CN109558398B - Data cleaning method based on big data and related device - Google Patents
Data cleaning method based on big data and related device Download PDFInfo
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Abstract
The embodiment of the application discloses a data cleaning method and a related device based on big data, wherein the method comprises the following steps: acquiring a first medical data set of a target medical institution in a preset period from a medical database of the target medical institution; converting the data format of the first medical data set to obtain a second medical data set corresponding to the first medical data set, wherein the data format of medical data in the second medical data set is the same; classifying the second medical data set according to a pre-stored classification strategy to obtain at least one third medical data set corresponding to the second medical data set; and performing data cleaning on the at least one third medical data set to obtain at least one target medical data set corresponding to the at least one third medical data set. By adopting the embodiment of the application, the medical data set is effectively cleaned, and the data cleaning efficiency is improved to a certain extent.
Description
Technical Field
The application relates to the technical field of big data, in particular to a data cleaning method based on big data and a related device.
Background
Currently, as the number of medical staff in medical institutions continues to increase, so does the medical data stored in medical institutions. In order to obtain the required medical data, data cleansing is required for the medical data. In general, when data cleansing is performed on a large amount of medical data, since the large amount of medical data corresponds to a plurality of data formats, a plurality of data cleansing algorithms are required to perform data cleansing on the large amount of medical data, and simultaneously, the processing pressure of the server is increased.
Disclosure of Invention
The embodiment of the application provides a data cleaning method and a related device based on big data, which are used for effectively cleaning a medical data set and improve the data cleaning efficiency to a certain extent.
In a first aspect, an embodiment of the present application provides a data cleansing method based on big data, the method including:
acquiring a first medical data set of a target medical institution in a preset period from a medical database of the target medical institution;
converting the data format of the first medical data set to obtain a second medical data set corresponding to the first medical data set, wherein the data formats of the medical data in the second medical data set are the same;
Classifying the second medical data set according to a pre-stored classification strategy to obtain at least one third medical data set corresponding to the second medical data set;
and performing data cleaning on the at least one third medical data set to obtain at least one target medical data set corresponding to the at least one third medical data set.
In a second aspect, an embodiment of the present application provides a data cleansing apparatus based on big data, the apparatus including:
an acquisition unit for acquiring a first medical data set of a target medical institution within a preset period from a medical database of the target medical institution;
the conversion unit is used for carrying out data format conversion on the first medical data set to obtain a second medical data set corresponding to the first medical data set, and the data formats of medical data in the second medical data set are the same;
the classification unit is used for classifying the second medical data set according to a pre-stored classification strategy to obtain at least one third medical data set corresponding to the second medical data set;
and the cleaning unit is used for carrying out data cleaning on the at least one third medical data set to obtain at least one target medical data set corresponding to the at least one third medical data set.
In a third aspect, an embodiment of the present application provides a server, including a processor, a memory, a communication interface, and one or more programs, where the one or more programs are stored in the memory and configured to be executed by the processor, and the programs include instructions for performing steps in the method according to the first aspect of the embodiment of the present application.
In a fourth aspect, embodiments of the present application provide a computer-readable storage medium storing a computer program for electronic data exchange, where the computer program causes a computer to perform some or all of the steps described in the method according to the first aspect of the embodiments of the present application.
In a fifth aspect, embodiments of the present application provide a computer program product comprising a non-transitory computer readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps described in the method of the first aspect of the embodiments of the present application.
It can be seen that, in the embodiment of the present application, the server first obtains a first medical data set of the target medical institution within a preset period from a medical data base of the target medical institution, then performs data format conversion on the first medical data set to obtain a second medical data set, further classifies the second medical data set according to a pre-stored classification policy to obtain at least one third medical data set, and finally performs data cleaning on the at least one third medical data set to obtain at least one target medical data set. Therefore, the medical data set is effectively cleaned, and the data cleaning efficiency is improved to a certain extent because the data formats of the medical data in the second medical data set are the same.
These and other aspects of the application will be more readily apparent from the following description of the embodiments.
Drawings
In order to more clearly describe the embodiments of the present application or the technical solutions in the background art, the following description will describe the drawings that are required to be used in the embodiments of the present application or the background art.
FIG. 1A is a flow chart of a first data cleansing method based on big data according to an embodiment of the present application;
FIG. 1B is a schematic diagram of an embodiment of the present application;
FIG. 1C is another schematic illustration provided by an embodiment of the present application;
FIG. 2 is a flow chart of a second method for cleaning data based on big data according to an embodiment of the present application;
FIG. 3 is a flow chart of a third method for cleaning data based on big data according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a data cleaning device based on big data according to an embodiment of the present application;
fig. 5 is a schematic flow chart of a server according to an embodiment of the present application.
Detailed description of the preferred embodiments
In order that those skilled in the art will better understand the present application, a technical solution in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present application without making any inventive effort, shall fall within the scope of the present application.
The following will describe in detail.
The terms "first," "second," "third," and "fourth" and the like in the description and in the claims and drawings are used for distinguishing between different objects and not necessarily for describing a particular sequential or chronological order. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those listed steps or elements but may include other steps or elements not listed or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
A server, also called a server, is a device that provides computing services. The server comprises a processor, hard disk, memory, system bus, etc., similar to a general purpose computer architecture. In a network environment, the service types provided by the servers are different and are divided into file servers, database servers, application program servers, WEB servers and the like.
Embodiments of the present application are described in detail below.
Referring to fig. 1A, fig. 1A is a flow chart of a first data cleaning method based on big data according to an embodiment of the present application, where the data cleaning method based on big data includes:
step 101: the server obtains a first medical data set of the target medical institution within a preset period from a medical database of the target medical institution.
The ending time of the preset time period may be the current system time, and the duration of the preset time period may be 3 days, 5 days, 7 days, 10 days, 15 days or other values.
Wherein the first medical data set includes a plurality of medical data, each medical data corresponding to a tag, each tag representing a medical item, each medical item corresponding to at least one cleaning procedure.
In one possible example, a server obtains a first medical data set of a target medical institution for a preset period of time from a medical database of the target medical institution, including:
The method comprises the steps that a server receives indication information sent by a medical data cleaning platform, wherein the indication information is used for indicating the server to obtain a first medical data set of a target medical institution in a preset period;
the server sends request information to the server of the target medical institution, wherein the request information is used for indicating the server of the target medical institution to feed back a plurality of medical data stored in a medical database of the server of the target medical institution within the preset period;
the server receives the plurality of medical data sent by the server of the target medical institution for the request information, and sets the plurality of medical data as a first medical data set of the target medical institution.
The medical data cleaning platform has a connection relation with a server, and the server has a connection relation with a server of a target medical institution.
Further, when the data cleansing function of the medical data cleansing platform is in an on state, the medical data cleansing platform displays a time period selection frame, a medical institution selection frame and a data cleansing button on a display interface thereof, wherein the time period selection frame comprises a start time period identification, a start time period input frame, a termination time period identification and a termination time period input frame, and the medical institution selection frame comprises a region identification, a region input frame, a medical institution identification and a medical institution input frame, as shown in fig. 1B; the medical data cleansing platform detects a click operation of a data cleansing button for a target medical institution within a preset period of time.
For example, as shown in fig. 1C, when a click operation of a data cleansing button for a target medical institution within a preset period is detected, the medical data cleansing platform transmits indication information to a server, the indication information is used for instructing the server to acquire a first medical data set of the target medical institution within the preset period, the server receives the indication information and transmits request information to the server of the target medical institution, and the server receives 100 pieces of medical data of the target medical institution within the preset period transmitted by the server of the target medical institution.
Step 102: the server performs data format conversion on the first medical data set to obtain a second medical data set corresponding to the first medical data set, wherein the data formats of the medical data in the second medical data set are the same.
In one possible example, the server performs data format conversion on the first medical data set to obtain a second medical data set corresponding to the first medical data set, including:
a server identifying a data format of each of a plurality of medical data included in the first medical data set;
the server converts the data format of each medical data in the plurality of medical data into a target data format to obtain a plurality of first medical data corresponding to the plurality of medical data;
The server takes a set of the plurality of first medical data as a second medical data set.
In particular, an embodiment in which the server identifies a data format of each of the plurality of medical data included in the first medical data set may be: inputting each of a plurality of medical data included in the first medical data set into a data format model; analyzing the medical data to obtain first characteristic information corresponding to the medical data; comparing the first characteristic information with all characteristic information in a characteristic information base stored in the data format model; if the first characteristic information is matched with the target characteristic information, determining that the data format corresponding to the target characteristic information is the data format of the medical data.
The target data format is a JS object numbered musical notation format, which is also called JSON format.
Specifically, the embodiment of the server converting the data format of each of the plurality of medical data into the target data format to obtain the plurality of first medical data corresponding to the plurality of medical data may be:
if the data format of the medical data is an XML format, mapping the medical data into a JSON character string, mapping the JSON character string into a JavaScript object to form the JSON object, and obtaining first medical data corresponding to the medical data; if the data format of the medical data is a JSON format, mapping the medical data into a JavaScript object to form the JSON object, and obtaining first medical data corresponding to the medical data; if the data format of the medical data is MML format, mapping the medical data into Map key value pairs, mapping the Map key value pairs into JSON character strings, mapping the JSON character strings into JavaScript objects to form the JSON objects, and obtaining first medical data corresponding to the medical data.
Step 103: the server classifies the second medical data set according to a pre-stored classification strategy to obtain at least one third medical data set corresponding to the second medical data set.
In one possible example, the server classifies the second medical data set according to a pre-stored classification policy, to obtain at least one third medical data set corresponding to the second medical data set, including:
the server analyzes each first medical data in the plurality of first medical data included in the second medical data set to obtain a label of each first medical data in the plurality of first medical data, wherein the label is used for representing a medical item to which the first medical data belong;
the server classifies the plurality of first medical data included in the second medical data set according to the labels to obtain at least one third medical data set corresponding to the second medical data set, and the labels of the first medical data in each third medical data set are the same.
The medical project refers to corresponding orders made by doctors for diagnosis of participants, such as medicine opening, blood test, CT, and the like.
Step 104: and the server performs data cleaning on the at least one third medical data set to obtain at least one target medical data set corresponding to the at least one third medical data set.
In one possible example, the server performs data cleansing on the at least one third medical data set to obtain at least one target medical data set corresponding to the at least one third medical data set, including:
the server analyzes each third medical data set in the at least one third medical data set to obtain a corresponding medical item of each third medical data set in the at least one third medical data set;
the server determines at least one target cleaning procedure corresponding to each third medical data set in the at least one third medical data set according to the mapping relation between the medical items and the data cleaning procedures;
and the server performs data cleaning on each third medical data set according to at least one target cleaning flow corresponding to each third medical data set to obtain a target medical data set corresponding to each third medical data set.
The mapping relationship between the medical item and the data cleaning flow is shown in table 1:
TABLE 1
| Medical item | Data cleaning process |
| Medical item 1 | Cleaning flow 1, cleaning flow 2, and cleaning flow 3 |
| Medical item 2 | Cleaning flow 1, cleaning flow 3, cleaning flow 4 |
| Medical item 3 | Cleaning flow 2, cleaning flow 3, cleaning flow 4 |
| …… | …… |
Wherein at least one cleaning process exists in at least one cleaning process corresponding to each of the different medical items and is the same.
In one possible example, the server performs data cleansing on each third medical data set according to at least one target cleansing procedure corresponding to each third medical data set, to obtain a target medical data set corresponding to each third medical data set, including:
the server analyzes each third medical data set to obtain the treatment time corresponding to each first medical data in at least one first medical data included in each third medical data set;
the server sorts the at least one first medical data according to the order of the treatment time to obtain a medical data queue corresponding to the at least one first medical data;
and the server sequentially adopts each target cleaning flow in at least one target cleaning flow corresponding to each third medical data set to carry out data cleaning on the medical data queue, so as to obtain a target medical data set corresponding to each third medical data set.
And each time the server performs data cleaning, performing data cleaning on all the first medical data included in the medical data queue by adopting one target cleaning flow in at least one target cleaning flow corresponding to the third medical data set.
For example, assuming that the third medical data set corresponds to the medical item 1, the third medical data set includes the first medical data 1, the first medical data 2 and the first medical data 3, the first medical data 1 corresponds to the number 8 months 12, the first medical data 2 corresponds to the number 8 months 5, the first medical data 3 corresponds to the number 8 months 23, the server determines that the medical data queue is the first medical data 2, the first medical data 1 and the first medical data 3 according to the order of the time of the visit according to the table 1, the server performs data cleaning on the first medical data 2, the first medical data 1 and the first medical data 3 by adopting the cleaning process 3, and the server performs data cleaning on the first medical data 2, the first medical data 1 and the first medical data 3 by adopting the cleaning process 3, thereby obtaining the target medical data set corresponding to the third medical data set.
In one possible example, the server performs data cleansing on each third medical data set according to at least one target cleansing procedure corresponding to each third medical data set, to obtain a target medical data set corresponding to each third medical data set, including:
The server analyzes each third medical data set to obtain the treatment time corresponding to each first medical data in at least one first medical data included in each third medical data set;
the server sorts the at least one first medical data according to the order of the treatment time to obtain a medical data queue corresponding to the at least one first medical data;
and the server sequentially carries out data cleaning on each first medical data in the medical data queue by adopting at least one target cleaning flow corresponding to each third medical data set to obtain a target medical data set corresponding to each third medical data set.
And each time the server performs data cleaning, performing data cleaning on one first medical data in the medical data queue by adopting at least one target cleaning flow corresponding to the third medical data set.
For example, assuming that the third medical data set corresponds to the medical item 1, the third medical data set includes the first medical data 1, the first medical data 2 and the first medical data 3, the first medical data 1 corresponds to the number 8 months 12, the first medical data 2 corresponds to the number 8 months 5, the first medical data 3 corresponds to the number 8 months 23, the server can know that the medical item 1 corresponds to the cleaning process 1, the cleaning process 2 and the cleaning process 3 according to the table 1, the server determines that the medical data queue is the first medical data 2, the first medical data 1 and the first medical data 3 according to the order of the time of the visit, the server uses the cleaning process 1, the cleaning process 2 and the cleaning process 3 to perform data cleaning on the first medical data 2, and the server uses the cleaning process 1, the cleaning process 2 and the cleaning process 3 to perform data cleaning on the first medical data 1, so as to obtain the target medical data set corresponding to the third medical data set.
It can be seen that, in the embodiment of the present application, the server first obtains a first medical data set of the target medical institution within a preset period from a medical data base of the target medical institution, then performs data format conversion on the first medical data set to obtain a second medical data set, further classifies the second medical data set according to a pre-stored classification policy to obtain at least one third medical data set, and finally performs data cleaning on the at least one third medical data set to obtain at least one target medical data set. Therefore, the medical data set is effectively cleaned, and the data cleaning efficiency is improved to a certain extent because the data formats of the medical data in the second medical data set are the same.
In one possible example, after the server performs data cleansing on the at least one third medical data set to obtain at least one target medical data set corresponding to the at least one third medical data set, the method further includes:
the server stores the at least one target medical data set to a preset storage area.
The preset storage area is used for storing medical data subjected to data cleaning operation in the server.
In one possible example, after the server performs data cleansing on the at least one third medical data set to obtain at least one target medical data set corresponding to the at least one third medical data set, the method further includes:
the server integrates the data of the at least one target medical data set to obtain a fourth medical data set corresponding to the at least one target medical data set;
the server sends the fourth medical data set to the server of the target medical data.
Specifically, the implementation manner of integrating the data of the at least one target medical data set by the server to obtain the fourth medical data set corresponding to the at least one target medical data set may be: determining a number of the at least one target medical data set as a first number; dividing the medical data set to be transmitted into storage areas to obtain a second number of storage areas, wherein the second number is equal to the first number; storing the at least one target medical dataset into a second number of storage areas and displaying a label corresponding to each medical dataset in the at least one target medical dataset; the medical data set to be transmitted is taken as a fourth medical data set.
Referring to fig. 2, fig. 2 is a flow chart of a second big data based data cleaning method according to an embodiment of the present application, where the big data based data cleaning method includes:
step 201: the server obtains a first medical data set of the target medical institution within a preset period from a medical database of the target medical institution.
Step 202: a server identifies a data format for each of a plurality of medical data included in the first medical data set.
Step 203: the server converts the data format of each medical data in the plurality of medical data into a target data format to obtain a plurality of first medical data corresponding to the plurality of medical data.
Step 204: the server takes a set of the plurality of first medical data as a second medical data set.
Step 205: the server analyzes each first medical data in the plurality of first medical data included in the second medical data set to obtain a label of each first medical data in the plurality of first medical data, and the label is used for representing a medical item to which the first medical data belongs.
Step 206: the server classifies the plurality of first medical data included in the second medical data set according to the labels to obtain at least one third medical data set corresponding to the second medical data set, and the labels of the first medical data in each third medical data set are the same.
Step 207: the server parses each third medical data set of the at least one third medical data set to obtain a corresponding medical item of each third medical data set of the at least one third medical data set.
Step 208: and the server determines at least one target cleaning procedure corresponding to each third medical data set in the at least one third medical data set according to the mapping relation between the medical items and the data cleaning procedures.
Step 209: and the server performs data cleaning on each third medical data set according to at least one target cleaning flow corresponding to each third medical data set to obtain a target medical data set corresponding to each third medical data set.
Step 210: and the server integrates the data of the at least one target medical data set to obtain a fourth medical data set corresponding to the at least one target medical data set.
Step 211: the server sends the fourth medical data set to the server of the target medical data.
It should be noted that, the specific implementation of each step of the method shown in fig. 2 may be referred to the specific implementation of the foregoing method, which is not described herein.
Referring to fig. 3, fig. 3 is a flow chart of a third data cleaning method based on big data according to an embodiment of the present application, where the data cleaning method based on big data includes:
step 301: the server obtains a first medical data set of the target medical institution within a preset period from a medical database of the target medical institution.
Step 302: the server performs data format conversion on the first medical data set to obtain a second medical data set corresponding to the first medical data set, wherein the data formats of the medical data in the second medical data set are the same.
Step 303: the server classifies the second medical data set according to a pre-stored classification strategy to obtain at least one third medical data set corresponding to the second medical data set.
Step 304: the server parses each third medical data set of the at least one third medical data set to obtain a corresponding medical item of each third medical data set of the at least one third medical data set.
Step 305: and the server determines at least one target cleaning procedure corresponding to each third medical data set in the at least one third medical data set according to the mapping relation between the medical items and the data cleaning procedures.
Step 306: the server analyzes each third medical data set to obtain the treatment time corresponding to each first medical data in at least one first medical data included in each third medical data set.
Step 307: the server sorts the at least one first medical data according to the order of the treatment time to obtain a medical data queue corresponding to the at least one first medical data.
Step 308: and the server sequentially adopts each target cleaning flow in at least one target cleaning flow corresponding to each third medical data set to carry out data cleaning on the medical data queue, so as to obtain a target medical data set corresponding to each third medical data set.
Step 309: and the server integrates the data of the at least one target medical data set to obtain a fourth medical data set corresponding to the at least one target medical data set.
Step 310: the server sends the fourth medical data set to the server of the target medical data.
It should be noted that, the specific implementation of each step of the method shown in fig. 3 may refer to the specific implementation of the foregoing method, which is not described herein.
The foregoing description of the embodiments of the present application has been presented primarily in terms of a method-side implementation. It will be appreciated that the big data based data cleansing means comprise, in order to achieve the above described functions, corresponding hardware structures and/or software modules performing the respective functions. Those of skill in the art will readily appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as hardware or combinations of hardware and computer software. Whether a function is implemented as hardware or computer software driven hardware depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
The embodiment of the application can divide the functional units of the data cleaning device based on big data according to the method example, for example, each functional unit can be divided corresponding to each function, and two or more functions can be integrated in one processing unit. The integrated units may be implemented in hardware or in software functional units. It should be noted that, in the embodiment of the present application, the division of the units is schematic, which is merely a logic function division, and other division manners may be implemented in actual practice.
Referring to fig. 4, fig. 4 is a schematic structural diagram of a data cleaning device based on big data according to an embodiment of the present application, where the data cleaning device 400 based on big data includes a processing unit 401, a storage unit 402, and a communication unit 403, and the processing unit 401 includes an acquiring unit, a converting unit, a classifying unit, and a cleaning unit, where:
an acquisition unit for acquiring a first medical data set of a target medical institution within a preset period from a medical database of the target medical institution;
the conversion unit is used for carrying out data format conversion on the first medical data set to obtain a second medical data set corresponding to the first medical data set, and the data formats of medical data in the second medical data set are the same;
the classification unit is used for classifying the second medical data set according to a pre-stored classification strategy to obtain at least one third medical data set corresponding to the second medical data set;
and the cleaning unit is used for carrying out data cleaning on the at least one third medical data set to obtain at least one target medical data set corresponding to the at least one third medical data set.
It can be seen that, in this embodiment of the present application, first, a first medical data set of a target medical institution within a preset period is obtained from a medical database of the target medical institution, then, the first medical data set is subjected to data format conversion to obtain a second medical data set, and then, the second medical data set is classified according to a pre-stored classification policy to obtain at least one third medical data set, and finally, the at least one third medical data set is subjected to data cleaning to obtain at least one target medical data set. Therefore, the medical data set is effectively cleaned, and the data cleaning efficiency is improved to a certain extent because the data formats of the medical data in the second medical data set are the same.
In one possible example, the above-mentioned acquisition unit is specifically configured to, in acquiring a first medical data set of a target medical institution within a preset period from a medical database of the target medical institution:
receiving indication information sent by a medical data cleaning platform, wherein the indication information is used for indicating a server to acquire a first medical data set of a target medical institution in a preset period;
transmitting request information to a server of the target medical institution, wherein the request information is used for instructing the server of the target medical institution to feed back a plurality of medical data stored in a medical database of the server of the target medical institution within the preset time period;
the server receiving the target medical institution sends the plurality of medical data for the request information, and takes a set of the plurality of medical data as a first medical data set of the target medical institution.
In one possible example, in performing data format conversion on the first medical data set to obtain a second medical data set corresponding to the first medical data set, the conversion unit is specifically configured to:
identifying a data format for each of a plurality of medical data included in the first medical data set;
Converting the data format of each medical data in the plurality of medical data into a target data format to obtain a plurality of first medical data corresponding to the plurality of medical data;
and taking the set formed by the plurality of first medical data as a second medical data set.
In one possible example, in classifying the second medical data set according to a pre-stored classification policy, to obtain at least one third medical data set corresponding to the second medical data set, the classification unit is specifically configured to:
analyzing each first medical data in the plurality of first medical data included in the second medical data set to obtain a label of each first medical data in the plurality of first medical data, wherein the label is used for representing a medical item to which the first medical data belong;
and classifying the plurality of first medical data included in the second medical data set according to the labels to obtain at least one third medical data set corresponding to the second medical data set, wherein the labels of the first medical data in each third medical data set are the same.
In one possible example, in data cleansing the at least one third medical data set to obtain at least one target medical data set corresponding to the at least one third medical data set, the cleansing unit is specifically configured to:
Parsing each third medical data set of the at least one third medical data set to obtain a corresponding medical item of each third medical data set of the at least one third medical data set;
determining at least one target cleaning procedure corresponding to each third medical data set in the at least one third medical data set according to the mapping relation between the medical items and the data cleaning procedures;
and carrying out data cleaning on each third medical data set according to at least one target cleaning flow corresponding to each third medical data set to obtain a target medical data set corresponding to each third medical data set.
In one possible example, in terms of performing data cleansing on each third medical data set according to at least one target cleansing procedure corresponding to each third medical data set, to obtain a target medical data set corresponding to each third medical data set, the cleansing unit is specifically configured to:
analyzing each third medical data set to obtain the diagnosis time corresponding to each first medical data in at least one first medical data included in each third medical data set;
sequencing the at least one first medical data according to the order of the treatment time to obtain a medical data queue corresponding to the at least one first medical data;
And sequentially adopting each target cleaning process in at least one target cleaning process corresponding to each third medical data set to perform data cleaning on the medical data queue to obtain a target medical data set corresponding to each third medical data set.
In one possible example, the processing unit 401 further includes:
an integration unit, configured to perform data integration on the at least one target medical data set, so as to obtain a fourth medical data set corresponding to the at least one target medical data set;
and the sending unit is used for sending the fourth medical data set to the server of the target medical data.
The processing unit 401 may be a processor or a controller (for example, may be a central processing unit (Central Processing Unit, CPU), a general purpose processor, a digital signal processor (Digital Signal Processor, DSP), an Application-specific integrated controller (Application-Specific Integrated Circuit, ASIC), a field programmable gate array (Field Programmable Gate Array, FPGA) or other programmable logic device, a transistor logic device, a hardware component, or any combination thereof), the storage unit 402 may be a memory, and the communication unit 403 may be a transceiver, a transceiver controller, a radio frequency chip, a communication interface, or the like.
Referring to fig. 5, in accordance with the embodiments shown in fig. 1A, 2 and 3, fig. 5 is a schematic structural diagram of a server according to an embodiment of the present application, the server includes a processor, a memory, a communication interface, and one or more programs, where the one or more programs are stored in the memory and configured to be executed by the processor, and the programs include instructions for performing the following steps:
acquiring a first medical data set of a target medical institution in a preset period from a medical database of the target medical institution;
converting the data format of the first medical data set to obtain a second medical data set corresponding to the first medical data set, wherein the data formats of the medical data in the second medical data set are the same;
classifying the second medical data set according to a pre-stored classification strategy to obtain at least one third medical data set corresponding to the second medical data set;
and performing data cleaning on the at least one third medical data set to obtain at least one target medical data set corresponding to the at least one third medical data set.
It can be seen that, in this example, the server first obtains the first medical data set of the target medical institution within the preset period from the medical data base of the target medical institution, then performs data format conversion on the first medical data set to obtain the second medical data set, further classifies the second medical data set according to the pre-stored classification policy to obtain at least one third medical data set, and finally performs data cleaning on the at least one third medical data set to obtain the at least one target medical data set. Therefore, the medical data set is effectively cleaned, and the data cleaning efficiency is improved to a certain extent because the data formats of the medical data in the second medical data set are the same.
In one possible example, in acquiring a first medical data set of a target medical institution within a preset period of time from a medical database of the target medical institution, the program comprises instructions specifically for performing the steps of:
receiving indication information sent by a medical data cleaning platform, wherein the indication information is used for indicating a server to acquire a first medical data set of a target medical institution in a preset period;
transmitting request information to a server of the target medical institution, wherein the request information is used for instructing the server of the target medical institution to feed back a plurality of medical data stored in a medical database of the server of the target medical institution within the preset time period;
the server receiving the target medical institution sends the plurality of medical data for the request information, and takes a set of the plurality of medical data as a first medical data set of the target medical institution.
In one possible example, in terms of data format converting the first medical data set to obtain a second medical data set corresponding to the first medical data set, the program includes instructions specifically for performing the following steps:
identifying a data format for each of a plurality of medical data included in the first medical data set;
Converting the data format of each medical data in the plurality of medical data into a target data format to obtain a plurality of first medical data corresponding to the plurality of medical data;
and taking the set formed by the plurality of first medical data as a second medical data set.
In one possible example, in classifying the second medical data set according to a pre-stored classification policy, to obtain at least one third medical data set corresponding to the second medical data set, the program comprises instructions specifically for performing the steps of:
analyzing each first medical data in the plurality of first medical data included in the second medical data set to obtain a label of each first medical data in the plurality of first medical data, wherein the label is used for representing a medical item to which the first medical data belong;
and classifying the plurality of first medical data included in the second medical data set according to the labels to obtain at least one third medical data set corresponding to the second medical data set, wherein the labels of the first medical data in each third medical data set are the same.
In one possible example, in terms of data cleansing the at least one third medical data set to obtain at least one target medical data set corresponding to the at least one third medical data set, the program comprises instructions specifically for performing the steps of:
Parsing each third medical data set of the at least one third medical data set to obtain a corresponding medical item of each third medical data set of the at least one third medical data set;
determining at least one target cleaning procedure corresponding to each third medical data set in the at least one third medical data set according to the mapping relation between the medical items and the data cleaning procedures;
and carrying out data cleaning on each third medical data set according to at least one target cleaning flow corresponding to each third medical data set to obtain a target medical data set corresponding to each third medical data set.
In one possible example, in terms of performing data cleansing on each third medical data set according to at least one target cleansing procedure corresponding to each third medical data set, to obtain a target medical data set corresponding to each third medical data set, the program includes instructions specifically for performing the following steps:
analyzing each third medical data set to obtain the diagnosis time corresponding to each first medical data in at least one first medical data included in each third medical data set;
Sequencing the at least one first medical data according to the order of the treatment time to obtain a medical data queue corresponding to the at least one first medical data;
and sequentially adopting each target cleaning process in at least one target cleaning process corresponding to each third medical data set to perform data cleaning on the medical data queue to obtain a target medical data set corresponding to each third medical data set.
In one possible example, the above-described program further includes instructions for performing the steps of:
data integration is carried out on the at least one target medical data set, and a fourth medical data set corresponding to the at least one target medical data set is obtained;
the fourth medical data set is sent to a server of the target medical data.
The embodiment of the application also provides a computer storage medium for storing a computer program for electronic data exchange, where the computer program causes a computer to execute part or all of the steps of any one of the methods described in the above method embodiments.
Embodiments of the present application also provide a computer program product comprising a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform part or all of the steps of any one of the methods described in the method embodiments above. The computer program product may be a software installation package.
It should be noted that, for simplicity of description, the foregoing method embodiments are all described as a series of acts, but it should be understood by those skilled in the art that the present application is not limited by the order of acts described, as some steps may be performed in other orders or concurrently in accordance with the present application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily required for the present application.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to related descriptions of other embodiments.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, such as the above-described division of units, merely a division of logic functions, and there may be additional manners of dividing in actual implementation, such as multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, or may be in electrical or other forms.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units described above, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable memory. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a memory, comprising several instructions for causing a computer device (which may be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the above-mentioned method of the various embodiments of the present application. And the aforementioned memory includes: a U-disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Those of ordinary skill in the art will appreciate that all or a portion of the steps in the various methods of the above embodiments may be implemented by a program that instructs associated hardware, and the program may be stored in a computer readable memory, which may include: flash disk, read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), magnetic disk or optical disk.
The foregoing has outlined rather broadly the more detailed description of embodiments of the application, wherein the principles and embodiments of the application are explained in detail using specific examples, the above examples being provided solely to facilitate the understanding of the method and core concepts of the application; meanwhile, as those skilled in the art will appreciate, modifications will be made in the specific implementation and application scope in accordance with the idea of the present application, and the above description should not be construed as limiting the present application.
Claims (9)
1. A data cleansing method based on big data, the method comprising:
acquiring a first medical data set of a target medical institution in a preset period from a medical database of the target medical institution;
Converting the data format of the first medical data set to obtain a second medical data set corresponding to the first medical data set, wherein the data formats of the medical data in the second medical data set are the same; comprising the following steps:
inputting each of a plurality of medical data included in the first medical data set into a data format model; analyzing the medical data to obtain first characteristic information corresponding to the medical data; comparing the first characteristic information with all characteristic information in a characteristic information base stored in the data format model; if the first characteristic information is matched with the target characteristic information, determining that the data format corresponding to the target characteristic information is the data format of the medical data; if the data format of the medical data is an XML format, mapping the medical data into a JSON character string, mapping the JSON character string into a JavaScript object to form the JSON object, and obtaining first medical data corresponding to the medical data; if the data format of the medical data is a JSON format, mapping the medical data into a JavaScript object to form the JSON object, and obtaining first medical data corresponding to the medical data; if the data format of the medical data is MML format, mapping the medical data into Map key value pairs, mapping the Map key value pairs into JSON character strings, mapping the JSON character strings into JavaScript objects to form the JSON objects, and obtaining first medical data corresponding to the medical data; taking a set of the plurality of first medical data as a second medical data set;
Classifying the second medical data set according to a pre-stored classification strategy to obtain at least one third medical data set corresponding to the second medical data set;
and performing data cleaning on the at least one third medical data set to obtain at least one target medical data set corresponding to the at least one third medical data set.
2. The method of claim 1, wherein the obtaining a first medical data set for the target medical facility from a medical database for the target medical facility for a predetermined period of time comprises:
receiving indication information sent by a medical data cleaning platform, wherein the indication information is used for indicating a server to acquire a first medical data set of a target medical institution in a preset period;
transmitting request information to a server of the target medical institution, wherein the request information is used for instructing the server of the target medical institution to feed back a plurality of medical data stored in a medical database of the server of the target medical institution within the preset time period;
the server receiving the target medical institution sends the plurality of medical data for the request information, and takes a set of the plurality of medical data as a first medical data set of the target medical institution.
3. The method according to claim 1, wherein classifying the second medical data set according to a pre-stored classification policy, to obtain at least one third medical data set corresponding to the second medical data set, comprises:
analyzing each first medical data in the plurality of first medical data included in the second medical data set to obtain a label of each first medical data in the plurality of first medical data, wherein the label is used for representing a medical item to which the first medical data belong;
and classifying the plurality of first medical data included in the second medical data set according to the labels to obtain at least one third medical data set corresponding to the second medical data set, wherein the labels of the first medical data in each third medical data set are the same.
4. A method according to claim 3, wherein said performing data cleansing on said at least one third medical data set to obtain at least one target medical data set corresponding to said at least one third medical data set comprises:
parsing each third medical data set of the at least one third medical data set to obtain a corresponding medical item of each third medical data set of the at least one third medical data set;
Determining at least one target cleaning procedure corresponding to each third medical data set in the at least one third medical data set according to the mapping relation between the medical items and the data cleaning procedures;
and carrying out data cleaning on each third medical data set according to at least one target cleaning flow corresponding to each third medical data set to obtain a target medical data set corresponding to each third medical data set.
5. The method according to claim 4, wherein the performing data cleansing on each third medical data set according to at least one target cleansing procedure corresponding to each third medical data set to obtain a target medical data set corresponding to each third medical data set includes:
analyzing each third medical data set to obtain the diagnosis time corresponding to each first medical data in at least one first medical data included in each third medical data set;
sequencing the at least one first medical data according to the order of the treatment time to obtain a medical data queue corresponding to the at least one first medical data;
and sequentially adopting each target cleaning process in at least one target cleaning process corresponding to each third medical data set to perform data cleaning on the medical data queue to obtain a target medical data set corresponding to each third medical data set.
6. The method of claim 5, wherein after the performing data cleansing on the at least one third medical data set to obtain at least one target medical data set corresponding to the at least one third medical data set, the method further comprises:
data integration is carried out on the at least one target medical data set, and a fourth medical data set corresponding to the at least one target medical data set is obtained;
the fourth medical data set is sent to a server of the target medical data.
7. A big data based data cleansing apparatus, the apparatus comprising:
an acquisition unit for acquiring a first medical data set of a target medical institution within a preset period from a medical database of the target medical institution;
the conversion unit is used for carrying out data format conversion on the first medical data set to obtain a second medical data set corresponding to the first medical data set, and the data formats of medical data in the second medical data set are the same; comprising the following steps:
inputting each of a plurality of medical data included in the first medical data set into a data format model; analyzing the medical data to obtain first characteristic information corresponding to the medical data; comparing the first characteristic information with all characteristic information in a characteristic information base stored in the data format model; if the first characteristic information is matched with the target characteristic information, determining that the data format corresponding to the target characteristic information is the data format of the medical data; if the data format of the medical data is an XML format, mapping the medical data into a JSON character string, mapping the JSON character string into a JavaScript object to form the JSON object, and obtaining first medical data corresponding to the medical data; if the data format of the medical data is a JSON format, mapping the medical data into a JavaScript object to form the JSON object, and obtaining first medical data corresponding to the medical data; if the data format of the medical data is MML format, mapping the medical data into Map key value pairs, mapping the Map key value pairs into JSON character strings, mapping the JSON character strings into JavaScript objects to form the JSON objects, and obtaining first medical data corresponding to the medical data; taking a set of the plurality of first medical data as a second medical data set;
The classification unit is used for classifying the second medical data set according to a pre-stored classification strategy to obtain at least one third medical data set corresponding to the second medical data set;
and the cleaning unit is used for carrying out data cleaning on the at least one third medical data set to obtain at least one target medical data set corresponding to the at least one third medical data set.
8. A server comprising a processor, a memory, a communication interface, and one or more programs stored in the memory and configured to be executed by the processor, the programs comprising instructions for performing the steps in the method of any of claims 1-6.
9. A computer readable storage medium for storing a computer program for electronic data exchange, wherein the computer program causes a computer to perform the method of any of claims 1-6.
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