerical medical and health information, the hidden retrieval module uses the numerical decryption formula to encrypt the numerical result. Decrypt to get the original data ; The numerical decryption formula is as follows: ; Where: Indicates the floor symbol; mod indicates the modulo operation.

[0011] Preferably, the folding storage module converts the Chinese characters in the patient's gender information into Arabic numerals, with male being 1 and female being 0; then the folding storage module stores the converted patient gender information and patient name information, patient disease information, and patient place of origin information in the second database.

[0012] Preferably, the enhanced recognition module first calculates a column of data Modification rate , The value range is 0 to 1, and then the modification rate calculated this time is Change rate compared to the past Compare and check whether there is any abnormal manual modification in the bill; the information input module selects the corresponding data to be retrieved, and the hidden retrieval module retrieves the corresponding data from the first storage library and the second storage library according to the selection in the information input module, and then the hidden retrieval module retrieves the corresponding data according to the modification rate. Get the call parameters , and then according to the call parameters And a virtual retrieval formula, randomly retrieve a corresponding column of data from the first repository, the second repository, and the backup repository ; Virtual call formula and call parameters The calculation formula is as follows: ; Where: Indicates the virtual call coefficient, when When the number is an odd number, the hidden retrieval module randomly retrieves a corresponding column of data from the first storage library and the second storage library. ,when When it is an even number, the hidden call module randomly calls a corresponding column of data from the backup storage library ; max means taking the maximum value in a set of values.

[0013] The present invention also provides a closed-loop management system for medical health information, including an identification and detection module, a folding storage module, and a hidden retrieval module; the folding storage module is unidirectionally connected to the identification and detection module; the hidden retrieval module is unidirectionally connected to the folding storage module; the hidden retrieval module is also bidirectionally connected to the identification and detection module; the identification and detection module includes an information input module, an enhanced identification module, and an information output module; the enhanced identification module is bidirectionally connected to the information input module; the folding storage module is unidirectionally connected to the enhanced identification module; the information output module is unidirectionally connected to the enhanced identification module; the hidden retrieval module is unidirectionally connected to the information output module; the hidden retrieval module is also unidirectionally connected to the enhanced identification module.

[0014] Preferably, the folding storage module includes a first repository, a second repository, and a backup repository; the first repository is used to store text-type medical and health information; the second repository is used to store numerical medical and health information; and the backup repository is used to store false medical and health information.

[0015] Compared with the prior art, the present invention has the following beneficial effects: (1) The enhanced recognition module determines the original medical and health information through the data mode determination formula, and divides the original medical and health information into numerical type and text type. For the original medical and health information that cannot be determined, it will be manually verified, and the wrong medical and health information will be corrected to obtain the verified medical and health information. (2) When determining the type of the verified medical and health information, the determination weight parameter in the data mode determination formula It is necessary to dynamically update according to the judgment update formula to enhance the accuracy of judging the correct medical and health information. (3) The modification rate of the hidden retrieval module Get the call parameters , and then according to the call parameters and a virtual retrieval formula to realize random confusion retrieval of data in the first repository, the second repository, and the backup repository. (4) The identified medical health information is input into the folding storage module, which stores the text-type medical health information in the first repository. The folding storage module also encrypts the numerical medical health information through the sum encryption formula and then stores it in the second repository. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a flow chart of the closed-loop management method for medical health information of the present invention.

[0017] Figure 2 This is a schematic diagram of the architecture of the medical health information closed-loop management system of the present invention.

[0018] Figure 3 This is a structural principle diagram of the identification detection module and the folding storage module of the present invention.

[0019] In the figure: 1- identification and detection module; 2- folding storage module; 3- hidden retrieval module; 11- information input module; 12- enhanced identification module; 13- information output module; 21- first storage library; 22- second storage library; 23- spare storage library. DETAILED DESCRIPTION

[0020] The technical solution of the present invention is further described below with reference to the accompanying drawings and through specific implementation methods.

[0021] Among them, the drawings are only used for illustrative explanations, and they only represent schematic diagrams rather than actual pictures, and should not be understood as limitations on the present invention. In order to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of actual products. For those skilled in the art, it is understandable that some well-known structures and their descriptions in the drawings may be omitted.

[0022] Figures 1 to 3 It is a preferred embodiment of the present invention.

[0023] Figure 1 A flowchart of the closed-loop management method for medical health information of the present invention is given, which includes the following steps: Step S1: Input the original medical health information form of each medical institution into the information input module 11 of the identification and detection module 1, and then the information input module 11 transmits the original medical health information form to the enhanced identification module 12. The enhanced identification module 12 determines the original medical health information through the data mode determination formula, and divides the original medical health information into numerical type and text type. For the original medical health information that cannot be determined, it will be manually verified, and the wrong medical health information will be corrected to obtain the verified medical health information; Step S2: When judging the type of the verified medical and health information, the judgment weight parameter in the data mode judgment formula It needs to be dynamically updated according to the judgment update formula to enhance the accuracy of judging the correct medical and health information; Step S3: Then, the enhanced recognition module 12 further classifies the numerical medical and health information through a numerical classification formula, and classifies the numerical medical and health information into patient age information, patient height information, patient weight information, and medical card number information; Step S4: Then the enhanced recognition module 12 further classifies the characters in the text-based medical and health information, and classifies the text-based medical and health information into patient name information, patient disease information, patient place of origin information, and patient gender information; Step S5: The identified medical health information is input into the folding storage module 2, and the folding storage module 2 stores the text-type medical health information in the first storage library 21. The folding storage module 2 also encrypts the numerical medical health information through a sum encryption formula and then stores it in the second storage library 22; Step S6: After step 1 is completed, the data that is manually verified and indeed contains errors will be marked as error data, and the folding storage module 2 will orderly combine multiple different types of error data into false medical and health information, and then the folding storage module 2 will store the false medical and health information in the backup storage library 23; Step S7: After step 1 is completed, the enhanced recognition module 12 will first calculate a row of data Modification rate , and then the modification rate calculated this time is Change rate compared to the past Compare and check whether there are any abnormal manual modifications to the bill, and then retrieve the modification rate of module 3 Get the call parameters , and then according to the call parameters And a virtual call formula is used to realize random confusion call of data in the first repository 21, the second repository 22 and the backup repository 23.

[0024] The working principle of the present invention is as follows: first, the original medical health information form of each medical institution is input into the information input module 11 of the identification detection module 1, and then the information input module 11 transmits the original medical health information form to the enhanced identification module 12. The enhanced identification module 12 determines the original medical health information through the data mode determination formula, and divides the original medical health information into numerical type and text type. For the original medical health information that cannot be determined, it will be manually verified, and the erroneous medical health information will be corrected to obtain the verified medical health information; when the type of the verified medical health information is determined, the determination weight parameter in the data mode determination formula It needs to be dynamically updated according to the judgment update formula to enhance the accuracy of judging the correct medical and health information; then the enhanced recognition module 12 re-identifies and classifies the numerical medical and health information through the numerical classification formula, and divides the numerical medical and health information into patient age information, patient height information, patient weight information, and medical card number information; then the enhanced recognition module 12 re-identifies and classifies the characters in the text-type medical and health information, and divides the text-type medical and health information into patient name information, patient disease information, patient place of origin information, and patient gender information; the identified medical and health information will be input into the folding storage module 2, and the folding storage module 2 will store the text-type medical and health information in the first storage library 21. The folding storage module 2 also encrypts the numerical medical and health information through the sum encryption formula and then stores it in the second storage library 22; data that is manually checked and does have errors will be marked as error data, and the folding storage module 2 will orderly combine multiple different types of error data into false medical and health information, and then the folding storage module 2 will store the false medical and health information in the spare storage library 23; the enhanced recognition module 12 will calculate a column of data Modification rate , and then the modification rate calculated this time is Change rate compared to the past Compare and check whether there are any abnormal manual modifications to the bill, and then retrieve the modification rate of module 3 Get the call parameters , and then according to the call parameters And a virtual call formula is used to realize random confusion call of data in the first repository 21, the second repository 22 and the backup repository 23.

[0025] Furthermore, after the original medical health information table is transmitted to the enhanced recognition module 12, the enhanced recognition module 12 determines the original medical health information through the data mode determination formula, and divides the original medical health information into numerical type and text type; suppose a column of data in the original medical health information is ,in, represents the i-th column in the original medical and health information, =1,2,3,4,5,6,7,8, Represents a column of data The specific data of row L in , , represents the set of positive integers; represents the set of positive integers; The characters in are mainly divided into Chinese character text, Arabic numeral character text, uppercase English alphabet character text, and lowercase English alphabet character text. The decimal Unicode code value range of Chinese character text is 19968 to 40959, the decimal Unicode code value range of Arabic numeral character text (0-9) is 48 to 57; the decimal Unicode code value range of uppercase English alphabet character text (AZ) is 65 to 90; the decimal Unicode code value range of lowercase English alphabet character text (az) is 97 to 122; therefore, There are four ranges of decimal Unicode code values ​​corresponding to a single character, which are greater than 19967 and less than 40960, greater than 47 and less than 58, greater than 64 and less than 91, and greater than 96 and less than 123. The data mode determination formula is as follows: ; Where: Represents a column of data is the probability of numerical medical and health information; represents the judgment weight parameter; Represents a column of data The Lth row of ; It also represents a column of data The total number of rows; Represents a digital judgment function, when When the first character in is an Arabic numeral, =1, when When the first character in is not an Arabic numeral, =0; Represents the Chinese-English judgment function, when When the first character in is a Chinese character or English, =1, when When the first character in is not a Chinese character or English, =0; represents an infinite coefficient; express The decimal Unicode code point corresponding to the first character in; Indicates the floor symbol; represents a natural constant; Indicates the number of selections, when =0, Express Whether it is an uppercase letter is judged. =1, Express Determine whether it is a lowercase letter; The existing medical and health information judgment method requires each character to be judged one by one, which cannot further improve the judgment efficiency; in the data modality judgment formula, in order to judge a column of data more quickly and efficiently Whether it is a numeric type or a text type, you need to first and right Determine the type of the first character in; and This is set up so that middle When the value of is greater than 47 and less than 58, The values ​​of and After the middle, =1, =0, thus judging In For Arabic numerals, is numerical data; at the same time middle When the value of is greater than 19967 and less than 40960, The value of After the middle, =0, =1, thus judging is a Chinese character, For text data; when middle When the value of is between greater than 64 and less than 91 or between greater than 96 and less than 123, The value of After the middle, =0, =1, thus judging is an uppercase English alphabetic character or a lowercase English alphabetic character, is text data; then the data mode determination formula is based on the numerical type Number and text type , find the number of columns of data Medium value The proportion of and text type The proportion of , then according to a column of data Medium value The proportion of , Text The proportion of And determine the weight parameters Find a column of data The probability of numerical medical and health information , so as to realize a column of data Fast and efficient identification; at the same time, when a column of data Medium value The proportion of Between 0 and 1, considering the text type The proportion of The size of Therefore, it is necessary to introduce the judgment weight parameter , making the probability Both numerical The proportion of The influence of text type The proportion of The impact of In the data mode determination formula for a column of data When making a judgment, first assume that the judgment weight parameter The base value is 0.7, and the numerical The proportion of It also affects the judgment weight parameter The value of The proportion of When the value of a column is larger, Chinese font The probability of an accidental error is greater, so when calculating the probability When the corresponding judgment weight parameter The larger the text type, the smaller the The proportion of The influence of The corresponding influence items are added after the basic value of Follow the numerical type The proportion of The increase of increases, so as to obtain the judgment update formula, the judgment weight parameter Perform dynamic updates; the update determination formula is as follows: ; In the formula: min means taking the minimum value in a set of values; Represents the probability correction coefficient. When the number of columns that have been determined to be text-type medical and health information is equal to the number of columns that have been determined to be numerical medical and health information, =1, when the number of columns that have been determined to be text-type medical and health information is not equal to the number of columns that have been determined to be numerical-type medical and health information, =1.2; In the judgment update formula, when a column of data Medium value The proportion of When it is 1, a column of data can be determined The probability of numerical medical and health information is 1, when a column of data Medium value The proportion of When it is 0, a column of data can be determined The probability of numerical medical and health information is 0; therefore, the numeric The proportion of When it is 1, it is necessary to ensure that the weight parameter Also 1, numeric type The proportion of When it is 0, it is necessary to ensure that the weight parameter is also 0, thus determining the influencing term The coefficient of is 0.3; the influence term The min in the formula is to ensure that the weight parameter is determined The final value range of is between greater than or equal to 0.7 and less than or equal to 1; To enhance the numerical The proportion of The weight parameter set due to the influence of.

[0026] Furthermore, when ≥0.65, the corresponding column of data It is numerical medical and health information; When ≤0.4, the corresponding column of data For text-based medical and health information; when 0.4< <0.65, the corresponding column of data Manual verification is required; if a column of data If it is judged to be numerical medical and health information, then middle =0 corresponds to Manual verification is required; if a column of data If it is judged to be text-based medical and health information, then middle =0 corresponds to Manual verification is required; after the manual verification is completed, the verified medical health information will be manually re-entered into the information input module 11.

[0027] Further, the enhanced recognition module 12 then further classifies the numerical medical and health information through a numerical classification formula, and classifies the numerical medical and health information into patient age information, patient height information, patient weight information, and medical card number information; in the numerical classification formula, it is necessary to first classify the numerical medical and health information according to a column All data in Calculate the average value, and then use the average value and the preset threshold The difference between them is used to determine the specific type of numerical medical and health information; the absolute value of the difference is set as the classification score ; The numerical classification formula is as follows: ; Where: represents the classification score; Indicates the preset threshold; Indicates the first Column; when =1, The preset threshold for age is assumed based on the patient age information in previous medical and health information. =47; when =2, The preset threshold for height is represented by the patient height information in the previous medical health information. =160, where height is in centimeters; =3, Represents the preset threshold of weight. Based on the patient weight information in the past medical and health information, we first assume =55, where weight is in kilograms; =4, Indicates the preset threshold of the medical card number. According to the medical card number information in the medical health information, the medical card number is generally 9 to 11 digits. Assume =10 9 ; The specific value of the preset threshold here is only a hypothetical value and can be dynamically adjusted according to the actual situation; Equal to 1, 2, 3, 4 respectively, the corresponding preset threshold and a column of data All Substitute it into the numerical classification formula to get the four classification scores ;when =2, and the classification score is is the lowest, then the corresponding column of data is the patient's height information; =4, and the classification score is is the lowest, then the corresponding column of data For medical card number information; =1, and the classification score is is the lowest, then the corresponding column of data is the patient's age information or the patient's weight information; =3, and the classification score is is the lowest, then the corresponding column of data It is the patient's age information or the patient's weight information; after judgment, a column of data can be accurately determined Is it the patient's weight information or the medical card number information, and then compare the sum of the remaining two columns of data Size, sum The largest column of data For patient weight information, total The smallest column of data Patient age information.

[0028] Furthermore, when the enhanced recognition module 12 reclassifies the characters in the text-based medical and health information, a column of data When the probability of the place name keyword text appearing is greater than 70%, the corresponding column of data For the patient's place of origin information; when a column of data When the probability of occurrence of the disease keyword text is greater than 70%, the corresponding column of data For patient disease information; when a column of data When the probability of the gender keyword is greater than 80%, the corresponding column of data is the patient's gender information; after determining the patient's gender information, disease information, and place of origin information, the remaining column The patient's name information.

[0029] Furthermore, when the enhanced recognition module 12 reclassifies the characters in the text-based medical and health information, a column of data When the probability of the place name keyword text appearing is greater than 70%, the corresponding column of data For the patient's place of origin information; when a column of data When the probability of occurrence of the disease keyword text is greater than 70%, the corresponding column of data For patient disease information; when a column of data When the probability of the gender keyword is greater than 80%, the corresponding column of data is the patient's gender information; after determining the patient's gender information, disease information, and place of origin information, the remaining column The patient's name information.

[0030] Furthermore, the folding storage module 2 encrypts the numerical medical and health information through the sum encryption formula to obtain the numerical encryption result and the numerical encryption key Then the folding storage module 2 will encrypt the result of the numerical value and the numerical encryption key Stored in the first repository 21; according to the sum encryption formula, a column of data All According to its position, multiply it by the corresponding coefficient and find the sum, then we can get the numerical encryption result. , and then the numerical encryption result The data is stored in the first repository 21 to realize the encryption and compression functions of the numerical medical and health information; the sum encryption formula is as follows: Then fold the storage module 2 to Add 0 in front of Fill it up to five digits to get the first temporary key of five digits , and then all Arrange them in order to form a string, and then use the first 20 digits of the string as the second temporary key , followed by the first temporary key and the second temporary key Through mathematical operations, the numerical encryption key is obtained by adding , and then the numerical encryption key and the second temporary key Stored in the first repository 21; Numerical encryption key The calculation formula is as follows: ; When the hidden retrieval module 3 retrieves the numerical medical and health information, the hidden retrieval module 3 decrypts the numerical encryption result through the numerical decryption formula. Decrypt to get the original data ; The numerical decryption formula is as follows: ; Where: Indicates the floor symbol; mod indicates the modulo operation.

[0031] Furthermore, the folding storage module 2 converts the Chinese characters in the patient's gender information into Arabic numerals, 1 for male and 0 for female; then the folding storage module 2 stores the converted patient gender information and patient name information, patient disease information, and patient place of origin information in the second storage repository 22.

[0032] Furthermore, the enhanced recognition module 12 first calculates a column of data Modification rate , The value range is 0 to 1, and then the modification rate calculated this time is Change rate compared to the past Compare and check whether there is any abnormal manual modification in the bill; the information input module 11 selects the corresponding data to be retrieved, and the hidden retrieval module 3 retrieves the corresponding data from the first storage library 21 and the second storage library 22 according to the selection in the information input module 11, and the hidden retrieval module 3 then retrieves the corresponding data according to the modification rate Get the call parameters , and then according to the call parameters And a virtual retrieval formula, randomly retrieves a corresponding column of data from the first storage library 21, the second storage library 22 and the backup storage library 23 In order to randomly retrieve a corresponding column of data from the first storage library 21, the second storage library 22 and the backup storage library 23 as randomly as possible , virtual call coefficient must be a random number; and a column of data Total number of rows in In each issue of medical and health information, Represents a column of data The Lth row of , therefore, The specific value is 1 to The non-fixed random number in the data, and the modification rate of each issue of medical and health information Also a random number; the rate will be modified later Expand 100 times to an integer greater than 0 to get the call parameter , and then through The modulo operation between the total number of columns 8 obtains the first random number, which is an integer greater than or equal to 0. Perform a modulus operation on the first random number plus 1 to get the second random number, and then select the maximum value of the first and second random numbers as the virtual call coefficient. , the first random number is added by 1 to prevent the modulo operation from having no solution; virtual call formula and call parameter The calculation formula is as follows: ; Where: Indicates the virtual call coefficient, when When the number is an odd number, the hidden retrieval module 3 randomly retrieves a corresponding column of data from the first storage library 21 and the second storage library 22. ,when When the number is an even number, the hidden retrieval module 3 randomly retrieves a corresponding column of data from the backup storage library 23. ; max means taking the maximum value in a set of values.

[0033] For a closed-loop management system for medical and health information, such as Figure 2 and Figure 3 As shown, it includes an identification and detection module 1, a folding storage module 2, and a hidden retrieval module 3; the folding storage module 2 is unidirectionally connected to the identification and detection module 1; the hidden retrieval module 3 is unidirectionally connected to the folding storage module 2; the hidden retrieval module 3 is also bidirectionally connected to the identification and detection module 1; the identification and detection module 1 includes an information input module 11, an enhanced identification module 12, and an information output module 13; the enhanced identification module 12 is bidirectionally connected to the information input module 11; the folding storage module 2 is unidirectionally connected to the enhanced identification module 12; the information output module 13 is unidirectionally connected to the enhanced identification module 12; the hidden retrieval module 3 is unidirectionally connected to the information output module 13; the hidden retrieval module 3 is also unidirectionally connected to the enhanced identification module 12; the folding storage module 2 includes a first storage repository 21, a second storage repository 22, and a spare storage repository 23; the first storage repository 21 is used to store text-type medical and health information; the second storage repository 22 is used to store numerical medical and health information; the spare storage repository 23 is used to store false medical and health information.

[0034] The present invention is not limited to the above-mentioned specific implementation modes. Various changes made by technicians in the relevant technical field based on the above-mentioned conception without creative work all fall within the protection scope of the present invention.

Claims

1. A closed-loop management method for medical health information, characterized in that: The following steps are involved: Step S1: inputting the original medical health information form of each medical institution into the information input module (11) of the identification detection module (1), and then the information input module (11) transmits the original medical health information form to the enhanced identification module (12), and the enhanced identification module (12) determines the original medical health information through the data mode determination formula, and divides the original medical health information into numerical type and text type. For the original medical health information that cannot be determined, it will be manually verified, and the wrong medical health information will be corrected to obtain the verified medical health information; Step S2: When judging the type of the verified medical and health information, the judgment weight parameter in the data mode judgment formula It needs to be dynamically updated according to the judgment update formula to enhance the accuracy of judging the correct medical and health information; Step S3: Then, the enhanced recognition module (12) further classifies the numerical medical and health information using a numerical classification formula, and classifies the numerical medical and health information into patient age information, patient height information, patient weight information, and medical card number information; Step S4: Then, the enhanced recognition module (12) further classifies the characters in the text-based medical and health information, and classifies the text-based medical and health information into patient name information, patient disease information, patient place of origin information, and patient gender information; Step S5: the identified medical health information is input into the folding storage module (2), the folding storage module (2) stores the text-type medical health information in the first storage library (21), and the folding storage module (2) also encrypts the numerical medical health information using a sum encryption formula and then stores it in the second storage library (22); Step S6: After step 1 is completed, data that is manually verified and indeed contains errors will be marked as error data, and the folding storage module (2) will orderly combine multiple different types of error data into false medical and health information, and then the folding storage module (2) will store the false medical and health information in the backup storage library (23); Step S7: After step 1 is completed, the enhanced recognition module (12) will first calculate a column of data Modification rate , and then the modification rate calculated this time is Change rate compared to the past Compare and check whether there are any abnormal manual modifications to the bill, and then hide and retrieve the module (3) to check the modification rate. Get the call parameters , and then according to the call parameters And a virtual call formula is used to realize random confusion call of data in the first storage library (21), the second storage library (22) and the backup storage library (23).

2. A medical health information closed-loop management method as claimed in claim 1, characterized in that: After the original medical health information table is transmitted to the enhanced recognition module (12), the enhanced recognition module (12) determines the original medical health information through a data modality determination formula, and divides the original medical health information into a numerical type and a text type; suppose a column of data in the original medical health information is ,in, represents the i-th column in the original medical and health information, =1,2,3,4,5,6,7,8, Represents a column of data The specific data of row L in , , Represents a set of positive integers; the data mode determination formula for a column of data is as follows: ; Where: Represents a column of data is the probability of numerical medical and health information; represents the judgment weight parameter; Represents a column of data The Lth row of ; It also represents a column of data The total number of rows; Represents a digital judgment function, when When the first character in is an Arabic numeral, =1, when When the first character in is not an Arabic numeral, =0; Represents the Chinese-English judgment function, when When the first character in is a Chinese character or English, =1, when When the first character in is not a Chinese character or English, =0; represents an infinite coefficient; express The decimal Unicode code point corresponding to the first character in; Indicates the floor symbol; represents a natural constant; Indicates the number of selections, when =0, Express Whether it is an uppercase letter is judged. =1, Express Determine whether it is a lowercase letter; In order to enhance the accuracy of judging medical and health information, the judgment weight parameter in the data modality judgment formula It needs to be dynamically updated according to the determination update formula; the determination update formula is as follows: ; In the formula: min means taking the minimum value in a set of values; Represents the probability correction coefficient. When the number of columns that have been determined to be text-type medical and health information is equal to the number of columns that have been determined to be numerical medical and health information, =1, when the number of columns that have been determined to be text-type medical and health information is not equal to the number of columns that have been determined to be numerical-type medical and health information, =1.

2.

3. A medical health information closed-loop management method as claimed in claim 2, characterized in that: when ≥0.65, the corresponding column of data It is numerical medical and health information; ≤0.4, the corresponding column of data It is text-based medical and health information; When 0.4< <0.65, the corresponding column of data Manual verification is required; if a column of data If it is judged to be numerical medical and health information, then middle =0 corresponds to Manual verification is required; if a column of data If it is judged to be text-based medical and health information, then middle =0 corresponds to Manual verification is required; after the manual verification is completed, the verified medical health information will be manually re-entered into the information input module (11).

4. A medical health information closed-loop management method as claimed in claim 3, characterized in that: Then, the enhanced recognition module (12) further classifies the numerical medical and health information through a numerical classification formula, and classifies the numerical medical and health information into patient age information, patient height information, patient weight information, and medical card number information; the numerical classification formula is as follows: ； Where: represents the classification score; Indicates the preset threshold; Indicates the first Column; when =1, represents the preset threshold of age, =47; when =2, represents the preset threshold value of height, =160, where height is in centimeters; =3, Indicates the preset threshold value of body weight, =55, where weight is in kilograms; =4, Indicates the preset threshold of the medical card number. =10 9 ;make Equal to 1, 2, 3, 4 respectively, and get four classification scores ;when =2, and the classification score is is the lowest, then the corresponding column of data is the patient's height information; =4, and the classification score is is the lowest, then the corresponding column of data For medical card number information; =1, and the classification score is is the lowest, then the corresponding column of data is the patient's age information or the patient's weight information; =3, and the classification score is is the lowest, then the corresponding column of data It is the patient's age information or the patient's weight information; after judgment, a column of data can be accurately determined Is it the patient's weight information or the medical card number information, and then compare the sum of the remaining two columns of data Size, sum The largest column of data For patient weight information, total The smallest column of data Patient age information.

5. A medical health information closed-loop management method as claimed in claim 4, characterized in that: When the enhanced recognition module (12) reclassifies the characters in the text-based medical and health information, a column of data When the probability of the place name keyword text appearing is greater than 70%, the corresponding column of data For the patient's place of origin information; when a column of data When the probability of occurrence of the disease keyword text is greater than 70%, the corresponding column of data For patient disease information; when a column of data When the probability of the gender keyword is greater than 80%, the corresponding column of data is the patient's gender information; after determining the patient's gender information, disease information, and place of origin information, the remaining column The patient's name information.

6. A closed-loop management method for medical health information as claimed in claim 5, characterized in that: The folding storage module (2) encrypts the numerical medical and health information through the sum encryption formula to obtain the numerical encryption result and the numerical encryption key , and then the folding storage module (2) encrypts the numerical result and the numerical encryption key Stored in the first database; the sum encryption formula is as follows: ; Then fold the storage module (2) to Add 0 in front of Fill it up to five digits to get the first temporary key of five digits , and then all Arrange them in order to form a string, and then use the first 20 digits of the string as the second temporary key , followed by the first temporary key and the second temporary key Through mathematical operations, the numerical encryption key is obtained by adding : ; When the hidden retrieval module (3) retrieves the numerical medical and health information, the hidden retrieval module (3) decrypts the numerical encryption result through the numerical decryption formula. Decrypt to get the original data ; The numerical decryption formula is as follows: ; Where: Indicates the floor symbol; mod indicates the modulo operation.

7. A medical health information closed-loop management method as claimed in claim 6, characterized in that: The folding storage module (2) converts the Chinese characters in the patient's gender information into Arabic numerals, with male being 1 and female being 0; then the folding storage module (2) stores the converted patient gender information and patient name information, patient disease information, and patient place of origin information in a second database.

8. A medical health information closed-loop management method as claimed in claim 7, characterized in that: The enhanced recognition module (12) will first calculate a column of data Modification rate , The value range is 0 to 1, and then the modification rate calculated this time is Change rate compared to the past Compare and check whether there is any abnormal artificial modification in the bill; the information input module (11) selects the corresponding data to be retrieved, and the hidden retrieval module (3) retrieves the corresponding data from the first storage library (21) and the second storage library (22) according to the selection in the information input module (11), and the hidden retrieval module (3) then retrieves the corresponding data according to the modification rate Get the call parameters , and then according to the call parameters and a virtual retrieval formula, randomly retrieving a corresponding column of data from the first storage library (21), the second storage library (22) and the backup storage library (23) ; Virtual call formula and call parameters The calculation formula is as follows: ; Where: Indicates the virtual call coefficient, when When it is an odd number, the hidden retrieval module (3) randomly retrieves a corresponding column of data from the first storage library (21) and the second storage library (22). ,when When it is an even number, the hidden retrieval module (3) randomly retrieves a corresponding column of data from the backup storage library (23) ; max means taking the maximum value in a set of values.

9. A system based on the closed-loop management method of medical health information according to claim 8, characterized in that: The invention comprises an identification detection module (1), a folding storage module (2), and a hidden retrieval module (3); the folding storage module (2) is unidirectionally connected to the identification detection module (1); the hidden retrieval module (3) is unidirectionally connected to the folding storage module (2); the hidden retrieval module (3) is also bidirectionally connected to the identification detection module (1); the identification detection module (1) comprises an information input module (11), an enhanced identification module (12), and an information output module (13); the enhanced identification module (12) is bidirectionally connected to the information input module (11); the folding storage module (2) is unidirectionally connected to the enhanced identification module (12); the information output module (13) is unidirectionally connected to the enhanced identification module (12); the hidden retrieval module (3) is unidirectionally connected to the information output module (13); the hidden retrieval module (3) is also unidirectionally connected to the enhanced identification module (12).

10. A system based on the closed-loop management method of medical health information according to claim 9, characterized in that: The folding storage module (2) comprises a first storage library (21), a second storage library (22), and a spare storage library (23); the first storage library (21) is used to store text-type medical health information; the second storage library (22) is used to store numerical-type medical health information; and the spare storage library (23) is used to store false-type medical health information.

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