A cardiovascular disease identification method and system based on medical insurance data

Abstract

The present invention relates to the field of standardized identification technology, and more specifically, to a method and system for cardiovascular disease identification based on medical insurance data. The scheme includes obtaining a medical insurance database, storing the information as a medical insurance data group according to the time of information entry; setting a standard name dictionary including heart failure, heart failure, ischemic heart disease or coronary heart disease, valvular heart disease, arrhythmia, thrombotic disease, peripheral vascular disease, cerebrovascular disease, pulmonary vascular disease, hypertension and aortic aneurysm according to the relevant disease diagnosis names; performing keyword matching, similarity analysis and matching according to the standard name dictionary and the medical insurance data group, and forming a standard classification list with the third standard dictionary; storing and displaying according to the standard classification list. After obtaining the medical insurance data, the scheme performs a three-level similarity analysis according to the standard diagnosis name, completes multiple diagnostic dictionary updates, and forms a standardized identification method that is highly efficient and suitable for practical applications.

Description

Technical Field

[0001] The present invention relates to the field of standardized identification technology, and more specifically, to a cardiovascular disease identification method and system based on medical insurance data. Background Art

[0002] There are many types of cardiovascular diseases, but in reality, the diagnostic information of patients in the data is poorly standardized and there is a lack of relevant standardized identification tools.

[0003] Prior to the invention, doctors could only rely on manual judgment and identification based on their experience, which was inefficient, inaccurate, and unclear in classification. Especially for cardiovascular diseases, there were many overlaps in the description methods and forms, making it difficult to quickly distinguish them. Summary of the invention

[0004] In view of the above problems, the present invention proposes a cardiovascular disease identification method and system based on medical insurance data. After obtaining the medical insurance data, a three-level similarity analysis is performed according to the standard diagnosis name, and multiple diagnostic dictionary updates are completed, thereby forming a highly efficient standardized identification method that is suitable for practical applications.

[0005] According to a first aspect of an embodiment of the present invention, a method for identifying cardiovascular diseases based on medical insurance data is provided.

[0006] In one or more embodiments, preferably, the cardiovascular disease identification method based on medical insurance data includes:

[0007] Obtain the medical insurance database and store the information into a medical insurance data group according to the time of information entry;

[0008] According to the names of relevant disease diagnoses, 11 standard name dictionaries of categories are set in parallel, wherein the standard name dictionaries include heart failure, heart failure, ischemic heart disease or coronary heart disease, valvular heart disease, arrhythmia, thrombotic disease, peripheral vascular disease, cerebrovascular disease, pulmonary vascular disease, hypertension and aortic aneurysm;

[0009] Perform keyword matching according to the standard name dictionary and the medical insurance data group, and update the standard name dictionary to a second standard dictionary;

[0010] Perform similarity analysis and matching according to the second standard dictionary, and update the second standard dictionary to a third standard dictionary;

[0011] Perform similarity analysis and matching according to the third standard dictionary, and form a standard classification list with the third standard dictionary;

[0012] The list is classified according to the standards and then stored and displayed specifically.

[0013] In one or more embodiments, preferably, the obtaining of the medical insurance database is stored as a medical insurance data group according to the information entry time, specifically including:

[0014] Obtain the medical insurance database and extract all the names of initial disease diagnoses;

[0015] The information is split according to the time of information entry, and all the initial disease diagnosis names are integrated and deduplicated, and stored as a medical insurance data group.

[0016] In one or more embodiments, preferably, according to the relevant disease diagnosis names, 11 classified standard name dictionaries are set in parallel, wherein the standard name dictionary includes heart failure, heart failure, ischemic heart disease or coronary heart disease, valvular heart disease, arrhythmia, thrombotic disease, peripheral vascular disease, cerebrovascular disease, pulmonary vascular disease, hypertension and aortic aneurysm, specifically including:

[0017] Obtain standard diagnostic names for the classification of the circulatory system and set up 11 major categories in parallel;

[0018] A name corresponding to a standard name dictionary is set for each major category, wherein the standard name dictionary includes heart failure, heart failure, ischemic heart disease or coronary heart disease, valvular heart disease, arrhythmia, thrombotic disease, peripheral vascular disease, cerebrovascular disease, pulmonary vascular disease, hypertension and aortic aneurysm.

[0019] In one or more embodiments, preferably, performing keyword matching according to the standard name dictionary and the medical insurance data group, and updating the standard name dictionary to a second standard dictionary, specifically includes:

[0020] Using a first calculation formula according to the standard name dictionary and the medical insurance data set, the similarity between a name in any standard name dictionary and a name in the medical insurance data set is calculated;

[0021] Determine whether each similarity satisfies the second calculation formula, and if so, store the corresponding name in the medical insurance data group into the name to be classified;

[0022] Manually classify the to-be-classified names into the standard name dictionary, and update the standard name dictionary to the second standard dictionary;

[0023] For the names in the medical insurance data group that do not satisfy the second calculation formula, they are stored as legacy data to be analyzed;

[0024] The first calculation formula is:

[0025] S ij =M ij / Nj

[0026] Among them, S ij is the similarity between the name in the ith quasi-name dictionary and the name in the jth medical insurance data set, M ij N is the number of characters that are the same between the name in the i-th quasi-name dictionary and the name in the j-th medical insurance data set, j is the total number of characters in the name of the jth medical insurance data group;

[0027] The second calculation formula is:

[0028] S ij >Y 1

[0029] Among them, Y 1 is the primary matching margin.

[0030] In one or more embodiments, preferably, performing similarity analysis and matching according to the second standard dictionary and updating the second standard dictionary to a third standard dictionary specifically includes:

[0031] Acquire the legacy data to be analyzed, and perform keyword analysis using the second standard dictionary;

[0032] When the legacy data to be analyzed includes names in the second standard dictionary, the names in the legacy data to be analyzed are stored in the corresponding categories of the standard name dictionary and updated to the third standard dictionary;

[0033] When a certain name of the legacy data to be analyzed is not within the name range of the second standard dictionary, it is stored as third-level analysis data.

[0034] In one or more embodiments, preferably, performing similarity analysis and matching according to the third standard dictionary and forming a standard classification list with the third standard dictionary specifically includes:

[0035] Acquire the third-level analysis data, and calculate the third-level analysis similarity using a third calculation formula according to the second standard dictionary;

[0036] Determine the type level of the three-level analysis data using a fourth calculation formula according to the three-level analysis similarity;

[0037] When the type level is greater than or equal to the preset value, the name in the three-level analysis data corresponding to the selected area is manually analyzed and judged, and classified. When the classification of the corresponding standard name dictionary is confirmed to belong to, it is stored in the third standard dictionary. After the analysis of all the three-level analysis data is completed, the third standard dictionary is formed into a standard classification list;

[0038] When the type level is less than a preset value, the corresponding third-level analysis data is directly excluded;

[0039] The third calculation formula is:

[0040] S xy =M xy / N y

[0041] Among them, S xy is the third-level analysis similarity between the name in the yth third-level analysis data and the name in the xth second standard dictionary, M xy The number of characters of the name in the yth third-level analysis data and the xth name in the second standard dictionary is the same, N y is the total number of characters in the name in the yth level 3 analysis data;

[0042] The fourth calculation formula is:

[0043]

[0044] Where D is the type level.

[0045] In one or more embodiments, preferably, the specific storage and display of the list classified according to the standard specifically includes:

[0046] Obtain the standard classification list, split it, classify it according to the standard name dictionary, and store the standard classification list in the form of a classification group;

[0047] The classification group form is obtained, and other untrained data are directly quickly identified according to the classification group form, and displayed online.

[0048] According to a second aspect of an embodiment of the present invention, a cardiovascular disease identification system based on medical insurance data is provided.

[0049] In one or more embodiments, preferably, the cardiovascular disease identification system based on medical insurance data includes:

[0050] The data collection module is used to obtain the medical insurance database and store the information as a medical insurance data group according to the time of information entry;

[0051] A standard diagnosis classification module is used to set up 11 classification standard name dictionaries in parallel according to the relevant disease diagnosis names, wherein the standard name dictionary includes heart failure, heart failure, ischemic heart disease or coronary heart disease, valvular heart disease, arrhythmia, thrombotic disease, peripheral vascular disease, cerebrovascular disease, pulmonary vascular disease, hypertension and aortic aneurysm;

[0052] A primary division module, used for performing keyword matching according to the standard name dictionary and the medical insurance data group, and updating the standard name dictionary to a second standard dictionary;

[0053] A secondary division module, used for performing similarity analysis and matching according to the second standard dictionary, and updating the second standard dictionary to a third standard dictionary;

[0054] A three-level classification module, used for performing similarity analysis and matching according to the third standard dictionary, and forming a standard classification list with the third standard dictionary;

[0055] The dictionary storage and display module is used to classify the list according to the standard and perform specific storage and display.

[0056] According to a third aspect of an embodiment of the present invention, there is provided a computer-readable storage medium on which computer program instructions are stored. When the computer program instructions are executed by a processor, the method as described in any one of the first aspect of the embodiment of the present invention is implemented.

[0057] According to a fourth aspect of an embodiment of the present invention, there is provided an electronic device, comprising a memory and a processor, wherein the memory is used to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement any one of the methods described in the first aspect of the embodiment of the present invention.

[0058] The technical solution provided by the embodiments of the present invention may have the following beneficial effects:

[0059] The solution of the present invention is based on valid medical insurance data and performs effective deduplication and integration based on standard diagnosis names multiple times.

[0060] The solution of the present invention is simple to execute and has strong adaptability, and can efficiently and quickly identify standardized cardiovascular disease data.

[0061] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings.

[0062] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0064] Figure 1 This is a flow chart of a cardiovascular disease identification method based on medical insurance data according to an embodiment of the present invention.

[0065] Figure 2 It is a flowchart of obtaining a medical insurance database in a cardiovascular disease identification method based on medical insurance data in one embodiment of the present invention, and storing the information as a medical insurance data group according to the information entry time.

[0066] Figure 3 This is a flowchart of a cardiovascular disease identification method based on medical insurance data in which 11 standard name dictionaries of categories are set in parallel according to the names of relevant disease diagnoses, wherein the standard name dictionary includes heart failure, heart failure, ischemic heart disease or coronary heart disease, valvular heart disease, arrhythmia, thrombotic disease, peripheral vascular disease, cerebrovascular disease, pulmonary vascular disease, hypertension and aortic aneurysm.

[0067] Figure 4 It is a flowchart of performing keyword matching according to the standard name dictionary and the medical insurance data group, and updating the standard name dictionary to a second standard dictionary in a cardiovascular disease identification method based on medical insurance data in one embodiment of the present invention.

[0068] Figure 5 It is a flowchart of performing similarity analysis and matching according to the second standard dictionary and updating the second standard dictionary to a third standard dictionary in a cardiovascular disease identification method based on medical insurance data according to an embodiment of the present invention.

[0069] Figure 6 It is a flowchart of a cardiovascular disease identification method based on medical insurance data in an embodiment of the present invention, performing similarity analysis and matching according to the third standard dictionary, and forming the third standard dictionary into a standard classification list.

[0070] Figure 7 It is a flowchart of a cardiovascular disease identification method based on medical insurance data in one embodiment of the present invention, which performs specific storage and display according to the standard classification list.

[0071] Figure 8 This is a structural diagram of a cardiovascular disease identification system based on medical insurance data according to an embodiment of the present invention.

[0072] Fig. 9 It is a structural diagram of an electronic device in one embodiment of the present invention.

[0073] Fig.10 It is a diagram showing the execution process of an embodiment of the present invention. DETAILED DESCRIPTION

[0074] In some of the processes described in the specification and claims of the present invention and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., do not represent the order of precedence, and do not limit the "first" and "second" to be different types.

[0075] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0076] There are many types of cardiovascular diseases, but in reality, the diagnostic information of patients in the data is poorly standardized and there is a lack of relevant standardized identification tools.

[0077] Prior to the invention, doctors could only rely on manual judgment and identification based on their experience, which was inefficient, inaccurate, and unclear in classification. Especially for cardiovascular diseases, there were many overlaps in the description methods and forms, making it difficult to quickly distinguish them.

[0078] In an embodiment of the present invention, a cardiovascular disease identification method and system based on medical insurance data is provided. After obtaining the medical insurance data, the solution performs a three-level similarity analysis based on the standard diagnosis name, completes multiple diagnostic dictionary updates, and then forms a standardized identification method that is highly efficient and suitable for practical applications.

[0079] According to a first aspect of an embodiment of the present invention, a method for identifying cardiovascular diseases based on medical insurance data is provided.

[0080] Figure 1 This is a flow chart of a cardiovascular disease identification method based on medical insurance data according to an embodiment of the present invention.

[0081] In one or more embodiments, preferably, the cardiovascular disease identification method based on medical insurance data includes:

[0082] S101, obtaining a medical insurance database, and storing the information as a medical insurance data group according to the time of information entry;

[0083] S102. According to the names of relevant disease diagnoses, 11 standard name dictionaries of categories are set in parallel, wherein the standard name dictionaries include heart failure, heart failure, ischemic heart disease or coronary heart disease, valvular heart disease, arrhythmia, thrombotic disease, peripheral vascular disease, cerebrovascular disease, pulmonary vascular disease, hypertension and aortic aneurysm;

[0084] S103, performing keyword matching according to the standard name dictionary and the medical insurance data group, and updating the standard name dictionary to a second standard dictionary;

[0085] S104, performing similarity analysis and matching according to the second standard dictionary, and updating the second standard dictionary to a third standard dictionary;

[0086] S105, performing similarity analysis and matching according to the third standard dictionary, and forming a standard classification list with the third standard dictionary;

[0087] S106, classifying the list according to the standard, and performing specific storage and display.

[0088] In an embodiment of the present invention, after obtaining relevant databases such as the autonomous region's urban basic medical insurance and basic medical insurance, data fusion is performed to form all the monitoring data corresponding to the observation period. On this basis, three levels of dictionary updates are completed according to the diagnosis name and the corresponding standard diagnosis, which can efficiently complete the splitting and identification of medical insurance data, ensuring accuracy on the one hand and recognition efficiency on the other.

[0089] Figure 2 It is a flowchart of obtaining a medical insurance database in a cardiovascular disease identification method based on medical insurance data in one embodiment of the present invention, and storing the information as a medical insurance data group according to the information entry time.

[0090] like Figure 2 As shown, in one or more embodiments, preferably, the obtaining of the medical insurance database is stored as a medical insurance data group according to the information entry time, specifically including:

[0091] S201, obtain the medical insurance database and extract all the names of initial disease diagnoses;

[0092] S202, splitting the information according to the time of information entry, integrating and removing duplicates of all the initial disease diagnosis names, and storing them as a medical insurance data group.

[0093] In an embodiment of the present invention, in the database of basic medical insurance for urban employees and basic medical insurance for urban residents in Inner Mongolia Autonomous Region, patients diagnosed with at least one of five types of tumors (prostate cancer, breast cancer, cervical cancer, gastric cancer and esophageal cancer) between January 1, 2012 and June 1, 2020 were identified, and their initial disease diagnosis names were captured, integrated and deduplicated, and finally a medical insurance data group was formed.

[0094] Figure 3 This is a flowchart of a cardiovascular disease identification method based on medical insurance data in which 11 standard name dictionaries of categories are set in parallel according to the names of relevant disease diagnoses, wherein the standard name dictionary includes heart failure, heart failure, ischemic heart disease or coronary heart disease, valvular heart disease, arrhythmia, thrombotic disease, peripheral vascular disease, cerebrovascular disease, pulmonary vascular disease, hypertension and aortic aneurysm.

[0095] like Figure 3 As shown, in one or more embodiments, preferably, according to the relevant disease diagnosis names, 11 classified standard name dictionaries are set in parallel, wherein the standard name dictionary includes heart failure, heart failure, ischemic heart disease or coronary heart disease, valvular heart disease, arrhythmia, thrombotic disease, peripheral vascular disease, cerebrovascular disease, pulmonary vascular disease, hypertension and aortic aneurysm, specifically including:

[0096] S301, obtaining the classification of the standard diagnostic name for the circulatory system, and setting 11 major categories in parallel;

[0097] S302. Set a name corresponding to a standard name dictionary for each major category, wherein the standard name dictionary includes heart failure, heart failure, ischemic heart disease or coronary heart disease, valvular heart disease, arrhythmia, thrombotic disease, peripheral vascular disease, cerebrovascular disease, pulmonary vascular disease, hypertension and aortic aneurysm.

[0098] In the embodiment of the present invention, according to the recommendations of the guidelines for oncology cardiology, the standard diagnostic names of CVD in IC D-10 (circulatory system disease codes, I00-I99) are classified into 12 categories, including: (1) heart failure; (2) heart failure; (3) ischemic heart disease / coronary heart disease; (4) valvular heart disease; (5) arrhythmia; (6) thrombotic disease; (7) peripheral vascular disease; (8) cerebrovascular disease; (9) pulmonary vascular disease; (10) hypertension; (11) aortic aneurysm; (12) others; and then, based on this standard classification, rapid identification and analysis are performed.

[0099] Figure 4 It is a flowchart of performing keyword matching according to the standard name dictionary and the medical insurance data group, and updating the standard name dictionary to a second standard dictionary in a cardiovascular disease identification method based on medical insurance data in one embodiment of the present invention.

[0100] like Figure 4 As shown, in one or more embodiments, preferably, performing keyword matching according to the standard name dictionary and the medical insurance data group, and updating the standard name dictionary to a second standard dictionary, specifically includes:

[0101] S401, using a first calculation formula to calculate the similarity between a name in any standard name dictionary and a name in the medical insurance data group according to the standard name dictionary and the medical insurance data group;

[0102] S402, determining whether each similarity satisfies the second calculation formula, and if so, storing the corresponding name in the medical insurance data group into the name to be classified;

[0103] S403, manually classifying the names to be classified into the standard name dictionary, and updating the standard name dictionary to the second standard dictionary;

[0104] S404, storing the names in the medical insurance data group that do not satisfy the second calculation formula as legacy data to be analyzed;

[0105] The first calculation formula is:

[0106] S ij =M ij / N j

[0107] Among them, S ij is the similarity between the name in the ith quasi-name dictionary and the name in the jth medical insurance data set, M ij N is the number of characters that are the same between the name in the i-th quasi-name dictionary and the name in the j-th medical insurance data set, j is the total number of characters in the name of the jth medical insurance data group;

[0108] The second calculation formula is:

[0109] S ij >Y 1

[0110] Among them, Y 1 is the primary matching margin.

[0111] In an embodiment of the present invention, first, the disease diagnosis name is compared with the standard diagnosis name of CVD in ICD-10 and the similarity (the ratio of the number of consistent characters to the total length of characters) is calculated. The second calculation formula is used for calculation. The primary matching margin is greater than Y1, and Y1 is the primary matching margin, preferably 0.5. The CVD-related diagnosis names are manually classified into 12 categories in parallel. Among the 12 categories, other types are not considered for the time being, and other irrelevant diseases are excluded, thereby preliminarily establishing a CVD diagnosis name dictionary containing diagnosis names and corresponding categories.

[0112] Figure 5 It is a flowchart of performing similarity analysis and matching according to the second standard dictionary and updating the second standard dictionary to a third standard dictionary in a cardiovascular disease identification method based on medical insurance data according to an embodiment of the present invention.

[0113] like Figure 5 As shown, in one or more embodiments, preferably, performing similarity analysis and matching according to the second standard dictionary and updating the second standard dictionary to a third standard dictionary specifically includes:

[0114] S501, obtaining the legacy data to be analyzed, and performing keyword analysis using the second standard dictionary;

[0115] S502: When the legacy data to be analyzed includes a name in the second standard dictionary, the name in the legacy data to be analyzed is stored in a corresponding category of the standard name dictionary and updated to the third standard dictionary;

[0116] S503: When a certain name of the legacy data to be analyzed is not within the name range of the second standard dictionary, it is stored as third-level analysis data.

[0117] In the embodiment of the present invention, the similarity between the dictionary and the disease diagnosis names with similarity ≤ 0.5 in the first step is then calculated again. The CVD-related disease diagnosis names with similarity ≥ 0.8 after the recalculation are manually classified into 12 categories in parallel to update the dictionary.

[0118] Figure 6 It is a flowchart of a cardiovascular disease identification method based on medical insurance data in an embodiment of the present invention, performing similarity analysis and matching according to the third standard dictionary, and forming the third standard dictionary into a standard classification list.

[0119] like Figure 6 As shown, in one or more embodiments, preferably, performing similarity analysis and matching according to the third standard dictionary, and forming the third standard dictionary into a standard classification list specifically includes:

[0120] S601, obtaining the third-level analysis data, and calculating the third-level analysis similarity using a third calculation formula according to the second standard dictionary;

[0121] S602: Determine the type level of the three-level analysis data using a fourth calculation formula according to the three-level analysis similarity;

[0122] S603, when the type level is greater than or equal to the preset value, the name in the three-level analysis data corresponding to the selected area is manually analyzed and judged, and classified, and the classification of the corresponding standard name dictionary is stored in the third standard dictionary when it is confirmed that it belongs to the classification of the corresponding standard name dictionary. After the analysis of all the three-level analysis data is completed, the third standard dictionary is formed into a standard classification list;

[0123] S604, when the type level is less than a preset value, directly exclude the corresponding third-level analysis data;

[0124] The third calculation formula is:

[0125] S xy =M xy / N y

[0126] Among them, S xy is the third-level analysis similarity between the name in the yth third-level analysis data and the name in the xth second standard dictionary, M xy The number of characters of the name in the yth third-level analysis data and the xth name in the second standard dictionary is the same, N y is the total number of characters in the name in the yth level 3 analysis data;

[0127] The fourth calculation formula is:

[0128]

[0129] Where D is the type level.

[0130] In the embodiment of the present invention, for patients whose similarity is <0.8 after recalculation, stratified sampling is performed, and 200 entries are extracted from each stratum to verify whether there are any missing CVD-related diagnoses. If there are still some missing CVD-related diagnoses, the process of matching the new dictionary with the diagnosis name will be performed for the third time. The comparison and similarity calculation of the disease diagnosis name with the dictionary are obtained through real-time analysis, which is simple and efficient. The analysis process can use software such as prony.

[0131] Figure 7 It is a flowchart of a cardiovascular disease identification method based on medical insurance data in one embodiment of the present invention, which performs specific storage and display according to the standard classification list.

[0132] like Figure 7 As shown, in one or more embodiments, preferably, the specific storage and display of the list classified according to the standard specifically includes:

[0133] S701, obtaining the standard classification list, splitting it, classifying it according to the standard name dictionary, and storing the standard classification list in the form of a classification group;

[0134] S702: Obtain the classification group form, and directly obtain rapid recognition of other untrained data according to the classification group form, and display it online.

[0135] In an embodiment of the present invention, a computable prototype tool is proposed, which can effectively extract non-standardized CVD diagnosis names of cancer patients in medical insurance data, which will help to carry out real-world research in oncology cardiology. At the same time, various medical databases are also in urgent need of improving the standardization of data storage.

[0136] According to a second aspect of an embodiment of the present invention, a cardiovascular disease identification system based on medical insurance data is provided.

[0137] Figure 8 This is a structural diagram of a cardiovascular disease identification system based on medical insurance data according to an embodiment of the present invention.

[0138] In one or more embodiments, preferably, the cardiovascular disease identification system based on medical insurance data includes:

[0139] The data collection module 801 is used to obtain the medical insurance database and store the information as a medical insurance data group according to the time of information entry;

[0140] The standard diagnosis classification module 802 is used to set up 11 classification standard name dictionaries in parallel according to the relevant disease diagnosis names, wherein the standard name dictionary includes heart failure, heart failure, ischemic heart disease or coronary heart disease, valvular heart disease, arrhythmia, thrombotic disease, peripheral vascular disease, cerebrovascular disease, pulmonary vascular disease, hypertension and aortic aneurysm;

[0141] A primary division module 803, for performing keyword matching according to the standard name dictionary and the medical insurance data group, and updating the standard name dictionary to a second standard dictionary;

[0142] A secondary division module 804 is used to perform similarity analysis and matching according to the second standard dictionary, and update the second standard dictionary to a third standard dictionary;

[0143] The three-level classification module 805 is used to perform similarity analysis and matching according to the third standard dictionary, and form a standard classification list with the third standard dictionary;

[0144] The dictionary storage and display module 806 is used to perform specific storage and display according to the standard classification list.

[0145] In the embodiment of the present invention, a system suitable for different structures is realized through a series of modular designs. The system can achieve closed-loop, reliable and efficient execution through collection, analysis and control.

[0146] According to a third aspect of an embodiment of the present invention, there is provided a computer-readable storage medium on which computer program instructions are stored. When the computer program instructions are executed by a processor, the method as described in any one of the first aspect of the embodiment of the present invention is implemented.

[0147] According to a fourth aspect of an embodiment of the present invention, an electronic device is provided. Fig. 9 It is a structural diagram of an electronic device in one embodiment of the present invention. Fig. 9 The electronic device shown is a universal cardiovascular disease identification device. Fig. 9 The electronic device 900 includes one or more (only one is shown in the figure) processors 902, a memory 904 and a wireless module 906 coupled to each other. The memory 904 stores a program that can execute the content of the above-mentioned embodiment, and the processor 902 can execute the program stored in the memory 904.

[0148] Among them, the processor 902 may include one or more processing cores. The processor 902 uses various interfaces and lines to connect various parts of the entire electronic device 900, and executes various functions and processes data of the electronic device 900 by running or executing instructions, programs, code sets or instruction sets stored in the memory 904, and calling data stored in the memory 904. Optionally, the processor 902 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 902 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and target application program, etc.; the GPU is responsible for rendering and drawing the display content; the modem is used to process wireless communication. It can be understood that the above-mentioned modem may not be integrated into the processor 902, and it can be implemented separately through a communication chip.

[0149] The memory 904 may include a random access memory (RAM) or a read-only memory (ROM). The memory 904 may be used to store instructions, programs, codes, code sets or instruction sets. The memory 904 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for implementing at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the following various method embodiments, etc. The data storage area may also store data (such as the aforementioned text document) created by the electronic device 900 during use, etc.

[0150] The wireless module 906 is used to receive and send electromagnetic waves, realize the mutual conversion between electromagnetic waves and electrical signals, and thus communicate with a communication network or other equipment, for example, communicate with a base station based on a mobile communication protocol. The wireless module 906 may include various existing circuit elements for performing these functions, for example, an antenna, a radio frequency transceiver, a digital signal processor, an encryption / decryption chip, a user identity module (SIM) card, a memory, etc. The wireless module 906 may communicate with various networks such as the Internet, an intranet, a wireless network, or communicate with other electronic devices through a wireless network. The above-mentioned wireless network may include a cellular telephone network, a wireless local area network, or a metropolitan area network. The above-mentioned wireless network may use various communication standards, protocols, and technologies, including but not limited to WLAN protocols and Bluetooth protocols, and may even include those protocols that have not yet been developed.

[0151] Fig.10 This is a diagram showing the execution process of an embodiment of the present invention. Fig.10 As shown in the figure, a total of 48705 initial disease diagnosis names were included. Among the disease diagnosis names with similarity > 0.5 (N = 8239), only 1875 diagnosis names were consistent with the standard CVD diagnosis, 2262 diagnosis names were added to the CVD category after manual judgment, and a preliminary CVD diagnosis name dictionary was established, and 4102 diagnosis names were excluded. For the diagnosis names with similarity ≤ 0.5 (N = 40466), 3322 diagnosis names had a similarity ≥ 0.8 compared with the CVD diagnosis name dictionary. Among them, 3176 diagnosis names were added to the dictionary. Stratified sampling was performed on the disease diagnosis names with similarity < 0.8 after recalculation. The results showed that the three groups did not contain CVD-related diagnoses ([0, 0.3] (N = 3706), [0.3, 0.5) (N = 19044), [0.5, 0.8) (N = 14394)), so they were all excluded. The final dictionary contains 7313 CVD-related disease diagnosis names and their corresponding categories.

[0152] The technical solution provided by the embodiments of the present invention may have the following beneficial effects:

[0153] The solution of the present invention is based on valid medical insurance data and performs effective deduplication and integration based on standard diagnosis names multiple times.

[0154] The solution of the present invention is simple to execute and has strong adaptability, and can efficiently and quickly identify standardized cardiovascular disease data.

[0155] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer-usable program code.

[0156] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0157] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0158] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0159] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A cardiovascular disease identification method based on medical insurance data, characterized in that: The method includes: Obtain the medical insurance database and store the information into a medical insurance data group according to the time of information entry; According to the names of relevant disease diagnoses, 11 standard name dictionaries of categories are set in parallel, wherein the standard name dictionaries include heart failure, heart failure, ischemic heart disease or coronary heart disease, valvular heart disease, arrhythmia, thrombotic disease, peripheral vascular disease, cerebrovascular disease, pulmonary vascular disease, hypertension and aortic aneurysm; Perform keyword matching according to the standard name dictionary and the medical insurance data group, and update the standard name dictionary to a second standard dictionary; Perform similarity analysis and matching according to the second standard dictionary, and update the second standard dictionary to a third standard dictionary; Perform similarity analysis and matching according to the third standard dictionary, and form a standard classification list with the third standard dictionary; Classify the list according to the standards and carry out specific storage and display; The step of performing keyword matching according to the standard name dictionary and the medical insurance data group and updating the standard name dictionary to a second standard dictionary specifically includes: Using a first calculation formula according to the standard name dictionary and the medical insurance data set, the similarity between a name in any standard name dictionary and a name in the medical insurance data set is calculated; Determine whether each similarity satisfies the second calculation formula, and if so, store the corresponding name in the medical insurance data group into the name to be classified; Manually classify the to-be-classified names into the standard name dictionary, and update the standard name dictionary to the second standard dictionary; For the names in the medical insurance data group that do not satisfy the second calculation formula, they are stored as legacy data to be analyzed; The first calculation formula is: S ij =M ij / N j Among them, S ij is the similarity between the name in the ith quasi-name dictionary and the name in the jth medical insurance data set, M ij N is the number of characters that are the same between the name in the i-th quasi-name dictionary and the name in the j-th medical insurance data set, j is the total number of characters in the name of the jth medical insurance data group; The second calculation formula is: With ij >Y1 Where, Y1 is the primary matching margin; The performing similarity analysis and matching according to the second standard dictionary and updating the second standard dictionary to a third standard dictionary specifically includes: Acquire the legacy data to be analyzed, and perform keyword analysis using the second standard dictionary; When the legacy data to be analyzed includes names in the second standard dictionary, the names in the legacy data to be analyzed are stored in the corresponding categories of the standard name dictionary and updated to the third standard dictionary; When a certain name of the legacy data to be analyzed is not within the name range of the second standard dictionary, it is stored as third-level analysis data; The similarity analysis and matching are performed according to the third standard dictionary, and the third standard dictionary is used to form a standard classification list, which specifically includes: Acquire the third-level analysis data, and calculate the third-level analysis similarity using a third calculation formula according to the second standard dictionary; Determine the type level of the three-level analysis data using a fourth calculation formula according to the three-level analysis similarity; When the type level is greater than or equal to the preset value, the name in the three-level analysis data corresponding to the selected area is manually analyzed and judged, and classified. When the classification of the corresponding standard name dictionary is confirmed to belong to, it is stored in the third standard dictionary. After the analysis of all the three-level analysis data is completed, the third standard dictionary is formed into a standard classification list; When the type level is less than a preset value, the corresponding third-level analysis data is directly excluded; The third calculation formula is: S xy =M xy / N y Among them, S xy is the third-level analysis similarity between the name in the yth third-level analysis data and the name in the xth second standard dictionary, M xy The number of characters of the name in the yth third-level analysis data and the xth name in the second standard dictionary is the same, N y is the total number of characters in the name in the yth level 3 analysis data; The fourth calculation formula is: Where D is the type level.

2. A cardiovascular disease identification method based on medical insurance data as claimed in claim 1, characterized in that: The obtaining of the medical insurance database, storing the information as a medical insurance data group according to the information entry time, specifically includes: Obtain the medical insurance database and extract all the names of initial disease diagnoses; The information is split according to the time of information entry, and all the initial disease diagnosis names are integrated and deduplicated, and stored as a medical insurance data group.

3. A cardiovascular disease identification method based on medical insurance data as claimed in claim 1, characterized in that: According to the diagnosis names of related diseases, 11 standard name dictionaries of categories are set in parallel, wherein the standard name dictionary includes heart failure, heart failure, ischemic heart disease or coronary heart disease, valvular heart disease, arrhythmia, thrombotic disease, peripheral vascular disease, cerebrovascular disease, pulmonary vascular disease, hypertension and aortic aneurysm, specifically including: Obtain standard diagnostic names for the classification of the circulatory system and set up 11 major categories in parallel; A name corresponding to a standard name dictionary is set for each major category, wherein the standard name dictionary includes heart failure, heart failure, ischemic heart disease or coronary heart disease, valvular heart disease, arrhythmia, thrombotic disease, peripheral vascular disease, cerebrovascular disease, pulmonary vascular disease, hypertension and aortic aneurysm.

4. The cardiovascular disease identification method based on medical insurance data according to claim 1, characterized in that: The specific storage and display of the list classified according to the standard specifically includes: Obtain the standard classification list, split it, classify it according to the standard name dictionary, and store the standard classification list in the form of a classification group; The classification group form is obtained, and rapid recognition of other untrained data is directly obtained based on the classification group form, and the data is displayed online.

5. A cardiovascular disease identification system based on medical insurance data, characterized in that: The system is used to implement the method according to any one of claims 1 to 4, and the system comprises: The data collection module is used to obtain the medical insurance database and store the information as a medical insurance data group according to the time of information entry; A standard diagnosis classification module is used to set up 11 classification standard name dictionaries in parallel according to the relevant disease diagnosis names, wherein the standard name dictionary includes heart failure, heart failure, ischemic heart disease or coronary heart disease, valvular heart disease, arrhythmia, thrombotic disease, peripheral vascular disease, cerebrovascular disease, pulmonary vascular disease, hypertension and aortic aneurysm; A primary division module, used for performing keyword matching according to the standard name dictionary and the medical insurance data group, and updating the standard name dictionary to a second standard dictionary; A secondary division module, used for performing similarity analysis and matching according to the second standard dictionary, and updating the second standard dictionary to a third standard dictionary; A three-level classification module, used for performing similarity analysis and matching according to the third standard dictionary, and forming a standard classification list with the third standard dictionary; The dictionary storage and display module is used to classify the list according to the standard and perform specific storage and display.

6. A computer-readable storage medium storing computer program instructions, characterized in that: The computer program instructions implement the method according to any one of claims 1 to 4 when executed by a processor.

7. An electronic device comprising a memory and a processor, characterized in that: The memory is used to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement the method according to any one of claims 1-4.

Images