CN112711581B - Medical data checking method and device, electronic equipment and storage medium - Google Patents
Medical data checking method and device, electronic equipment and storage medium Download PDFInfo
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- G06—COMPUTING; CALCULATING OR COUNTING
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- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/21—Design, administration or maintenance of databases
- G06F16/215—Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/23—Updating
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- G06F40/284—Lexical analysis, e.g. tokenisation or collocates
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- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
Abstract
The embodiment of the disclosure provides a medical data verification method, a medical data verification device, electronic equipment and a computer-readable storage medium, belonging to the technical field of medical data, wherein the medical data verification method comprises the following steps: acquiring a current quality control demand, splitting and analyzing the current quality control demand, and determining a plurality of types of quality control rules corresponding to the current quality control demand; performing logic combination on the multiple types of quality control rules, and determining a target rule set corresponding to the multiple types of quality control rules according to a combination result; and calling the target rule set to check the medical data to be processed from the multiple data sources in parallel to obtain a checking result meeting the current quality control requirement. The embodiment of the disclosure can improve the efficiency of data verification.
Description
Technical Field
The embodiment of the disclosure relates to the technical field of medical data, in particular to a medical data checking method, a medical data checking device, electronic equipment and a computer-readable storage medium.
Background
In order to improve the quality and accuracy of data, it is often necessary to verify the data of different organizations.
In the related art, all the rules in the quality control rules are generally called integrally, so that the data is verified according to all the quality control rules. Moreover, since data has multiple modes, it is necessary to perform targeted verification using different quality control rules for data of different modes, and it is not possible to verify data of multiple modes at the same time. In the above manner, the efficiency of verifying the data by integrally calling the quality control rule is low, and different data needs to be verified in a targeted manner, so that certain limitations are provided.
It is to be noted that the information disclosed in the above background section is only for enhancement of understanding of the background of the present invention and therefore may include information that does not constitute prior art known to a person of ordinary skill in the art.
Disclosure of Invention
An object of the embodiments of the present disclosure is to provide a medical data verification method, a medical data verification apparatus, an electronic device, and a computer-readable storage medium, so as to overcome the problems of low data verification efficiency and limitation to at least some extent.
Additional features and advantages of the disclosed embodiments will be set forth in the detailed description which follows, or may be learned by practice of the invention.
According to an aspect of an embodiment of the present disclosure, there is provided a medical data verification method including: acquiring a current quality control demand, splitting and analyzing the current quality control demand, and determining a plurality of types of quality control rules corresponding to the current quality control demand; performing logic combination on the multi-class quality control rules, and determining a target rule set corresponding to the multi-class quality control rules according to a combination result; and calling the target rule set to check the medical data to be processed from the plurality of data sources in parallel to obtain a checking result meeting the current quality control requirement.
In an exemplary embodiment of the present disclosure, the splitting and analyzing the current quality control requirement, and determining multiple types of quality control rules corresponding to the current quality control requirement include: and splitting and analyzing the current quality control requirement through a rule classification model, and determining the multi-class quality control rules.
In an exemplary embodiment of the present disclosure, the method further comprises: and constructing the rule classification model according to the attribute information of a plurality of reference quality control rules in the rule base.
In an exemplary embodiment of the disclosure, the attribute information includes one or more of a combination of rule source, verification purpose, verification dataset attribute, life cycle, and degree of automation.
In an exemplary embodiment of the present disclosure, the determining the multiple types of quality control rules by splitting and analyzing the current quality control requirement through a rule classification model includes: extracting keywords from the current quality control requirement to determine target keywords, and determining model attributes corresponding to the target keywords; and determining the multi-class quality control rules according to the model attributes.
In an exemplary embodiment of the present disclosure, the logically combining the multiple types of quality control rules and determining a target rule set corresponding to the multiple types of quality control rules according to a combination result includes: determining labels corresponding to the multiple types of quality control rules; and combining the labels corresponding to the multiple types of quality control rules to obtain the target rule set.
In an exemplary embodiment of the present disclosure, the combining the labels corresponding to the multiple classes of quality control rules to obtain the target rule set includes: and logically combining the labels corresponding to the multiple types of quality control rules according to the logical relationship in the current quality control requirement, and logically calculating according to the priority order among the logical relationships to obtain the target rule set.
According to an aspect of the present disclosure, there is provided a medical data verification apparatus including: the rule determining module is used for acquiring the current quality control requirement, splitting and analyzing the current quality control requirement and determining a plurality of types of quality control rules corresponding to the current quality control requirement; the rule combination module is used for logically combining the multi-class quality control rules and determining a target rule set corresponding to the multi-class quality control rules according to a combination result; and the data verification module is used for calling the target rule set to verify the medical data to be processed from the plurality of data sources in parallel so as to obtain a verification result meeting the current quality control requirement.
According to an aspect of the present disclosure, there is provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a medical data verification method as in any one of the above.
According to an aspect of the present disclosure, there is provided an electronic device including: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform any of the medical data verification methods described above via execution of the executable instructions.
In the medical data verification method, the medical data verification device, the electronic equipment and the computer-readable storage medium provided by the embodiment of the disclosure, the current quality control requirement is split and analyzed to obtain the corresponding multi-class quality control rules, and then the target rule set is determined based on the multi-class quality control rules, so that the target rule set is called to verify the medical data to be processed of a plurality of data sources in parallel, and the verification result meeting the current quality control requirement is obtained. On the one hand, as part of the target rule set can be determined to check the medical data to be processed, the operation of checking the data by integrally calling all the quality control rules is avoided, the operation amount is reduced, the flexibility is increased, and the checking efficiency is improved. On the other hand, the same target rule set can be called for the medical data to be processed of a plurality of data sources to be checked in parallel, so that the limitation is avoided, the universality of data checking is improved, the medical data to be processed of different modalities of different data sources can be checked flexibly and conveniently without specific checking, the effective scheduling of quality control rules is realized, and the application range is enlarged.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention. It is obvious that the drawings in the following description are only some embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from them without inventive effort. In the drawings:
fig. 1 schematically illustrates a system architecture diagram for performing a medical data verification method according to an embodiment of the present disclosure.
Fig. 2 schematically shows a flow chart of a medical data verification method according to an embodiment of the present disclosure.
Fig. 3 schematically illustrates a flow chart of determining multi-class quality control rules according to an embodiment of the disclosure.
Fig. 4 schematically shows a flow chart of logical combination in the embodiment of the present disclosure.
Fig. 5 schematically illustrates an application scenario diagram of data verification in the embodiment of the present disclosure.
Fig. 6 schematically shows an overall flow chart of medical data verification in an embodiment of the present disclosure.
Fig. 7 schematically shows a block diagram of a medical data verification device according to an embodiment of the present disclosure.
Fig. 8 schematically shows a block diagram of an electronic device for implementing the medical data verification method described above.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to provide a thorough understanding of embodiments of the invention. One skilled in the relevant art will recognize, however, that the invention may be practiced without one or more of the specific details, or with other methods, components, devices, steps, and so forth. In other instances, well-known methods, devices, implementations or operations have not been shown or described in detail to avoid obscuring aspects of the invention.
The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.
The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
A system architecture diagram for performing the medical data verification method in an embodiment of the present disclosure is schematically illustrated in fig. 1.
As shown in fig. 1, the system architecture 100 may include a first end 101, a network 102, and a second end 103. The first end 101 may be a client, and may be various handheld devices (smart phones) having a computing function and a data processing function, a desktop computer, and the like. The network 102 is used to provide a medium for a communication link between the first end 101 and the second end 103, the network 102 may include various connection types, such as a wired communication link, a wireless communication link, and the like, and in the embodiment of the present disclosure, the network 102 between the first end 101 and the second end 103 may be a wired communication link, for example, the communication link may be provided through a serial connection line, or may be a wireless communication link, and the communication link may be provided through a wireless network. The second end 103 may be a client, for example, a terminal device with a data processing function, such as a portable computer, a desktop computer, a smart phone, and the like, and is configured to perform a verification process on data input by the first end. When the first end and the second end are both clients, the first end and the second end may be the same client. The second end may also be a server, which is not limited herein.
It should be understood that the number of first ends, networks and second ends in fig. 1 is merely illustrative. There may be any number of clients, networks, and servers, as desired for an implementation.
It should be noted that the medical data verification method provided by the embodiment of the present disclosure may be completely executed by the second end, and accordingly, the medical data verification device may be disposed in the second end 103.
Based on the system architecture, the embodiment of the present disclosure provides a medical data verification method, which can be applied to an application scenario in which medical data is verified to obtain data meeting quality control requirements. The first end acquires medical data to be processed of the medical institution and sends the medical data to the second end, so that the second end verifies the medical data to be processed. The second end can obtain a quality control target, analyze the quality control target through a rule classification model and determine a plurality of types of quality control rules required by the quality control target; furthermore, multiple types of quality control rules can be combined to obtain a target rule set, and the target rule set is called to perform data verification on the medical data to be processed of multiple data sources, so that data verification results conforming to quality control targets are obtained. Subsequently, the verification result can also be sent to the first end for display or subsequent processing.
The executing subject of the medical data verification method may be a server or a terminal with computing capability, and as shown in fig. 2, the medical data verification method includes steps S210 to S230, which are described in detail as follows:
in step S210, a current quality control requirement is acquired, and the current quality control requirement is split and analyzed, and a plurality of types of quality control rules corresponding to the current quality control requirement are determined;
in step S220, performing logical combination on the multiple types of quality control rules, and determining a target rule set corresponding to the multiple types of quality control rules according to a combination result;
in step S230, the target rule set is called to perform parallel verification on the medical data to be processed from multiple data sources, so as to obtain a verification result meeting the current quality control requirement.
In the medical data verification method provided by the embodiment of the disclosure, on one hand, since part of the target rule set can be determined to verify the medical data to be processed, the operation of integrally calling all quality control rules to verify the data is avoided, the operation amount is reduced, the flexibility is increased, and the verification efficiency is improved. On the other hand, the same target rule set can be called for the medical data to be processed of the multiple data sources to be checked in parallel, so that the limitation is avoided, the universality of data checking is improved, the medical data to be processed of different modalities of different data sources can be checked flexibly and conveniently without performing targeted checking, the effective scheduling of quality control rules is realized, and the application range is enlarged.
Next, the medical data verification method in the embodiment of the present disclosure is explained in detail with reference to the drawings.
In step S210, a current quality control requirement is obtained, and the current quality control requirement is split and analyzed, and a plurality of types of quality control rules corresponding to the current quality control requirement are determined.
In the embodiment of the present disclosure, the current quality control requirement refers to a quality control target or a quality control requirement of the data quality control at this time, and the current quality control requirement may be changed according to an actual requirement, and is not fixed. For example, in a certain project, a business party needs to perform lung cancer specific disease research. The quality control aims at performing quality inspection on lung cancer related data integrity indexes of multi-system (his, lis and emr) data of multiple hospitals in a cleaned data aggregation layer (schema layer) at the same time. Thus, the current quality control requirements can be considered as: and meanwhile, the quality inspection of the lung cancer related data integrity indexes is carried out on the cleaned data aggregation layer (schema layer) of the multi-system (his, lis, emr) data of multiple hospitals. The current quality control requirement may include multiple types of quality control rules, and may be obtained by splitting and analyzing. The quality control rule refers to a rule for expressing the service logic and the medical logic of the medical information system according to a specific model frame and grammatical requirements and verifying and checking the data quality of each link of data processing. For each current quality control requirement, the multi-class quality control rules are used for describing the quality control requirements from different dimensions, and the corresponding multi-class quality control rules are fixed and unchanged.
When the multi-class quality control rules are obtained, the current quality control requirement can be split and analyzed through the rule classification model, and therefore the multi-class quality control rules corresponding to the current quality control requirement are obtained. The rule classification model can be used for accurately analyzing and classifying the current quality control requirements. The rule classification model may be specifically constructed according to attribute information of a plurality of reference quality control rules in the rule base. The rule base refers to a database for storing all quality control rules, wherein a plurality of reference quality control rules may be the quality control rules which have been determined and can be used for executing the quality control. The attribute information may include, but is not limited to, one or more of a rule source, a verification purpose, a verification dataset attribute, a life cycle, a degree of automation. Wherein, the rule source can be specifically represented by table/field attributes, and the verification purpose is specifically represented by a problem type. Specifically, a plurality of classifications can be obtained by performing classification definition on a plurality of reference quality control rules according to attribute information such as rule sources, verification purposes, verification data set attributes, life cycles and automation degrees, each classification corresponds to an attribute label, and a rule classification model can be further determined according to a classification definition result. Therefore, the rule classification model is a model for representing attribute information that is abstracted from a plurality of attribute information.
While the rule classification model is constructed according to the attribute information, the model attribute of the rule classification model can be determined for the rule classification model. Specifically, the model attributes of the rule classification model may include both generic attributes and professional attributes. Generic attributes include, but are not limited to: table/field attributes, problem type, service data layer, service production link, production purpose, applicable metadata version, site organization type, supported applications, status, degree of automation, etc. Professional attributes include, but are not limited to: a diagnostic domain and a therapeutic domain, which in turn comprise a plurality of subdomains, respectively. Meanwhile, the model attribute can be provided with a custom label so as to manage other types of quality control rules except the types.
It is added that each model attribute defines a plurality of functions and grammar templates. In addition to the existing quality control rules, in order to improve the integrity and comprehensiveness of the quality control rules, in the embodiment of the present disclosure, new quality control rules may be created, so as to update the quality control rules in time. Specifically, the quality control rule may be newly built according to the classification method of the model attributes and the grammar template corresponding to each model attribute. For example, one model attribute may be selected as the attribute to be processed, and further, a function and a grammar template corresponding to the attribute to be processed may be called, and the grammar template is automatically filled with data to generate the quality control rule corresponding to the attribute to be processed, so that the quality control rule corresponding to each model attribute can be quickly and accurately established. Meanwhile, model attribute labels can be generated according to the newly established quality control rules, and screening, modification and maintenance can be further carried out according to various model attribute labels.
Fig. 3 is a flow chart schematically illustrating the determination of multi-class quality control rules, and referring to fig. 3, the method mainly includes the following steps:
in step S310, keyword extraction is performed on the current quality control requirement to determine a target keyword, and a model attribute corresponding to the target keyword is determined.
In step S320, the multi-class quality control rules are determined according to the model attributes.
In the embodiment of the disclosure, the text information corresponding to the current quality control requirement can be acquired, and the keyword extraction is performed on the text information corresponding to the current quality control requirement, so as to determine the target keyword corresponding to the current quality control requirement. In particular, the text information may be keyword extracted according to a keyword extraction algorithm. The keyword extraction algorithm may include, but is not limited to, keyword extraction based on statistical features, keyword extraction based on a word graph model, keyword extraction algorithm based on a topic model, and the like as long as a target keyword can be extracted.
For example, the current quality control requirements are: the business side needs to research the special lung cancer diseases, and the quality control aims to simultaneously perform quality inspection on the lung cancer related data integrity indexes of the data of multiple systems (his, lis and emr) in multiple hospitals in a cleaned data aggregation layer (schema layer). Therefore, the target keywords can be extracted from the target keywords, and the target keywords comprise: multiple systems, after cleaning, data aggregation layer, lung cancer, integrity index, special disease research, and the like.
After the target keyword is obtained, the target keyword can be matched with all model attributes corresponding to the rule classification model to determine the model attribute corresponding to the target keyword. For example, the model attribute corresponding to the cleaned target keyword is a production link, and the model attribute corresponding to the target keyword data aggregation layer is a service data layer, and the like.
Further, the multi-class quality control rules can be determined according to the model attributes corresponding to the target keywords. The multi-class quality control rule refers to a rule range required by the current quality control requirement. The multiple classes of quality control rules may include generic attributes and professional attributes. Wherein, there is a one-to-one correspondence between the model attributes and the quality control rules. After the target keywords are extracted, the corresponding model attributes can be determined, and then the quality control rules required by the current quality control requirements can be determined according to the one-to-one correspondence relationship between the model attributes and the quality control rules.
For example, the current quality control requirements are: the business side needs to research the special lung cancer diseases, and the quality control aims to simultaneously carry out quality check on the lung cancer related data integrity indexes of the data of multiple systems (his, lis and emr) in multiple hospitals in a cleaned data aggregation layer (schema layer). After the target keyword is extracted, a plurality of types of quality control rules corresponding to the current quality control requirement can be determined, the plurality of types of quality control rules are used for describing a rule range required by the current quality control requirement, and the plurality of types of quality control rules comprise general attributes and professional attributes, wherein:
the generic attributes include: table attribute: the rules to which his, lis, emr relate; the type of problem: integrity problem rules; service data layer: schema layer rules; and (3) a production link: the rule after cleaning; the supported applications are: special disease scientific research rules; degree of automation: and (4) automatic rules. Professional attributes include: a diagnostic domain: lung cancer is a rule.
In the embodiment of the disclosure, the current quality control requirement is split and analyzed through the rule classification model, so that multiple types of quality control rules corresponding to the current quality control requirement can be quickly and accurately obtained, and the accuracy of analyzing the quality control rules is improved.
In step S220, the multiple types of quality control rules are logically combined, and a target rule set corresponding to the multiple types of quality control rules is determined according to a combination result.
In the embodiment of the disclosure, after obtaining the multiple types of quality control rules, one or more of the labels of the multiple types of quality control rules may be selected to determine the target rule set. Specifically, whether to select one tag or multiple tags may be determined based on the complexity of the current quality control requirements. When the complexity level is less than the complexity threshold, a tag may be selected; when the complexity level is greater than a complexity threshold, multiple label combinations may be selected. Wherein the complex threshold may be determined by a quality control dimension included in the current quality control requirement. The more quality control dimensions, the greater the complexity. In the embodiment of the present disclosure, an example in which a target rule set is determined by a combination of a plurality of tags is described. The obtained multiple types of quality control rules can be logically combined to combine the multiple types of quality control rules into an integral target rule set, so that the target rule set comprises all the analyzed quality control rules.
For example, a multi-tag Boolean logical assembly may include: tag 1and tag 2; tag 1or tag 2; tag 1not tag 2; the labels 1and 2not 3 and the like can be used for quickly locking corresponding target rule sets according to different quality control requirements.
The flow chart for performing the logical combination is schematically shown in fig. 4, and referring to the flow chart shown in fig. 4, mainly includes the following steps:
in step S410, the labels corresponding to the multiple types of quality control rules are determined.
In this step, each type of quality control rule corresponds to a label for describing the attribute of the quality control rule, and the label can be consistent with the quality control rule. For example, the tag of the integrity issue rule is an integrity issue.
In step S420, the labels corresponding to the multiple types of quality control rules are combined to obtain the target rule set.
In this step, when multiple types of quality control rules are combined, the labels corresponding to each type of quality control rule may be combined to obtain a combined result, and the target rule set is further determined according to the combined result. When the labels corresponding to each type of quality control rule are combined, firstly, the logic relationship analyzed from the current quality control requirement can be obtained, and the logic relationship includes but is not limited to and, not, or and the like. Further, the labels corresponding to the multiple types of quality control rules can be logically combined according to the logical relationship in the current quality control requirement. For example, the tags corresponding to the multiple types of quality control rules may be combined according to the arrangement order of the analyzed target keywords in the current quality control requirement, or the tags of the multiple types of quality control rules may be combined according to a random order, which is not limited herein, as long as the tags of all the analyzed quality control rules are completely combined. And then, carrying out logic calculation according to the priority sequence among the logic relations to obtain the target rule set. There is a calculated priority order between logical relationships, and the priority order from high to low is: not, and, or. Based on the priority, the labels of the multiple types of quality control rules can be logically calculated according to the priority order, so that an integral target rule set formed by combining the multiple types of quality control rules is obtained. The target rule set herein may include all rule ranges of the current quality control requirements, and thus has comprehensiveness and completeness.
For example, boolean logic combination is performed on the multiple types of quality control rules, and the obtained target rule set is: module (his or lis or emr) and integrity problems and before schema layer and automated not cleaning. Therefore, the target rule set of the quality control can be quickly locked, and the efficiency of determining the target rule set is improved.
Next, in step S230, the target rule set is called to perform parallel verification on the medical data to be processed from the multiple data sources, so as to obtain a verification result meeting the current quality control requirement.
In the embodiment of the present disclosure, multiple data sources may be used to represent sources of data, and specifically, different application scenarios may be included. Different application scenarios may include, but are not limited to, different organizations, different modalities, different production links, different data sets, different application quality requirements, and may be, for example, application scenarios such as upper-level scientific research, management, prediction, and the like. The description here takes a plurality of data sources as an example of different mechanisms. The different institutions may be a plurality of hospitals or medical institutions. The Medical data to be processed may be data stored in EMRs (Electronic Medical Record, computerized hospital Medical Record systems) of respective Medical institutions. The medical institution may be a hospital or a medical center or the like that can be used for medical treatment. The medical data to be processed of the different data sources may include a plurality of modalities, a modality referring to a source or form of each data. The target rule sets of the multiple data sources may be the same or different, and are determined according to the current quality control requirements of each data source.
The pending medical data of different data sources may be stored in a distributed manner in a private cloud, i.e. in servers of the respective medical institutions. The current quality control requirement is issued from a public cloud and sent to a private cloud corresponding to each data source. When the current quality control requirement is received, the private cloud can call a target rule set corresponding to the current quality control requirement to check the to-be-processed medical data of the multiple data sources simultaneously in parallel, so that a check result meeting the target rule set in each data source is obtained, and a check result meeting the current quality control requirement is obtained.
Referring to fig. 5, a schematic diagram of an application scenario of data verification is schematically shown, where a public cloud 510 sends a current quality control requirement, specifically, a private cloud 521 of a data source 1 sends a current quality control requirement 5210, a private cloud 522 of a data source 2 sends a current quality control requirement 5220, and a private cloud 523 of a data source 3 sends a current quality control requirement 5230. It should be noted that the public cloud 510 may simultaneously send respective corresponding current quality control requirements to the private cloud 521 and the private cloud 522, and the private cloud 521 and the private cloud 522 may respond to the received current quality control requirements and concurrently invoke a target rule set corresponding to the current quality control requirements, so as to verify respective medical data to be processed, and obtain corresponding verification results. For example, in response to the current quality control requirement 5210, the private cloud 521 invokes a target rule set 1 corresponding to the current quality control requirement 5210 to check the to-be-processed medical data a stored in the private cloud 5210, so as to obtain a check result 1. In response to the current quality control requirement 5220, the private cloud 522 invokes a target rule set 2 corresponding to the current quality control requirement 5220 to verify the to-be-processed medical data B stored in the private cloud 5220, so as to obtain a verification result 2. In response to the current quality control requirement 5230, the private cloud 523 invokes the target rule set 3 to check the to-be-processed medical data C stored in the private cloud 5230, so as to obtain a check result 3.
In the embodiment of the disclosure, for regional data integration, a scene that multiple medical institutions need to simultaneously perform data quality verification according to the same quality control rule is supported, parallel verification can be realized by only starting one task, and the efficiency of performing data quality control on different data sources is improved.
Fig. 6 schematically shows an overall flow chart for performing data quality control, and referring to fig. 6, the overall flow chart mainly includes the following steps:
in step S601, a rule classification model is constructed. And establishing a rule establishing, maintaining and managing system taking the rule classification model as a core.
In step S602, rule creation is performed according to the model framework and the grammar template, and quality control rules are maintained and managed in a label manner.
In step S603, the multi-class quality control rules are flexibly invoked through the tag/tag set assembly.
In step S604, the scheduling rules may simultaneously support multiple mechanism multiple data sets for quality control verification. That is, after the target rule set is selected, a multi-organization database and a multi-database can be simultaneously selected, and the back end simultaneously distributes task information and rule set configuration parameters to the corresponding clusters for verification, so that the corresponding target rule set is called according to the configuration parameters to perform data quality control.
According to the technical scheme, aiming at multi-mode data aggregation, under the conditions of different mechanisms, different production links, different data types and different application quality requirements, part of quality control rules can be quickly locked in a large number of quality control rules in a flexible tag/tag set assembly mode to be used as a target rule set to be executed in parallel, the problem that all quality control rules can only be integrally called is avoided, the target rule set can be flexibly called, and the dispatched target rule set can simultaneously support different data sets to perform quality verification in parallel. Moreover, the method avoids long time consumption and resource waste of data verification, saves calculation resources and time cost, improves the accuracy of data verification, can realize personalized and efficient flexible scheduling and management of medical data quality control rules according to needs, and increases the application range.
In an embodiment of the present disclosure, there is also provided a medical data verification apparatus, and referring to fig. 7, the medical data verification apparatus 700 may include the following modules:
a rule determining module 701, configured to obtain a current quality control requirement, split and analyze the current quality control requirement, and determine multiple types of quality control rules corresponding to the current quality control requirement;
a rule combination module 702, configured to perform logical combination on the multiple types of quality control rules, and determine a target rule set corresponding to the multiple types of quality control rules according to a combination result;
and the data verification module 703 is configured to invoke the target rule set to perform parallel verification on the medical data to be processed from the multiple data sources, so as to obtain a verification result meeting the current quality control requirement.
In an exemplary embodiment of the present disclosure, the rule determining module includes: and the analysis module is used for carrying out resolution on the current quality control requirement through a rule classification model and determining the multi-class quality control rules.
In an exemplary embodiment of the present disclosure, the apparatus further includes: and the classification model building module is used for building the rule classification model according to the attribute information of the plurality of reference quality control rules in the rule base.
In an exemplary embodiment of the present disclosure, the attribute information includes one or more of a combination of rule source, verification purpose, verification dataset attribute, life cycle, and degree of automation.
In an exemplary embodiment of the present disclosure, the parsing module includes: the keyword extraction module is used for extracting keywords from the current quality control requirement to determine target keywords and determining model attributes corresponding to the target keywords; and the quality control rule determining module is used for determining the multi-class quality control rules according to the model attributes.
In an exemplary embodiment of the present disclosure, the rule combining module includes: the label acquisition module is used for determining labels corresponding to the multi-class quality control rules; and the label combination module is used for combining labels corresponding to the multiple types of quality control rules to obtain the target rule set.
In an exemplary embodiment of the disclosure, the tag combination module is configured to: and logically combining the labels corresponding to the multiple types of quality control rules according to the logical relationship in the current quality control requirement, and logically calculating according to the priority order among the logical relationships to obtain the target rule set.
It should be noted that, the functional modules of the medical data verification apparatus according to the embodiment of the present disclosure are the same as the steps of the exemplary embodiment of the medical data verification method, and therefore, the description thereof is omitted here.
It should be noted that although in the above detailed description several modules or units of the device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functions of two or more modules or units described above may be embodied in one module or unit according to an embodiment of the invention. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units.
Moreover, although the steps of the methods of the present invention are depicted in the drawings in a particular order, this does not require or imply that the steps must be performed in this particular order, or that all of the depicted steps must be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken into multiple step executions, etc.
In an exemplary embodiment of the present invention, there is also provided an electronic device capable of implementing the above method.
As will be appreciated by one skilled in the art, aspects of the present invention may be embodied as a system, method or program product. Thus, various aspects of the invention may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, microcode, etc.), or an embodiment combining hardware and software aspects that may all generally be referred to herein as a "circuit," module "or" system.
An electronic device 800 according to this embodiment of the invention is described below with reference to fig. 8. The electronic device 800 shown in fig. 8 is only an example and should not bring any limitations to the functionality and scope of use of the embodiments of the present disclosure.
As shown in fig. 8, electronic device 800 is in the form of a general purpose computing device. The components of the electronic device 800 may include, but are not limited to: the at least one processing unit 810, the at least one memory unit 820, a bus 830 that couples various system components (including the memory unit 820 and the processing unit 810), and a display unit 840.
Wherein the storage unit stores program code that can be executed by the processing unit 810, such that the processing unit 810 performs the steps according to various exemplary embodiments of the present invention described in the above section "exemplary method" of this specification. For example, the processing unit 810 may perform the steps as shown in fig. 2.
The storage unit 820 may include readable media in the form of volatile memory units such as a random access memory unit (RAM) 8201 and/or a cache memory unit 8202, and may further include a read only memory unit (ROM) 8203.
The storage unit 820 may also include a program/utility 8204 having a set (at least one) of program modules 8205, such program modules 8205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
The electronic device 800 may also communicate with one or more external devices 900 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 800, and/or with any device (e.g., router, modem, etc.) that enables the electronic device 800 to communicate with one or more other computing devices. Such communication may occur via input/output (I/O) interfaces 850. Also, the electronic device 800 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the internet) via the network adapter 860. As shown, the network adapter 860 communicates with the other modules of the electronic device 800 via the bus 830. It should be appreciated that although not shown, other hardware and/or software modules may be used in conjunction with the electronic device 800, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, to name a few.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiment of the present invention may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the medical data verification method according to the embodiment of the present invention.
In an exemplary embodiment of the present invention, there is also provided a computer-readable storage medium having stored thereon a program product capable of implementing the above-described method of the present specification. In some possible embodiments, aspects of the invention may also be implemented in the form of a program product comprising program code means for causing a terminal device to carry out the steps according to various exemplary embodiments of the invention described in the above section "exemplary methods" of the present description, when said program product is run on the terminal device.
According to the program product for realizing the method, the portable compact disc read only memory (CD-ROM) can be adopted, the program code is included, and the program product can be operated on terminal equipment, such as a personal computer. However, the program product of the present invention is not limited in this regard and, in the present document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
A computer readable signal medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In situations involving remote computing devices, the remote computing devices may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to external computing devices (e.g., through the internet using an internet service provider).
Furthermore, the above-described figures are merely schematic illustrations of processes involved in methods according to exemplary embodiments of the invention, and are not intended to be limiting. It will be readily appreciated that the processes illustrated in the above figures are not intended to indicate or limit the temporal order of the processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, e.g., in multiple modules.
Other embodiments of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the invention being indicated by the following claims.
Claims (8)
1. A medical data verification method, comprising:
acquiring a current quality control demand, splitting and analyzing the current quality control demand, and determining a plurality of types of quality control rules corresponding to the current quality control demand;
performing logic combination on the multi-class quality control rules, and determining a target rule set corresponding to the multi-class quality control rules according to a combination result;
calling the target rule set to check the medical data to be processed from a plurality of data sources in parallel to obtain a checking result meeting the current quality control requirement;
the splitting and analyzing the current quality control requirement and determining the multi-class quality control rules corresponding to the current quality control requirement comprise:
extracting keywords from the current quality control requirement through a rule classification model to determine target keywords, matching the target keywords with all model attributes corresponding to the rule classification model, and determining the model attributes corresponding to the target keywords;
selecting a model attribute as a to-be-processed attribute, calling a function and a grammar template corresponding to the to-be-processed attribute, automatically filling data into the grammar template, generating a quality control rule corresponding to the to-be-processed attribute, and determining the multiple types of quality control rules according to the model attribute.
2. The medical data verification method according to claim 1, further comprising:
and constructing the rule classification model according to the attribute information of a plurality of reference quality control rules in the rule base.
3. The medical data verification method according to claim 2, wherein the attribute information comprises one or more of a combination of rule source, verification purpose, verification dataset attribute, life cycle, and degree of automation.
4. The medical data verification method according to claim 1, wherein the logically combining the multiple types of quality control rules and determining a target rule set corresponding to the multiple types of quality control rules according to a combination result comprises:
determining labels corresponding to the multiple types of quality control rules;
and combining the labels corresponding to the multiple types of quality control rules to obtain the target rule set.
5. The medical data verification method according to claim 4, wherein the combining the labels corresponding to the multiple types of quality control rules to obtain the target rule set comprises:
and logically combining the labels corresponding to the multiple types of quality control rules according to the logical relationship in the current quality control requirement, and logically calculating according to the priority order among the logical relationships to obtain the target rule set.
6. A medical data verification device, comprising:
the rule determining module is used for acquiring a current quality control demand, splitting and analyzing the current quality control demand and determining a plurality of types of quality control rules corresponding to the current quality control demand;
the rule combination module is used for carrying out logic combination on the multiple types of quality control rules and determining a target rule set corresponding to the multiple types of quality control rules according to a combination result;
the data verification module is used for calling the target rule set to verify the medical data to be processed from the plurality of data sources in parallel so as to obtain a verification result meeting the current quality control requirement;
the splitting and analyzing the current quality control requirement and determining the multi-class quality control rules corresponding to the current quality control requirement comprise:
extracting keywords from the current quality control requirement through a rule classification model to determine target keywords, matching the target keywords with all model attributes corresponding to the rule classification model, and determining the model attributes corresponding to the target keywords;
selecting a model attribute as a to-be-processed attribute, calling a function and a grammar template corresponding to the to-be-processed attribute, automatically filling data into the grammar template, generating a quality control rule corresponding to the to-be-processed attribute, and determining the multiple types of quality control rules according to the model attribute.
7. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out a medical data verification method as claimed in any one of claims 1 to 5.
8. An electronic device, comprising:
a processor; and
a memory for storing executable instructions of the processor;
wherein the processor is configured to perform the medical data verification method of any one of claims 1-5 via execution of the executable instructions.
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| CN113297853B (en) * | 2021-07-27 | 2021-10-01 | 北京惠每云科技有限公司 | Quality control method and device for electronic medical record, electronic equipment and storage medium |
| CN113627153B (en) * | 2021-07-30 | 2023-10-27 | 湖南提奥医疗科技有限公司 | Method, device, equipment and storage medium for processing data |
| CN113793681B (en) * | 2021-11-15 | 2022-02-15 | 广州悦微信息技术有限公司 | Hospital multi-stage medical index quality control method and system |
| CN115905595A (en) * | 2023-03-14 | 2023-04-04 | 阿里健康科技(中国)有限公司 | Graph data publishing method and device, computer equipment and storage medium |
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| CN110764942A (en) * | 2019-09-17 | 2020-02-07 | 平安银行股份有限公司 | Multi-type data checking method, device, computer system and readable storage medium |
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