Processing method and device of medical record mixed data, electronic equipment and storage medium

Abstract

The invention provides a medical record mixed data processing method and device, electronic equipment and a storage medium, and is suitable for the technical field of medical information.The processing method comprises the steps that a first data set constructed through mixed inverted indexing is used as a query basis on the basis of data of a first medical record model subjected to data treatment; the query condition in the query request is processed based on the first data set to generate a unified DSL statement to execute query, and different data structures and storage can be adapted and considered, so that the purpose of improving the query working efficiency under any unstructured medical record mixed data structure is achieved.

Description

Technical Field

[0001] The present invention relates to the field of medical information technology, and in particular, to a method, device, electronic device, and storage medium for processing mixed medical record data. Background Art

[0002] Currently, during the daily medical work process, a large amount of data is collected or generated, such as the chief complaints and current medical histories of patients. Most of this data exists in an unstructured form, making it difficult to store using traditional relational databases.

[0003] Currently, non-relational databases are mostly used to store the above types of data. However, since the data in different hospital centers is governed according to their respective versions of the medical record model and data governance tools, the data structures after governance will also be different. Therefore, for non-relational databases currently, separate codes are written and maintained for the storage of each individual data. This is not only cumbersome during development and maintenance, but also leads to low query efficiency when applying queries because it is necessary to adapt to different data storage and structures. Summary of the Invention

[0004] In view of this, embodiments of the present invention provide a method, device, electronic device, and storage medium for processing mixed medical record data to solve the problem that the existing processing of medical data cannot take into account different data structures and storage, resulting in low work efficiency when it comes to queries.

[0005] To solve the above problems, the embodiments of the present invention provide the following technical solutions:

[0006] The first aspect of the present invention discloses a method for processing mixed medical record data, and the processing method includes:

[0007] Obtain a query request, and determine a second data set from a pre-constructed first data set based on the query request. The query request includes query conditions and query parameters constructed based on a first medical record model. The first medical record model is constructed from different types of first medical record data under various medical topics, and each first medical record model corresponds to one type of first medical record data;

[0008] Based on each index in the second data set, establish a mapping relationship between the index name and the second data set. The mapping relationship is used to indicate the storage location and data format of the second medical record data in the second data set corresponding to the index;

[0009] Convert the query conditions in the query request into a DSL statement, and the DSL statement at least includes a range query statement and a matching query statement;

[0010] Query in the index based on the DSL statement, determine the medical record data ID, and group the medical record data IDs according to the corresponding index to obtain data groups;

[0011] For each of the data groups, use the mapping relationship to convert the index ID corresponding to the medical record data ID into a medical record storage ID;

[0012] Obtain the third registration content pre-stored in the data registration center according to the index ID, and construct a final query condition based on the third registration content and the medical record storage ID;

[0013] Query the database storing the third medical record data based on the final query condition, and merge the queried third medical record data to obtain the final medical record.

[0014] Preferably, the process of pre-constructing the first data set includes:

[0015] Obtain different types of first medical record data under each medical topic, and construct a corresponding first medical record model for each type of first medical record data under each medical topic;

[0016] Use different versions of the first data governance tool to govern the first medical record data in the first medical record model, and generate first data storage metadata with a corresponding version of the hybrid data structure;

[0017] Register the first data storage metadata in the data registration center to generate first registration content. The data registration center at least includes a first level and a second level. The first level includes the first data governance tool, and the second level includes the first data storage metadata;

[0018] Use the first medical record model and the first registration content to construct an inverted index, and generate different first data sets divided according to the data source based on the inverted index. The data source includes different first data sets established according to different data centers when different data centers in the same hospital use the same version of the data governance tool and the first medical record model, or different first data sets established according to different hospitals when different hospitals in a region use the same version of the data governance tool and the first medical record model.

[0019] Preferably, it further includes:

[0020] If there are multiple second data sets, create the same index alias for the indexes under the multiple second data sets;

[0021] Establish a mapping relationship between the index alias and the multiple second data sets.

[0022] Preferably, after determining that there are multiple second data sets, it further includes:

[0023] Intercept the query conditions in the query request that damage the data structure of the second medical record data in the multiple second data sets.

[0024] Preferably, converting the index id corresponding to the medical record data id into a medical record storage id by using the mapping relationship includes:

[0025] Obtain the index id corresponding to the medical record data id;

[0026] Based on the mapping relationship, determine the third data set corresponding to the index id;

[0027] Obtain the third registration content of the third medical record data in the data registration center in the third data set, and determine the corresponding id generation rule according to the third registration content;

[0028] Convert the index id into a medical record storage id according to the id generation rule.

[0029] Preferably, obtaining the third registration content pre-stored in the data registration center according to the index id, and constructing a final query condition based on the third registration content and the medical record storage id includes:

[0030] Based on the index id and the mapping relationship, determine the corresponding third data set;

[0031] Based on the third data governance tool and the third medical record model in the third data set, determine the corresponding third data storage metadata;

[0032] In the data registration center, determine the third registration content based on the third data storage metadata;

[0033] Based on the third registration content, identify the storage location and data format of the third medical record data that meet the query condition;

[0034] Concatenate the storage location, data format and medical record storage id of the third medical record data to obtain the final query condition.

[0035] Preferably, querying the database storing the third medical record data based on the final query condition, and merging the queried third medical record data to obtain the final medical record, includes:

[0036] Query the database storing the third medical record data based on the medical record data query field, and obtain the corresponding third medical record data, where the first medical record data includes the third medical record data;

[0037] Convert the data format of the third medical record data to obtain the third medical record data that conforms to the preset standard data format;

[0038] Merge the third medical record data that conforms to the preset standard data format to obtain the final medical record.

[0039] A second aspect of the embodiments of the present invention discloses a processing device for medical record hybrid data, and the processing device includes:

[0040] A first acquisition unit, configured to acquire a query request, and determine a second data set from a pre-constructed first data set based on the query request, where the query request includes query parameters constructed based on a first medical record model, and the first medical record model is constructed by different types of first medical record data under various medical topics, and each of the medical record models corresponds to a type of first medical record data;

[0041] A first establishment unit, configured to establish a mapping relationship between an index name and the second data set based on each index in the second data set, where the mapping relationship is used to indicate the storage location and data format of the second medical record data in the second data set corresponding to the index;

[0042] A first conversion unit, configured to convert the query condition in the query request into a DSL statement, where the DSL statement at least includes a range query statement and a matching query statement;

[0043] A first query unit, configured to query in the index based on the DSL statement, determine a medical record data id, and group the medical record data id according to the corresponding index to obtain a data group;

[0044] A second conversion unit, configured to convert the index id corresponding to the medical record data id into a medical record storage id for each of the data groups by using the mapping relationship;

[0045] A second acquisition unit, configured to acquire third registration content pre-stored in a data registration center according to the index id, and construct a final query condition based on the third registration content and the medical record storage id;

[0046] A second query unit, configured to query a database storing first medical record data based on the final query condition, and merge the queried third medical record data to obtain a final medical record.

[0047] A third aspect of the embodiments of the present invention discloses a computer storage medium, on which a program is stored, and when the program is executed by a processor, it implements the processing method for medical record hybrid data disclosed in the first aspect of the embodiments of the present invention.

[0048] A fourth aspect of the embodiments of the present invention discloses an electronic device, including a memory, a processor, and a program stored on the memory, and the processor executes the program to implement the method for processing medical record hybrid data disclosed in the first aspect of the embodiments of the present invention.

[0049] Based on the method, device, electronic device, and storage medium for processing medical record hybrid data provided in the embodiments of the present invention, the processing method pre - establishes a first medical record model, a first data set, and a data registration center. In the data registration center, the mapping relationship between the first data storage metadata obtained after data governance of the first medical record data and the first medical record model is registered. The first data set is generated based on the inverted index constructed from the first medical record model, the first data storage metadata, and the first registration content in the data registration center. When querying medical records, the second data set initially meeting the query request is determined from the first data set. Based on each index in the second data set, the mapping relationship between the index name and the second data set is established, and the query condition in the query request is converted into a DSL statement; query is performed based on the DSL statement and the mapping relationship to determine the medical record data id, and the index id corresponding to the medical record data id is converted into the medical record storage id; according to the medical record storage id, the third registration content pre - stored in the data registration center is obtained, and the database storing the first medical record data is queried based on the medical record data query field in the third registration content, and the queried third medical record data is merged to obtain the final medical record. In the embodiments of the present invention, for different first medical record models, data after data governance using different versions of the first data governance tool is used to construct a first data set for hybrid inverted index. Taking this first data set as the query basis, during the query process, the query condition in the query request is processed based on this first data set to generate a unified DSL statement for query execution, which can adapt to and take into account different data structures and storage, so as to achieve the purpose of improving the query work efficiency under any unstructured medical record hybrid data structure. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained according to the provided drawings without creative efforts.

[0051] Figure 1 It is a schematic diagram of the processing architecture for medical record hybrid data disclosed in the embodiments of the present invention;

[0052] Figure 2 It is an example diagram of constructing a data set disclosed in the embodiments of the present invention;

[0053] Figure 3 Schematic flow chart of a method for processing hybrid medical record data disclosed in an embodiment of the present invention;

[0054] Figure 4 Schematic structural diagram of a device for processing hybrid medical record data disclosed in an embodiment of the present invention;

[0055] Figure 5 Schematic structural diagram of an electronic device disclosed in an embodiment of the present invention. Detailed implementation manners

[0056] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0057] In the present invention, relational terms such as first and second are only used to distinguish one entity or operation or level from another entity or operation or level, and do not necessarily require or imply any actual relationship or order between these entities or operations. The terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including a..." does not exclude the presence of additional identical elements in the process, method, article or device including the element.

[0058] As known from the background art, for unstructured medical record data, it is necessary to separately encode and maintain codes for each of its storage methods. When specifically querying and applying, it is necessary to adapt to different data storage and data structures, resulting in low query efficiency.

[0059] Therefore, the present invention discloses a processing solution for medical record hybrid data, which is mainly applicable to the medical record query application scenario. By performing data governance on the data obtained after data governance using different versions of the first data governance tool for different first medical record models, a first data set is constructed by hybrid inverted indexing. Using this first data set as the query basis, during the query process, the query conditions in the query request are processed based on this first data set to generate a unified DSL statement for query execution, which can adapt to and balance different data structures and storage, so as to achieve the purpose of improving the query work efficiency under any unstructured medical record hybrid data structure. The specific implementation process is as follows:

[0060] As Figure 1 shown, it is a schematic diagram of the processing architecture of a medical record hybrid data disclosed in an embodiment of the present invention; it mainly includes: a medical record module 10, a data registration center 11, a data set module 12, and a query module 13.

[0061] The medical record module 10 mainly contains a first medical record model constructed based on different types of first medical record data under various medical themes.

[0062] In the medical field, there are different medical themes, and each medical theme contains different types of first medical record data. Based on this, a corresponding first medical record model is constructed for each type of first medical record data under each medical theme.

[0063] For example, in specific applications, there are medical themes such as clinical themes, Internet of Things themes, health resource themes, and bioinformatics themes in the medical field. Each medical theme has different types of data. Therefore, in the present invention, based on different types of first medical record data under each medical theme, a corresponding first medical record model is constructed as the basis for subsequent data governance.

[0064] It should be noted that the first medical record models constructed for different data can be named according to their data characteristics, but they are all first medical record models in essence.

[0065] The following specifically takes the construction of first medical record models corresponding to different types of first medical record data under different medical themes as an example for illustration.

[0066] Example 1: Under the clinical theme, based on the characteristics of clinical medical record data, a clinical research model is constructed. This clinical research model contains documents such as the front page of the medical record, admission record, inspection and examination reports, as shown in Table 1.

[0067] Table 1:

[0068]

[0069] Example 2: Under the clinical theme, a patient portrait model is constructed based on the data characteristics of patients. The patient portrait model contains personal information, disease information, family history, health predictions, etc. This information can exist in the form of documents, as shown in Table 2 specifically.

[0070] Table 2:

[0071]

[0072] Example 3: Under the Internet of Things theme, a monitoring model is constructed based on the patient electrocardiogram monitoring data recorded in the intensive care unit. The monitoring model contains information such as vital signs and electrocardiogram. This information can exist in the form of documents, as shown in Table 3.

[0073] Table 3:

[0074]

[0075] The data registration center 11 is pre-constructed and has at least two levels.

[0076] The data registration center 11 is mainly used for data governance and registration based on the first medical record model constructed in the medical record module 10.

[0077] Specifically, because the first data governance tool is continuously iteratively improved, different version numbers of iterative versions appear. It is precisely because of continuous iterative improvement that a mixed data structure appears.

[0078] In the data registration center 11, the version numbers of different versions of the first data governance tool are used as the first level of the data registration center 11. For example, the first data governance tool with version number v1.0 is denoted as: adcutil1.0. The first data governance tool with the iteratively improved version number v2.0 is denoted as: adcutil2.0.

[0079] In the data registration center 11, after different first medical record models are governed by different versions of the first data governance tool at the first level, the first data storage metadata with a mixed data structure is generated as the second level of the data registration center 11. The first data storage metadata corresponds to the version of the first data governance tool used during governance. For example: currently, a first medical record model is governed by the first data governance tool with version number adcutil1.0, and the obtained first data storage metadata corresponds to the adcutil1.0 version; after the first data governance tool is iterated, the first data governance tool with version number adcutil2.0 is obtained, and the same first medical record model is governed, and the obtained first data storage metadata corresponds to the adcutil2.0 version.

[0080] After generating the first data storage metadata of the hybrid data structure in the data registration center 11, establish the mapping relationship between the first data storage metadata and its corresponding first medical record model, register it in the data registration center 11, and obtain the corresponding first registration content, so that when querying later, based on the mapping relationship registered in the data registration center 11, the field path corresponding to the first medical record model can be found.

[0081] It should be noted that the registration content includes but is not limited to the fields of the first medical record model, the database name, table, data format for storing the first data storage metadata, and the splicing rules of the id, etc. When querying later, based on the first registration content, the query database and table can be automatically identified, and the data of the fields required for the query can be automatically interpreted and identified.

[0082] For example, after the data in the clinical research model is governed by the data governance tool v1.0, the first data storage metadata of the hybrid data structure is generated, specifically represented as: clinicalresearch1.0. The registered path after registration is: adcutil1.0 / clinicalresearch1.0.

[0083] After the data in the patient portrait model is governed by the data governance tool v1.0, the first data storage metadata of the hybrid data structure is generated, specifically represented as: userportrait1.0. The registered path after registration is: adcutil1.0 / userportrait1.0.

[0084] After the data in the wardship model is governed by the data governance tool v1.0, the first data storage metadata of the hybrid data structure is generated, specifically represented as: wardship1.0. The registered path after registration is: adcutil1.0 / wardship1.0.

[0085] The following specifically takes the example of registering and generating the first registration content in the data registration center 11 after data governance based on different first medical record models and the first data governance tool for illustration.

[0086] Example 4: Take the clinical research model as an example.

[0087] Use adcutil 1.0 to perform data governance on the data in the clinical research model. Based on the clinical research model, the storage structure consists of two libraries, one is the historical clinical research medical record library (lishi), and the other is the in-hospital research medical record library (zaiyuan). In each library, various clinical original data tables and data tables after governance are established based on clinical operations. The table should at least include the original medical record front page (binganshouye_src), the medical record front page after governance (binganshouye), the original inspection report (jianyanbaogao_src), the inspection report after governance (jianyanbaogao), the original admission record (ruyuanjilu_src), the admission record after governance (ruyuanjilu), etc.

[0088] The first registration content is the mapping relationship between the first data storage metadata obtained after governance and the corresponding first medical record model. An example of the first registration content is shown in Table 4.

[0089] Table 4:

[0090]

[0091] Use adcutil 2.0 to perform data governance on the data in the clinical research model. Based on the clinical research model, place the historical clinical research medical records and in-hospital research medical records in one library, and the storage structure is one library (dsj). In this library, establish the historical clinical original data table (src), the historical data table after governance (nosrc), the in-hospital clinical original data table (src_inh), and the in-hospital data table after governance (nosrc_inh) based on clinical operations. The first registration content is also the mapping relationship between the first data storage metadata obtained after governance and the corresponding first medical record model. An example of the first registration content is shown in Table 5.

[0092] Table 5:

[0093]

[0094] Use adcutil 3.0 to perform data governance on the data in the clinical research model. Based on the clinical research model, place the historical clinical research medical records and in-hospital research medical records in one library, and the storage structure is one library (dsj). In this library, establish the historical clinical data table (inhistory) and the in-hospital clinical data table (inhospital) based on clinical operations. The first registration content is also the mapping relationship between the first data storage metadata obtained after governance and the corresponding first medical record model. An example of the first registration content is shown in Table 6.

[0095] Table 6:

[0096]

[0097] The data set module 12 has a first data set corresponding to the diverse first medical record models in the medical record module 10. For example, the clinical research model corresponds to the clinical research data set, the patient portrait model corresponds to the patient portrait data set, and the monitoring model corresponds to the monitoring data set.

[0098] The first data set corresponding to the first medical record model records: the first medical record model, the version number of the data governance tool for data governance of the first medical record model, and the index name of the index constructed for the first data storage metadata after data governance of the data in the first medical record model. The index name is unique and is similar to the name of a database.

[0099] Taking the clinical research data set as an example, its example is as follows:

[0100] Data governance tool version number: adcutil3.0

[0101] First medical record model: Clinical research model 1.0

[0102] Index name: dsj_index

[0103] In an embodiment of the present invention, the constructed index is an inverted index. An inverted index is an efficient index structure used to store the association relationship between documents and keywords, including document ID (DocId) and term frequency information (TF) of keywords. The similarity between the document and the query is calculated by applying TF-IDF, BM25 or other term weight calculation methods, and the sorting is performed according to these similarities. It should be noted that the calculation of term weights is dynamically completed during the query.

[0104] Based on the first medical record model, an inverted index is constructed for the first data storage metadata after data governance, which can cover the diverse first medical record models in the medical record module 10 and provide fine-grained multi-dimensional semantic indexes for the data content in different first medical record models.

[0105] When constructing the inverted index, for different semantic features, index creation statements are generated and executed to construct a comprehensive inverted index system. Specifically, the Analyzer component is responsible for performing word segmentation operations on the text and splitting it into tokens / terms. These individual words or phrases obtained after the word segmentation operation are then stored in the inverted index on the disk.

[0106] Taking the department field and the chief complaint field in the clinical research model in the above Example 1 as an example, the department field is "binganshouye.dept"; the chief complaint field is "ruyuanjilu.chief_complaint". The Analyzer component uses specific delimiters (such as the default space, period, word segmentation operation, etc.) to split the text into tokens, and applies specific filters to each token. After this analysis, the tokens are transformed into terms, and these terms are stored in the inverted list for the corresponding fields. The finally formed inverted list contains the terms corresponding to all fields in the clinical research model, and this inverted list has a unique identifier id, that is, the index name.

[0107] It should be noted that a token is each split entry record and contains information such as its position and length in the corresponding field; a term generally refers to the actual term stored in the index, which is the value directly stored and queried without analysis (tokenization) or word segmentation.

[0108] In the present invention, taking the clinical research data set as an example, after the data governance tool adcutil3.0 performs data governance on the clinical research model 1.0 and registers it in the data registration center, the obtained registration path is adcutil3.0 / clinicalresearch1.0.

[0109] As Figure 2 shown, based on the content in Example 1, Example 2 and Example 3 of the present invention, such as data from clinical data sources, Internet of Things data sources and health resource data sources, etc., under the clinical theme and the Internet of Things theme, corresponding clinical research models, patient portrait models and monitoring models are constructed for each type of first medical record data. Based on different versions of the first data governance tool, data governance is performed on the first medical record data in the above clinical research models, patient portrait models and monitoring models, and then registered in the data registration center, and the first data storage metadata obtained after governance is stored in the corresponding database to obtain a hybrid database. An inverted index is constructed for the first data storage metadata based on the above constructed first medical record model, and corresponding first data sets are constructed based on different first medical record models to obtain a clinical research data set, a patient portrait data set and a monitoring data set.

[0110] When the query module 13 receives a query request initiated by a user, it obtains the query information in the query request, constructs query parameters based on the specifications of the first medical record model, and then determines the second dataset involved from the pre-constructed first dataset based on the query parameters. If there are multiple second datasets, it is necessary to intercept the query conditions that disrupt the data structure in the query request. After finally determining one or more second datasets, in the case of a single second dataset, a mapping relationship between the index name and the dataset is established based on the index name in the second dataset. In the case of multiple second datasets, the same index alias is created for the indexes under each second dataset, and a mapping relationship between the index alias and the second dataset is established.

[0111] By creating a concise and clear alias for each involved index, it can simplify the writing of query statements and facilitate the unified management and maintenance of the indexes in the multiple second datasets involved. At the same time, during subsequent query processes, this index alias can be utilized, making it more convenient to reference and operate these indexes.

[0112] The mapping relationship between the index name (index alias) constructed in the query module 13 and the second dataset can be used to determine the corresponding second dataset. Based on the data governance tool version number and the second medical record model included in the second dataset, the storage path of the second data storage metadata after governance by the second data governance tool based on the second medical record model in the data registration center can be determined. Therefore, through the mapping relationship between this index alias and the second dataset, the storage location and data format of the specific data corresponding to each index can be accurately specified.

[0113] Here, taking the query parameters of the clinical research model as an example, the sample of the query parameters based on the clinical research model is as follows:

[0114] {

[0115] "expressions":

[0116] [{

[0117] "field": "Medical record front page_Department",

[0118] "values":

[0119] "Cardiology Department"

[0120] ,

[0121] "exp": "equal to"

[0122] },{

[0123] "field": "Admission record_Chief complaint",

[0124] "values":

[0125] "chest pain"

[0126] ,

[0127] "exp": "include"

[0128] }

[0129] ],

[0130] "size": 10, "page": "0",

[0131] "fields":

[0132] [{

[0133] "field": "test_report_test_item_name"

[0134] }

[0135] ,

[0136] [{

[0137] "field": "test_report_test_sub_item"

[0138] }

[0139] ]}

[0140] The query module 13 is also used to convert the specific content based on the query conditions into a DSL statement, and the DSL statement includes a range query and a match query.

[0141] For example: The query condition included in the query request is to find the medical records of all patients aged between 30 and 50 years old and suffering from a certain specific disease. Then converting this query condition into a DSL statement involves a combination of a range query (for the age field) and a match query (for the disease description field), and the combination is as follows:

[0142] “{"query": { "bool": { "must": [ { "range": { "age": { "gte": 30, "lte": 50}}}, { "match": { "symptom_description": "a certain specific disease"}} ]}}”

[0143] After converting the query conditions into a DSL statement, the query module 13 executes the DSL statement in the index to obtain the medical record data ids that meet the query conditions.

[0144] It should be noted that the medical record data exists in the form of documents, and each medical record document has a unique identification id. This unique identification id serves as not only the medical record data id but also the index id. Therefore, by executing DSL statements in the index, the medical record data id that meets the query conditions can be obtained.

[0145] After obtaining the medical record data id, the query module 13 groups them according to their corresponding indexes to obtain corresponding data groups.

[0146] It should be noted that different indexes correspond to different storage and data formats. By grouping the medical record data ids with the same index together, it is beneficial to query the storage address and data format of the second medical record data based on the mapping relationship between the index name (index alias) and the second data set in the subsequent process. And in the subsequent process, the data obtained after processing different data groups will be merged through heterogeneous merging.

[0147] For example, if there are two indexes, one for inpatient medical records and the other for outpatient medical records, then after grouping the obtained medical record data ids according to these two indexes, subsequent queries and processing can be carried out respectively for the medical record data of inpatients and outpatients.

[0148] After obtaining the data groups, the query module 13, for different data groups, uses the mapping relationship between the index name (index alias) and the second data set to query the second data set corresponding to the medical record data id, determines the second registration content corresponding to the second data set in the data registration center based on the path in the second data set, and converts the index id corresponding to the medical record data id into a medical record storage id based on this second registration content and the id generation rule.

[0149] Among them, the medical record storage id refers to the id of data storage in the database.

[0150] Based on the above-mentioned different versions of the first data governance tool, the registration content of the first data storage metadata after data governance is also different, and the converted different versions of the medical record storage ids are also different. For example:

[0151] When the version number of the first data governance tool is adcutil1.0, the medical record data id is local##2#000000A16100#3, and it is converted into a medical record storage id of local#000000A16100#3, removing the middle outpatient / inpatient identifier.

[0152] When the version number of the first data governance tool is adcutil2.0, the medical record data ID is local##2#000000A16100#3, which is converted into the medical record storage ID local#000000A16100#3#binganshouye. The outpatient / inpatient identifier in the middle is removed, and the specific document name is added at the same time.

[0153] When the version number of the first data governance tool is adcutil3.0, the medical record data ID is ff7eLOCAL##2#T001081004-h9#8, which is converted into the medical record storage ID 76aaLOCAL#T001081004-h9#8#2#binganshouye#T001081004-h9@1. The first four digits of the medical record data ID are deleted, and the first four digits of the medical record storage ID are recalculated and generated. At the same time, the document identifier and the unique identifier of each document data are added.

[0154] The query module 13 is also used to determine the third data set mapped by the index ID in the second data set based on the mapping relationship between the index ID, the index name (index alias), and the second data set, and determine the corresponding third data storage metadata through the third data governance tool and the third medical record model in the third data set. In the data registration center, the corresponding third registration content is determined based on the third data storage metadata; according to the query conditions, the data of the required fields are automatically interpreted and recognized for the third registration content, and the obtained medical record data query fields are concatenated with the medical record storage ID to obtain the final query condition.

[0155] For example, if you want to query fields such as the test details and test values of a certain patient, according to the storage address and data format, combined with the patient's medical record data ID, the final query condition concatenated is:

[0156] "db.getCollection('jianyanbaogao').find({"_id":"BJDXDSYY#000527478900#4"},{"jianyanbaogao.lab_report.lab_sub_item_name":1,"jianyanbaogao.lab_report.lab_qual_result":1})".

[0157] The query module 13 connects to the database, executes the final query condition, and obtains the required third medical record data from the corresponding database. If the obtained third medical record data is in different formats, the obtained third medical record data in different formats is heterogeneously merged to obtain the third medical record data in a unified format, which constitutes the medical record.

[0158] It should be noted that the queried medical record data may come from different databases, which can be non-relational databases for storing medical record data or other types of storage systems. Therefore, heterogeneous merging is required to obtain medical record data in the same data format and then return to form a medical record to ensure the consistency of medical record data for subsequent use and analysis by users.

[0159] It should be noted that the above-mentioned first medical record data, second medical record data, and third medical record data all refer to medical record data. "First", "second", and "third" are only used to distinguish different data ranges. In this application, the third medical record data is included in the second medical record data, and the second medical record data is included in the first medical record data. Other content distinguished by "first", "second", and "third" also has an inclusion relationship.

[0160] Combined with the above data grouping, since the required data versions of each product are inconsistent, during heterogeneous merging, conversion is performed according to the correspondence between the fields required by the product call interface and the data format. For example, the repeatable documents of adcutil3.0 are stored one by one, while adcutil1.0 and adcutil2.0 are stored together. Combined with the data grouping, the documents are merged together one by one through heterogeneous merging.

[0161] In the processing architecture of the medical record hybrid data disclosed in the embodiments of the present invention, for different first medical record models, after using different versions of the first data governance tools to govern the data, a first data set is constructed by performing a hybrid inverted index. Taking this first data set as the query basis, during the query process, the query conditions in the query request are processed based on this first data set to generate a unified DSL statement for query execution, which can adapt to and take into account different data structures and storage, so as to achieve the purpose of improving the query work efficiency under any unstructured medical record hybrid data structure.

[0162] As Figure 3 shown, it is a flowchart of a method for processing medical record hybrid data disclosed in the embodiments of the present invention, mainly including:

[0163] S301: Obtain a query request, and determine a second data set from a pre-constructed first data set based on the query request.

[0164] In step S301, the query request includes a query condition and query parameters constructed based on the first medical record model. The first medical record model is constructed by different types of first medical record data under various medical topics, and each medical record model corresponds to one type of first medical record data; specific examples of the first medical record model can be seen in the above Examples 1 to 3.

[0165] In step S301, the process of pre - constructing the first data set can refer to the process of constructing the data set in the data set module 12 in the above - mentioned Figure 1 wherein.

[0166] In the specific execution of S301, by analyzing parameters such as the field range and data source involved in the query parameters in the query request, a second data set that meets the requirements of the query parameters is determined from the pre - constructed first data set. The first data set contains the second data set.

[0167] S302: Determine whether there are multiple second data sets. If not, execute S303; if so, execute S304.

[0168] S303: Based on each index in the second data set, establish a mapping relationship between the index name and the second data set.

[0169] S304: Create the same index alias for the indexes under multiple second data sets.

[0170] In an embodiment of the present invention, after determining that there are multiple second data sets and before executing S304 to create the same index alias, it further includes: intercepting the query conditions in the query request that damage the data structure of the second medical record data in the multiple second data sets. Based on this, the subsequent query conditions used are the query conditions after interception processing.

[0171] S305: Establish a mapping relationship between the index alias and the multiple second data sets.

[0172] The mapping relationships obtained after executing S303 or S305 are both used to indicate the storage location and data format of the second medical record data in the second data set corresponding to the index.

[0173] S306: Convert the query conditions in the query request into DSL statements.

[0174] In S306, the DSL statements at least include range query statements and match query statements; that is, by converting the query conditions into DSL statements that conform to the DSL syntax, DSL statements involving a combination of range queries and match queries can be obtained.

[0175] S307: Query in the index based on the DSL statements, determine the medical record data id, and group the medical record data id according to the corresponding index to obtain data groups.

[0176] S308: For each data group, use the mapping relationship to convert the index id corresponding to the medical record data id into a medical record storage id.

[0177] In S308, for the mapping relationship here, if S303 is executed above, it indicates the mapping relationship between the index name and the second data set, and if S305 is executed above, it indicates the mapping relationship between the index alias and the second data set.

[0178] During the specific execution of S308, for each data group, the corresponding third data set can be quickly determined according to the index name or index alias through the mapping relationship, and based on the content in the third data set, the data storage address and data format can be determined, which is convenient for subsequent querying to obtain accurate medical record data.

[0179] It should be noted that the third data set is included in the second data set.

[0180] S309: Obtain the third registration content pre-stored in the data registration center according to the index id, and construct the final query condition based on the third registration content and the medical record storage id.

[0181] During the specific execution of S309, determine the corresponding index according to the index id, determine the third data set corresponding to the index id from the second data set according to the mapping relationship between the index name of the index and the second data set, obtain the registration paths of the third data governance tool and the third medical record model in the third data set in the data registration center based on the third data set and the inverted index, then determine the third registration content according to the registration path, and then automatically identify and query the corresponding database and table based on the third registration content, automatically parse and identify and obtain the storage location and data format of the third medical record data that meets the query conditions, and splice the storage location and data format of the third medical record data with the medical record storage id to form the final query condition.

[0182] It should be noted that the aforementioned S308 and S309 are both executed for each data group, and the results obtained in the subsequent execution of S310 are heterogeneously merged.

[0183] S310: Query the database storing the medical record data based on the final query condition, and merge the queried third medical record data to obtain the final medical record.

[0184] During the execution of S310, for the queried third medical record data, if there are different formats, the third medical record data with different formats is converted into the third medical record data with the same format through heterogeneous merging and then merged. Finally, a medical record containing the third medical record data with a unified data format is obtained, that is, the final medical record.

[0185] In the embodiments of the present invention, a unified data format standard can be preset to automatically convert the obtained third medical record data with different data formats.

[0186] For example, part of the data format inspection report is an inspection report. Part of the data divides the inspection report into the integration of inspection report details and the integration of the main inspection report form. Only by integrating the two documents can the data of the inspection report be obtained.

[0187] Therefore, by automatically converting the third medical record data in different data formats into a unified data format, the consistency of medical record data is ensured, which is convenient for subsequent users to use and analyze. At the same time, it is also convenient for the third medical record data to be transmitted and shared between different hospital systems or modules.

[0188] In the method for processing medical record hybrid data disclosed in the embodiments of the present invention, a first data set constructed by performing a hybrid inverted index based on data of different first medical record models that have undergone data governance is used as a query basis. During the query process, the query conditions in the query request are processed based on this first data set to generate a unified DSL statement for query execution, which can adapt to and take into account different data structures and storage, so as to achieve the purpose of improving the query efficiency under any unstructured medical record hybrid data structure.

[0189] In an embodiment of the present invention, the process of pre-constructing the first data set includes:

[0190] S11: Obtain first medical record data of different types under each medical topic, and construct a corresponding medical record model for each type of first medical record data under each medical topic.

[0191] S12: Use first data governance tools of different versions to govern the first medical record data in the first medical record model to generate first data storage metadata in a hybrid data structure.

[0192] S13: Register the first data storage metadata in the data registration center to generate first registration content.

[0193] In S13, the data registration center includes at least a first layer and a second layer. The first layer includes first data governance tools, and the first data governance tools are first data governance tools of different versions. The second layer includes first data storage metadata, and the first data storage metadata corresponds to the version of the first data governance tool used during governance. The specific registration process can also refer to Figure 1 the registration process performed by the digital registration center 12 in

[0194] S14: Use the first medical record model, the first data storage metadata, and the first registration content to construct an inverted index, and generate different first data sets divided according to data sources based on the inverted index.

[0195] In S14, the data sources include different first data sets established according to different data centers when different data centers in the same hospital use the same version of the data governance tool and the first medical record model, or different first data sets established according to different hospitals when different hospitals in a region use the same version of the data governance tool and the first medical record model.

[0196] In the present invention, a first data set is pre-constructed, that is, a hybrid inverted index system is constructed. By constructing this first data set, in the subsequent query process, the registration path of the first data governance tool based on the first medical record model in the data registration center can be obtained based on this first data set.

[0197] In an embodiment of the present invention, the specific process of performing S308 for each data group to convert the index id corresponding to the medical record data id into a medical record storage id by using the mapping relationship includes:

[0198] S21: Obtain the index id corresponding to the medical record data id.

[0199] The medical record data governed by different versions of the first data governance tool, or the medical record data in different data formats, use different medical record data ids when stored. Therefore, S21 is executed to determine the index id corresponding to the medical record data id, so as to subsequently use the inverted index to determine the corresponding third data set according to the index name or index alias, and then determine the storage location and data format of the third medical record data based on the third data set.

[0200] S22: For each data group, based on the mapping relationship, determine the third data set corresponding to the index id.

[0201] In S22, based on the mapping relationship, the third data set corresponding to the index id is determined in the second data set, and this third data set is included in the second data set.

[0202] S23: Obtain the third registration content of the third medical record data in the data registration center, and determine the corresponding id generation rule according to the third registration content.

[0203] In S23, the third registration content includes but is not limited to the fields of the third medical record model, the database name, table, data format for storing the third data storage metadata, and the splicing rule of the id, etc. That is, the id generation rule can be obtained through this registration content. For specific details, please refer to the above Examples 1 to 3.

[0204] S24: Convert the index id into a medical record storage id according to the id generation rule.

[0205] During the execution of S24, the index id corresponding to the third data set is converted into a medical record storage id according to the id generation rule in the third medical record model determined based on the third registration content.

[0206] In an embodiment of the present invention, the specific process of performing S309 to obtain the third registration content pre-stored in the data registration center according to the index id and construct the final query condition based on the third registration content and the medical record storage id includes:

[0207] S31: Determine the corresponding third data set based on the index id and the mapping relationship.

[0208] S32: Determine the corresponding third data storage metadata based on the third data governance tool and the third medical record model in the third data set.

[0209] S33: In the data registration center, determine the third registration content based on the third data storage metadata.

[0210] During the execution of S31 to S33, obtain the registration path of the third medical record data corresponding to the third data set based on the index id, and then determine the third registration content of the third data storage metadata obtained after data governance in the third data set in the data registration center according to the registration path.

[0211] It should be noted that the above S31 to S33 can be executed in combination with or in parallel with the above S23. In the actual application process, after obtaining the third registration content during the execution of S23, S34 and S24 can be branched for execution.

[0212] S34: Identify the third medical record data that meets the query condition based on the third registration content.

[0213] During the specific execution of S34, automatically identify and parse based on the database name, table, data format, id generation rule, etc. stored in the third registration content to obtain the storage location and data format of the third medical record that meet the query condition.

[0214] S35: Concatenate the storage location, data format, and medical record storage id of the third medical record data to obtain the final query condition.

[0215] In an embodiment of the present invention, the specific process of performing S310 to query the database storing the first medical record data based on the final query condition, and merge the queried third medical record data to obtain the final medical record includes:

[0216] S41: Query the database storing the third medical record data based on the final query condition to obtain the corresponding third medical record data.

[0217] S42: Convert the data format of the third medical record data to obtain the third medical record data that conforms to the preset standard data format.

[0218] S43: Merge the third medical record data that conforms to the preset standard data format to obtain the final medical record.

[0219] It should be noted that when merging in this S310, heterogeneous merging needs to be carried out in combination with the data grouping situation.

[0220] In the present invention, since the storage methods of the first data storage metadata generated after data governance by different versions of the first data governance tool may be different, resulting in different data formats, therefore, unified format conversion is performed on the third medical record data with different data formats obtained, to determine data consistency, which provides convenience for subsequent use and analysis.

[0221] Based on the above-mentioned method for processing medical record hybrid data disclosed in the embodiments of the present invention, the present invention also correspondingly discloses a device for processing medical record hybrid data. As Figure 4 shown, the processing device includes: a first acquisition unit 401, a first establishment unit 402, a first conversion unit 403, a first query unit 404, a second conversion unit 405, a second acquisition unit 406, and a second query unit 407.

[0222] The first acquisition unit 401 is configured to acquire a query request, and determine a second data set from a pre-constructed first data set based on the query request. The query request includes a query condition and query parameters constructed based on the first medical record model. The first medical record model is constructed from different types of first medical record data under various medical topics, and each medical record model corresponds to a type of first medical record data.

[0223] The first establishment unit 402 is configured to establish a mapping relationship between the index name and the second data set based on each index in the second data set. The mapping relationship is used to indicate the storage location and data format of the second medical record data in the second data set corresponding to the index.

[0224] The first conversion unit 403 is configured to convert the query condition in the query request into a DSL statement, and the DSL statement at least includes a range query statement and a matching query statement.

[0225] The first query unit 404 is configured to query in the index based on the DSL statement, determine the medical record data id, and group the medical record data id according to the corresponding index to obtain a data grouping.

[0226] The second conversion unit 405 is configured to convert the index id corresponding to the medical record data id into a medical record storage id for each data grouping by using the mapping relationship.

[0227] A second acquisition unit 406, configured to acquire third registration content pre-stored in a data registration center according to an index id, and construct a final query condition based on the third registration content and a medical record storage id.

[0228] A second query unit 407, configured to query a database storing third medical record data based on the final query condition, and merge the queried third medical record data to obtain a final medical record.

[0229] In an embodiment of the present invention, the processing device further includes a preset unit, which is specifically configured to: acquire first medical record data of different types under each medical subject, and construct a corresponding first medical record model for each type of first medical record data under each medical subject; use different versions of a first data governance tool to govern the first medical record data in the first medical record model to generate first data storage metadata with a corresponding version of a hybrid data structure; register the first data storage metadata in a data registration center to generate first registration content, where the data registration center includes at least a first level and a second level, the first level includes the first data governance tool, and the second level includes the first data storage metadata; construct an inverted index using the first medical record model, the first data storage metadata, and the first registration content, and generate different first data sets divided according to data sources based on the inverted index, where the data sources include different first data sets established according to different data centers when different data centers in the same hospital use the same version of the data governance tool and the first medical record model, or different first data sets established according to different hospitals when different hospitals in a region use the same version of the data governance tool and the first medical record model.

[0230] In an embodiment of the present invention, the first establishment unit 402 is further configured to, if there are multiple second data sets, create the same index alias for the indexes under the multiple second data sets. Establish a mapping relationship between the index alias and the multiple second data sets.

[0231] In an embodiment of the present invention, the first establishment unit 402 is further configured to, after determining that there are multiple second data sets, intercept a query condition in a query request that destroys the data structure of the second medical record data in the multiple second data sets.

[0232] In an embodiment of the present invention, the second conversion unit 405 is specifically configured to, for each data group, acquire an index id corresponding to a medical record data id; based on the mapping relationship, determine a third data set corresponding to the index id from the second data set; acquire third registration content of third medical record data in the third data set in the data registration center, and determine a corresponding id generation rule according to the third registration content; convert the index id into a medical record storage id according to the id generation rule.

[0233] In an embodiment of the present invention, the second acquisition unit 406 is specifically configured to, for each data packet, determine a corresponding third data set based on the index id and the mapping relationship; determine corresponding third data storage metadata based on the third data governance tool and the third medical record model in the third data set; in the data registration center, determine third registration content based on the third data storage metadata; identify the storage location and data format of the third medical record data that meet the query conditions based on the third registration content; splice the storage location, data format, and medical record storage id of the third medical record data to obtain a final query condition.

[0234] In an embodiment of the present invention, the second query unit 407 is specifically configured to, for each data packet, query the database storing the third medical record data based on the medical record data query field to obtain the corresponding third medical record data; convert the data format of the third medical record data to obtain third medical record data that conforms to the preset standard data format; merge the third medical record data that conforms to the preset standard data format to obtain a final medical record.

[0235] It should be noted that the above-mentioned processing device for medical record hybrid data disclosed in the embodiments of the present invention can be specifically applied to Figure 1 the disclosed processing architecture of medical record hybrid data.

[0236] In the processing device for medical record hybrid data disclosed in the embodiments of the present invention, the first data set for constructing a hybrid inverted index based on the data of different first medical record models after data governance is used as the query basis. During the query process, the query conditions in the query request are processed based on this first data set to generate a unified DSL statement for query execution, which can adapt to and take into account different data structures and storages, so as to achieve the purpose of improving the query work efficiency under any unstructured medical record hybrid data structure.

[0237] Based on the above-mentioned processing device for medical record hybrid data disclosed in the embodiments of the present disclosure, the above-mentioned various modules can be implemented by a hardware device composed of a processor and a memory. Specifically, the above-mentioned various modules are stored in the memory as program units, and the processor executes the above-mentioned program units stored in the memory to achieve thread control.

[0238] Among them, the processor contains a kernel, and the kernel retrieves the corresponding program unit from the memory. One or more kernels can be set, and the database capacity can be expanded by adjusting the kernel parameters.

[0239] The embodiments of the present invention disclose a computer storage medium, on which a program is stored. When the program is executed by a processor, it implements the processing method for medical record hybrid data as disclosed in the above-mentioned embodiments of the present invention.

[0240] A computer-readable medium includes permanent and non-permanent, removable and non-removable media that can implement information storage by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tapes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media do not include transitory media such as modulated data signals and carrier waves.

[0241] An embodiment of the present invention discloses an electronic device, including a memory, a processor, and a program stored in the memory. The processor executes the above program to implement the processing method of the medical record hybrid data disclosed in the above embodiments of the present invention.

[0242] Specifically, as Figure 5 shown, it is a schematic structural diagram of the electronic device disclosed in the embodiment of the present invention. The electronic device 500 includes at least one processor 501, at least one memory 502 connected to the processor, and a bus 503.

[0243] The processor 501 and the memory 502 complete mutual communication through the bus 503.

[0244] The processor 501 is used to execute the program stored in the memory.

[0245] The memory 502 is used to store the program, which is at least used to implement the processing method of the medical record hybrid data disclosed in the above embodiments of the present invention.

[0246] In a typical configuration, the device includes one or more processors (CPUs), a memory, and a bus. The device may also include an input / output interface, a network interface, etc.

[0247] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM), and / or non-volatile memory in the form of, for example, read-only memory (ROM) or flash RAM (flash memory). The memory includes at least one storage chip. The memory is an example of a computer-readable medium.

[0248] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for a system or system embodiment, since it is basically similar to a method embodiment, the description is relatively simple. For the relevant parts, reference can be made to the description of the method embodiment. The systems and system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. A person of ordinary skill in the art can understand and implement it without creative work.

[0249] Those skilled in the art can further realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described according to functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Skilled professionals can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0250] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for processing mixed data of medical records, characterized in that The processing method includes: Obtain a query request, and determine a second data set from a pre-constructed first data set based on the query request. The query request includes a query condition and query parameters constructed based on a first medical record model, and the first medical record model is constructed from different types of first medical record data under various medical topics. Each first medical record model corresponds to a type of first medical record data; Based on each index in the second data set, establish a mapping relationship between the index name and the second data set. The mapping relationship is used to indicate the storage location and data format of the second medical record data in the second data set corresponding to the index; Convert the query condition in the query request into a DSL statement. The DSL statement at least includes a range query statement and a matching query statement; Query in the index based on the DSL statement to determine the medical record data id, and group the medical record data id according to the corresponding index to obtain a data group; For each data group, use the mapping relationship to convert the index id corresponding to the medical record data id into a medical record storage id; Obtain the third registration content pre-stored in the data registration center according to the index id, and construct a final query condition based on the third registration content and the medical record storage id; Query the database storing the third medical record data based on the final query condition, and merge the queried third medical record data to obtain a final medical record.

2. The processing method according to claim 1, wherein The process of pre-constructing the first data set includes: Obtain different types of first medical record data under each medical topic, and construct a corresponding first medical record model for each type of first medical record data under each medical topic; Govern the first medical record data in the first medical record model using different versions of the first data governance tool to generate first data storage metadata with a corresponding version of a hybrid data structure; Register the first data storage metadata in the data registration center to generate first registration content. The data registration center at least includes a first level and a second level. The first level includes the first data governance tool, and the second level includes the first data storage metadata; Construct an inverted index using the first medical record model and the first registration content, and generate different first data sets divided according to the data source based on the inverted index. The data source includes different first data sets established according to different data centers when different data centers in the same hospital use the same version of the data governance tool and the first medical record model, or different first data sets established according to different hospitals when different hospitals in a region use the same version of the data governance tool and the first medical record model.

3. The processing method according to claim 1, wherein It also includes: If there are multiple second data sets, create the same index alias for the indexes under the multiple second data sets; Establish a mapping relationship between the index alias and the multiple second data sets.

4. The processing method according to claim 3, characterized in that, After determining that there are multiple second data sets, it also includes: Intercept the query condition in the query request that destroys the data structure of the second medical record data in the multiple second data sets.

5. The processing method according to any one of claims 1 to 4, characterized in that, Converting the index ID corresponding to the medical record data ID into a medical record storage ID by using the mapping relationship includes: Obtaining the index ID corresponding to the medical record data ID; Based on the mapping relationship, determining the third data set corresponding to the index ID; Obtaining the third registration content of the third medical record data in the data registration center in the third data set, and determining the corresponding ID generation rule according to the third registration content; Converting the index ID into a medical record storage ID according to the ID generation rule.

6. The processing method according to any one of claims 1 to 4, characterized in that, Obtaining the third registration content pre-stored in the data registration center according to the index ID, and constructing a final query condition based on the third registration content and the medical record storage ID, including: Determining the corresponding third data set based on the index ID and the mapping relationship; Based on the third data governance tool and the third medical record model in the third data set, determining the corresponding third data storage metadata; In the data registration center, determining the third registration content based on the third data storage metadata; Identifying the storage location and data format of the third medical record data that meets the query condition based on the third registration content; Concatenating the storage location, data format, and medical record storage ID of the third medical record data to obtain a final query condition.

7. The processing method according to any one of claims 1 to 4, characterized in that, Querying the database storing the third medical record data based on the final query condition, and merging the queried third medical record data to obtain a final medical record, including: Querying the database storing the third medical record data based on the medical record data query field to obtain the corresponding third medical record data, where the first medical record data includes the third medical record data; Converting the data format of the third medical record data to obtain the third medical record data that conforms to the preset standard data format; Merging the third medical record data that conforms to the preset standard data format to obtain a final medical record.

8. A processing device for medical record mixed data, characterized in that, The processing device includes: A first acquisition unit, configured to acquire a query request, and determine a second data set from a pre-constructed first data set based on the query request. The query request includes query parameters constructed based on a first medical record model, and the first medical record model is constructed by different types of first medical record data under various medical topics, and each medical record model corresponds to one type of first medical record data; A first establishment unit, configured to establish a mapping relationship between the index name and the second data set based on each index in the second data set. The mapping relationship is used to indicate the storage location and data format of the second medical record data in the second data set corresponding to the index; A first conversion unit, configured to convert the query condition in the query request into a DSL statement, where the DSL statement at least includes a range query statement and a matching query statement; A first query unit, configured to query in the index based on the DSL statement to determine a medical record data ID, and group the medical record data IDs according to the corresponding index to obtain a data group; A second conversion unit, configured to convert the index ID corresponding to the medical record data ID into a medical record storage ID for each data group by using the mapping relationship; A second acquisition unit, configured to obtain third registration content pre-stored in a data registration center according to the index id, and construct a final query condition based on the third registration content and the medical record storage id; A second query unit, configured to query a database storing first medical record data based on the final query condition, and merge the queried third medical record data to obtain a final medical record.

9. A computer storage medium, on which a program is stored, characterized in that, When the program is executed by a processor, it implements the method for processing medical record hybrid data according to any one of claims 1 to 7.

10. An electronic device, comprising a memory, a processor, and a program stored on the memory, characterized in that, The processor executes the program to implement the method for processing medical record hybrid data according to any one of claims 1 to 7.

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