CN113658711B

CN113658711B - Medical data localization method, device, computer equipment and storage medium

Info

Publication number: CN113658711B
Application number: CN202111013845.0A
Authority: CN
Inventors: 程吉安
Original assignee: Shenzhen Ping An Medical Health Technology Service Co Ltd
Current assignee: Shenzhen Ping An Medical Health Technology Service Co Ltd
Priority date: 2021-08-31
Filing date: 2021-08-31
Publication date: 2024-04-05
Anticipated expiration: 2041-08-31
Also published as: CN113658711A

Abstract

The embodiment of the application belongs to the field of big data, is applied to the medical field, and relates to a medical data localization method, which comprises the steps of obtaining local medical record data and grouping results, inquiring the standard grouping results according to a standard grouping device to obtain a first result sequence, and inquiring the local medical record data to obtain a second result sequence; when the first result sequence is inconsistent with the second result sequence, grouping the local medical record data according to the first result sequence to obtain a grouping sequence; calculating the total information entropy of the grouping sequence, selecting a preferred sequence, and determining a classified preferred field; writing the classified preferred field into a standard packet device to obtain a target packet device, and grouping the target medical records according to the target packet device to obtain localized data. The application also provides a medical data localization device, a computer device and a storage medium. In addition, the present application relates to blockchain technology in which localized data may be stored. The method and the device improve the accuracy of localization of the medical records.

Description

Medical data localization method, device, computer equipment and storage medium

Technical Field

The present disclosure relates to the field of big data technologies, and in particular, to a method, an apparatus, a computer device, and a storage medium for localizing medical data.

Background

Currently, when medical data of a patient is managed, the medical data is often classified and associated according to different groups of diseases. The hospitalization records of patients can be divided into medical records grouping schemes of different treatment groups according to the information of the medical records first page, and the schemes are usually generated according to local conditions, namely, the actual medical level and expert opinion of each region.

In practical applications, the grouping scheme is often not public and is updated annually. Therefore, it is often difficult for the medical institution to grasp the standard grouping flow, and accurate localization grouping management cannot be performed on the medical data, which eventually results in a problem that the localization accuracy of the medical data is low.

Disclosure of Invention

An embodiment of the application aims to provide a medical data localization method, a medical data localization device, computer equipment and a storage medium, so as to solve the technical problem of low medical data localization accuracy.

In order to solve the above technical problems, the embodiments of the present application provide a method for localizing medical data, which adopts the following technical scheme:

Acquiring local medical records data and grouping results corresponding to the local medical records data, carrying out reverse query on the standard grouping results according to a standard grouping device to obtain a first result sequence, and carrying out forward query on the local medical records data according to the standard grouping device to obtain a second result sequence;

determining whether the first result sequence is consistent with the second result sequence, and grouping the local medical record data according to the first result sequence when the first result sequence is inconsistent with the second result sequence to obtain a grouping sequence;

calculating the total information entropy of the group sequence, selecting the group sequence corresponding to the minimum total information entropy as a preferred sequence, and determining the sequence field of the preferred sequence as a classified preferred field;

and reversely writing field contents of the classification preferred fields serving as knowledge base data into the standard packet device to obtain a target packet device, and grouping the target medical record data according to the target packet device when receiving the target medical record data to obtain localized data corresponding to the target medical record data.

Further, the step of grouping the local medical records according to the first result sequence to obtain a grouping sequence includes:

Acquiring the number of medical records in each grouping sequence and a preset threshold value;

determining whether the number of the medical records is smaller than or equal to the preset threshold value, taking the grouping sequence of which the number of the medical records is smaller than or equal to the preset threshold value as an abnormal sequence, and deleting the abnormal sequence.

Further, the step of calculating the total information entropy of the packet sequence includes:

acquiring preset classification variables, counting the occurrence times of the classification variables in each grouping sequence, and converting the occurrence times into occurrence probability through a preset normalization function;

and calculating field information entropy of the grouping sequence according to the occurrence probability, and accumulating the field information entropy to obtain the total information entropy.

Further, the step of reversely writing the field content of the classified preferred field as knowledge base data into the standard packet device to obtain a target packet device includes:

acquiring field content of the classified preferred field, and generating a check medical record according to the field content;

comparing the check medical records with the local medical records data, and counting the number of medical records which are consistent with the check medical records in the local medical records data;

and determining the field content corresponding to the check cases with the number of cases being greater than or equal to the preset standard number as a correction field, and writing the correction field serving as knowledge base data into the standard grouping device.

Further, after the step of determining whether the first result sequence and the second result sequence are identical, the method further includes:

when the first result sequence is consistent with the second result sequence, reversely inquiring the grouping result according to the standard grouping device to obtain a third result sequence, and forwardly inquiring the local medical record data according to the standard grouping device to obtain a fourth result sequence;

determining whether the third result sequence is consistent with the fourth result sequence, and when the third result sequence is consistent with the fourth result sequence, performing reverse query on the grouping result according to the standard grouping device to obtain a fifth result sequence, and performing forward query on the local medical record data according to the standard grouping device to obtain a sixth result sequence;

and determining whether the fifth result sequence is consistent with the sixth result sequence, determining that the standard grouping device is a first grouping device when the fifth result sequence is consistent with the sixth result sequence, and grouping the target medical records according to the first grouping device when the target medical records are received, so as to obtain localized data corresponding to the target medical records.

Further, after the step of determining whether the third result sequence and the fourth result sequence are identical, the method further includes:

when the third result sequence is inconsistent with the fourth result sequence, grouping the local medical record data according to the third result sequence to obtain a first subsequence;

calculating a first sub-information entropy of each first sub-sequence, and selecting a sequence field of the first sub-sequence corresponding to the minimum first sub-information entropy as a first preferred sub-field;

and reversely writing field contents of the first preferred subfields serving as knowledge base data into the standard grouping device to obtain a second grouping device, and grouping the target medical records according to the second grouping device when the target medical records are received to obtain localized data corresponding to the target medical records.

Further, after the step of determining whether the fifth result sequence and the sixth result sequence are identical, the method further includes:

when the fifth result sequence is inconsistent with the sixth result sequence, grouping the local medical record data according to the fifth result sequence to obtain a second subsequence;

Calculating a second sub-information entropy of each second sub-sequence, and selecting a sequence field of the second sub-sequence corresponding to the minimum second sub-information entropy as a second preferred sub-field;

and reversely writing field contents of the second preferred subfields serving as knowledge base data into the standard grouping device to obtain a third grouping device, and grouping the target medical records according to the third grouping device when receiving the target medical records to obtain localized data corresponding to the target medical records.

In order to solve the above technical problems, the embodiments of the present application further provide a medical data localization device, which adopts the following technical scheme:

the query module is used for acquiring local medical records data and grouping results corresponding to the local medical records data, carrying out reverse query on the standard grouping results according to a standard grouping device to obtain a first result sequence, and carrying out forward query on the local medical records data according to the standard grouping device to obtain a second result sequence;

the first grouping module is used for determining whether the first result sequence is consistent with the second result sequence, and grouping the local medical records according to the first result sequence when the first result sequence is inconsistent with the second result sequence to obtain a grouping sequence;

The calculation module is used for calculating the total information entropy of the grouping sequence, selecting the grouping sequence corresponding to the minimum total information entropy as a preferred sequence, and determining a sequence field of the preferred sequence as a classified preferred field;

and the second grouping module is used for reversely writing field contents of the classification preferred fields into the standard grouping device as knowledge base data to obtain a target grouping device, and grouping the target medical records according to the target grouping device when receiving the target medical records to obtain localized data corresponding to the target medical records.

In order to solve the above technical problems, the embodiments of the present application further provide a computer device, which adopts the following technical schemes:

In order to solve the above technical problems, embodiments of the present application further provide a computer readable storage medium, which adopts the following technical solutions:

According to the method, local medical records data and grouping results corresponding to the local medical records data are obtained, reverse query is conducted on the standard grouping results according to a standard grouping device to obtain a first result sequence, forward query is conducted on the local medical records data according to the standard grouping device to obtain a second result sequence, and the second result sequence can be compared with the first result sequence, so that localized related grouping fields are determined; then, determining whether the first result sequence is consistent with the second result sequence, and grouping the local medical record data according to the first result sequence when the first result sequence is inconsistent with the second result sequence to obtain a grouping sequence; then, calculating the total information entropy of the grouping sequence, selecting the grouping sequence corresponding to the minimum total information entropy as a preferable sequence, determining a sequence field of the preferable sequence as a classification preferable field, wherein the classification preferable field is a localized preferable field, and accurately localizing medical data according to the classification preferable field; and finally, reversely writing field contents of the classified preferred fields into the standard packet device as knowledge base data to obtain a target packet device, and grouping the target medical record data according to the target packet device when receiving the target medical record data to obtain localized data corresponding to the target medical record data, thereby improving the localization efficiency and accuracy of the medical record data, enabling adaptive data extension according to the localized medical record data, and further improving the data processing efficiency.

Drawings

For a clearer description of the solution in the present application, a brief description will be given below of the drawings that are needed in the description of the embodiments of the present application, it being obvious that the drawings in the following description are some embodiments of the present application, and that other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art.

FIG. 1 is an exemplary system architecture diagram in which the present application may be applied;

FIG. 2 is a flow chart of one embodiment of a medical data localization method according to the present application;

FIG. 3 is a schematic structural view of one embodiment of a medical data localization apparatus according to the present application;

FIG. 4 is a schematic structural diagram of one embodiment of a computer device according to the present application.

Reference numerals: a medical data localization apparatus 300, a query module 301, a first grouping module 302, a calculation module 303, and a second grouping module 304.

Detailed Description

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used in the description of the applications herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having" and any variations thereof in the description and claims of the present application and in the description of the figures above are intended to cover non-exclusive inclusions. The terms first, second and the like in the description and in the claims or in the above-described figures, are used for distinguishing between different objects and not necessarily for describing a sequential or chronological order.

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 present 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.

In order to better understand the technical solutions of the present application, the following description will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings.

As shown in fig. 1, a system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used as a medium to provide communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.

The user may interact with the server 105 via the network 104 using the terminal devices 101, 102, 103 to receive or send messages or the like. Various communication client applications, such as a web browser application, a shopping class application, a search class application, an instant messaging tool, a mailbox client, social platform software, etc., may be installed on the terminal devices 101, 102, 103.

The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablet computers, electronic book readers, MP3 players (Moving Picture Experts Group Audio Layer III, dynamic video expert compression standard audio plane 3), MP4 (Moving Picture Experts Group Audio Layer IV, dynamic video expert compression standard audio plane 4) players, laptop and desktop computers, and the like.

The server 105 may be a server providing various services, such as a background server providing support for pages displayed on the terminal devices 101, 102, 103.

It should be noted that, the medical data localization method provided in the embodiments of the present application is generally executed by a server/terminal device, and accordingly, the medical data localization device is generally disposed in the server/terminal device.

It should be understood that the number of terminal devices, networks and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.

With continued reference to fig. 2, a flow chart of one embodiment of a method of medical data localization according to the present application is shown. The medical data localization method comprises the following steps:

Step S201, local medical records data and grouping results corresponding to the local medical records data are obtained, the standard grouping results are reversely inquired according to a standard grouping device to obtain a first result sequence, and the local medical records data are positively inquired according to the standard grouping device to obtain a second result sequence;

in this embodiment, the local medical records data are data of a first medical records page, and the local medical records data and the grouping result corresponding to the local medical records data are obtained, where the grouping result is a grouping result stored in the local medical records data, and the grouping result is not necessarily obtained by local grouping, and the grouping result corresponding to the local medical records data can be obtained from other medical institutions. And carrying out reverse query on the standard grouping result according to the standard grouping device to obtain a first result sequence, and carrying out forward query on the local medical record data according to the standard grouping device to obtain a second result sequence. Specifically, the standard packet device is a packet device set in advance according to a standard packet specification, and the data under different packets such as a DRG (Diagnosis Related Groups, disease diagnosis related group), an MDC (major diagnosis major class), an ADRG (Adjacent-DRG, core disease diagnosis related group) and the like can be obtained by forward querying local medical record data according to the standard packet device. When a grouping result corresponding to the local medical record data is obtained, reversely inquiring DRG in the grouping result according to the standard grouping device to obtain corresponding MDC data, wherein the MDC data obtained by reversely inquiring is a first result sequence; and forward inquiring the local medical record data according to the standard packet device to obtain MDC data, wherein the MDC data obtained by forward inquiring is the second result sequence.

Step S202, determining whether the first result sequence is consistent with the second result sequence, and grouping the local medical record data according to the first result sequence when the first result sequence is inconsistent with the second result sequence to obtain a grouping sequence;

in this embodiment, when the first result sequence and the second result sequence are obtained, the first result sequence and the second result sequence are compared, and whether field contents of the first result sequence and the second result sequence are consistent is determined. And when the first result sequence is inconsistent with the second result sequence, grouping the local medical record data according to the first result sequence to obtain a plurality of grouping sequences.

Step S203, calculating the total information entropy of the grouping sequence, selecting the grouping sequence corresponding to the minimum total information entropy as a preferred sequence, and determining the sequence field of the preferred sequence as a classified preferred field;

in this embodiment, when the packet sequence is obtained, the total information entropy of the packet sequence is calculated, where the total information entropy is an uncertainty representation of the packet sequence, and the total information entropy may be determined according to the field information entropy of the field content in the packet sequence. Specifically, when a grouping sequence is obtained, obtaining the occurrence times of the classification variable corresponding to each field content in the grouping sequence, and normalizing the occurrence times to obtain the field information entropy corresponding to the field content; wherein the classification variable is a preset classification field. And accumulating field information entropy of each group sequence to obtain total information entropy corresponding to each group sequence. And selecting a grouping sequence corresponding to the minimum total information entropy as a preferred sequence, and taking a sequence field corresponding to the preferred sequence as a classification preferred field. For example, if the sequence field corresponding to the preferred sequence is "age", then it is determined that "age" is the classification preferred field.

And step S204, reversely writing field contents of the classification preferred fields into the standard packet device as knowledge base data to obtain a target packet device, and grouping the target medical record data according to the target packet device when receiving the target medical record data to obtain localized data corresponding to the target medical record data.

In this embodiment, when the classification preferred field is obtained, the field content of the classification preferred field is reversely written into the standard packet as knowledge base data to obtain the target packet; the target packetizer is the packetizer after the standard packetizer is subjected to the localization modification of the grouping logic. And when receiving the target medical records, grouping the target medical records according to the target grouping device to obtain localized data corresponding to the target medical records. The target medical record data is the received data which needs to be subjected to localization processing.

It is emphasized that to further guarantee the privacy and security of the localized data, the localized data may also be stored in a blockchain node.

The blockchain referred to in the application is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and the like. The Blockchain (Blockchain), which is essentially a decentralised database, is a string of data blocks that are generated by cryptographic means in association, each data block containing a batch of information of network transactions for verifying the validity of the information (anti-counterfeiting) and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, an application services layer, and the like.

According to the data processing method and device, the efficiency and the accuracy of localization of the medical records are improved, so that the data can be adaptively extended according to the localized medical records, and the data processing efficiency is further improved.

In some optional implementations of this embodiment, the step of grouping the local medical records data according to the first result sequence to obtain a grouping sequence includes:

In this embodiment, after the local medical record data is grouped according to the first result sequence to obtain the grouping sequence, the obtained grouping sequence may be further verified to exclude abnormal groupings therein. Specifically, the number of medical records in each grouping sequence is obtained, the grouping sequence with the number of medical records smaller than or equal to a preset threshold value is determined to be an abnormal sequence, and the abnormal sequence is deleted.

According to the embodiment, the abnormal sequence is determined, so that the influence of the abnormal sequence on the localization of the medical data is avoided, and the accuracy of the localization of the medical data is further improved.

In some optional implementations of this embodiment, the step of calculating the total information entropy of the packet sequence includes:

In this embodiment, the classification variables are predetermined classification fields, for example, the ages are (0, 1), (1, 3), (3-7), (7-17), (18-24), (24-44), (45-65), (65-79), (80, + ] and are divided, wherein (0, 1), (1, 3), (3-7), (7-17), (18-24), (24-44), (45-65), (65-79), (80, + ] can be used as the predetermined classification variables.

For example, assume that there are 0001\0002\0003 cases in the MDC-A group, the main field contents of which are shown in Table 1 below:

TABLE 1

The statistics of the number of occurrences of each field are shown in table 2 below:

medical record number	0001、0002、0003
		Master diagnostics	A：1，B：1，C：1
Secondary diagnosis	D：1、E：1、F：2、G：1
		Surgical operation	O：3、P:1、L：1
Age of	(45-65]：1，(22，44]:1，(65，79]:1
		MDC	MDC-A

TABLE 2

The normalized values and entropy of each field are shown in the following table 3:

TABLE 3 Table 3

According to the embodiment, the total information entropy of the grouping sequence is calculated, so that the classification preference field can be accurately determined through the total information entropy, and the accuracy of the medical data grouping is improved.

In some optional implementations of this embodiment, the step of reversely writing the field content of the classification preferred field as knowledge base data into the standard packet device to obtain the target packet device includes:

In this embodiment, when the classification preferred field is obtained, the field content of the classification preferred field may include a plurality of different field contents, for example, the field contents corresponding to the "age" include (45-65) and (65-79).

The present embodiment determines the correction field by classifying the preferred field and writes the correction field into the standard packetizer, so that the standard packetizer written with the correction field can accurately classify and localize the medical data.

In some optional implementations of this embodiment, after the step of determining whether the first result sequence and the second result sequence are identical, the method further includes:

In this embodiment, when the first result sequence and the second result sequence are consistent, a third result sequence (i.e., ADRG1, core disease diagnosis related packet) is obtained by performing reverse query on the packet result according to the standard packet, and a fourth result sequence (i.e., ADRG2, core disease diagnosis related packet) is obtained by performing forward query on the local medical record data according to the standard packet. Determining whether the third result sequence (ADRG 1) and the fourth result sequence (ADRG 2) are consistent, when the third result sequence (ADRG 1) and the fourth result sequence (ADRG 2) are consistent, performing reverse query on the grouping result according to the standard grouping device to obtain a fifth result sequence (namely DRG1 and a disease diagnosis related group), performing forward query on the local medical record data according to the standard grouping device to obtain a sixth result sequence (namely DRG2 and a disease diagnosis related group), and determining whether the fifth result sequence (DRG 1) and the sixth result sequence (DRG 2) are consistent; when the fifth result sequence (DRG 1) and the sixth result sequence (DRG 2) coincide, the standard packetizer is determined to be the first packetizer. The first result sequence and the second result sequence are the same type of result sequence and are obtained according to different data queries; the third result sequence and the fourth result sequence are the same type of result sequence and are obtained according to different data queries; the fifth result sequence and the sixth result sequence are the same type of result sequence and are obtained according to different data queries. The first result sequence, the third result sequence and the fifth result sequence are progressive relations of sequential reverse query. And when receiving the target medical records, grouping the target medical records according to the first grouping device to obtain localized data corresponding to the target medical records.

According to the embodiment, when the first result sequence is consistent with the second result sequence, the third result sequence is judged with the fourth result sequence, and the fifth result sequence is judged with the sixth result sequence in sequence, so that the accurate determination of the target grouping device is realized, and the accuracy and the efficiency of the localization of the medical data are further improved.

In some optional implementations of this embodiment, after the step of determining whether the third result sequence and the fourth result sequence are identical, the method further includes:

In this embodiment, when the third result sequence (ADRG 1) and the fourth result sequence (ADRG 2) are inconsistent, the local medical records data are grouped according to the third result sequence, so as to obtain the first subsequence. And calculating a first sub-information entropy of each first sub-sequence, wherein the first sub-information entropy is the total information entropy corresponding to the first sub-sequence. Specifically, counting the occurrence times of the classification variables corresponding to each field in the first subsequence, and normalizing the occurrence times to obtain the field information entropy corresponding to the field. Accumulating field information entropy of each first sub-sequence to obtain total information entropy corresponding to each first sub-sequence; and selecting a sequence field of the first sub-sequence corresponding to the minimum total information entropy as a first preferred sub-field. And reversely writing the field content of the first preferred subfield into a standard packet device as knowledge base data to obtain a second packet device. And when the target medical records data are received, grouping the target medical records data according to the second grouping device to obtain localized data.

According to the embodiment, when the third result sequence is inconsistent with the fourth result sequence, the local medical records are grouped according to the third result sequence, the first preferred sub-field is determined according to the first sub-sequence obtained by grouping, and the field content of the first preferred sub-field is written into the standard grouping device, so that the determination of the first preferred sub-field when grouping logic is inconsistent with local grouping logic is realized, and the accuracy of the localization of medical data is further improved.

In some optional implementations of this embodiment, after the step of determining whether the fifth result sequence and the sixth result sequence are identical, the method further includes:

In this embodiment, when the fifth result sequence (DRG 1) and the sixth result sequence (DRG 2) are inconsistent, the local medical record data is grouped according to the fifth result sequence, so as to obtain the second subsequence. And calculating a second sub-information entropy of each second sub-sequence, wherein the second sub-information entropy is the total information entropy corresponding to the second sub-sequence. Specifically, counting the occurrence times of the classification variables corresponding to each field in the second subsequence, and normalizing the occurrence times to obtain the field information entropy corresponding to the field. Accumulating field information entropy of each second sub-sequence to obtain total information entropy corresponding to each second sub-sequence; and selecting a sequence field of a second sub-sequence corresponding to the minimum total information entropy as a second preferred sub-field. And reversely writing the field content of the second preferred subfield into the standard packetizer as knowledge base data to obtain a third packetizer. And when the target medical records data are received, grouping the target medical records data according to the third grouping device to obtain localized data.

According to the embodiment, when the fifth result sequence is inconsistent with the sixth result sequence, the second preferred subfield is determined, so that accurate determination of the grouping field for localization of the medical data is further realized, and the accuracy rate of localization of the medical data is improved.

Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by computer readable instructions stored in a computer readable storage medium that, when executed, may comprise the steps of the embodiments of the methods described above. The storage medium may be a nonvolatile storage medium such as a magnetic disk, an optical disk, a Read-Only Memory (ROM), or a random access Memory (Random Access Memory, RAM).

It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited in order and may be performed in other orders, unless explicitly stated herein. Moreover, at least some of the steps in the flowcharts of the figures may include a plurality of sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, the order of their execution not necessarily being sequential, but may be performed in turn or alternately with other steps or at least a portion of the other steps or stages.

With further reference to fig. 3, as an implementation of the method shown in fig. 2, the present application provides an embodiment of a medical data localization apparatus, where an embodiment of the apparatus corresponds to the embodiment of the method shown in fig. 2, and the apparatus may be specifically applied to various electronic devices.

As shown in fig. 3, the medical data localization apparatus 300 according to the present embodiment includes: a query module 301, a first grouping module 302, a calculation module 303, and a second grouping module 304. Wherein:

the query module 301 is configured to obtain local medical records data and a grouping result corresponding to the local medical records data, perform a reverse query on the standard grouping result according to a standard grouping device to obtain a first result sequence, and perform a forward query on the local medical records data according to the standard grouping device to obtain a second result sequence;

A first grouping module 302, configured to determine whether the first result sequence and the second result sequence are consistent, and group the local medical record data according to the first result sequence when the first result sequence and the second result sequence are inconsistent, so as to obtain a grouping sequence;

in some alternative implementations of the present embodiment, the first grouping module 302 includes:

the acquisition unit is used for acquiring the number of medical records in each grouping sequence and a preset threshold value;

and the first confirmation unit is used for determining whether the number of the medical records is smaller than or equal to the preset threshold value, taking the grouping sequence of which the number of the medical records is smaller than or equal to the preset threshold value as an abnormal sequence, and deleting the abnormal sequence.

The calculating module 303 is configured to calculate a total information entropy of the packet sequence, select a packet sequence corresponding to a minimum total information entropy as a preferred sequence, and determine a sequence field of the preferred sequence as a classification preferred field;

In some alternative implementations of the present embodiment, the computing module 303 includes:

the statistics unit is used for acquiring preset classification variables, counting the occurrence times of the classification variables in each grouping sequence, and converting the occurrence times into occurrence probability through a preset normalization function;

and the calculating unit is used for calculating field information entropy of the grouping sequence according to the occurrence probability, and accumulating the field information entropy to obtain the total information entropy.

And the second grouping module 304 is configured to reversely write field contents of the classification preferred fields into the standard grouping device as knowledge base data to obtain a target grouping device, and when receiving target medical record data, group the target medical record data according to the target grouping device to obtain localized data corresponding to the target medical record data.

In some alternative implementations of the present embodiment, the second packet module 304 includes:

the generation unit is used for acquiring the field content of the classified preferred field and generating a check medical record according to the field content;

the comparison unit is used for comparing the check medical records with the local medical records data and counting the number of medical records which are consistent with the check medical records in the local medical records data;

and the second confirmation unit is used for determining that the field content corresponding to the check cases with the number of cases being greater than or equal to the preset standard number is a correction field, and writing the correction field serving as knowledge base data into the standard grouping device.

In some optional implementations of this embodiment, the medical data localization apparatus 300 further includes:

the acquisition module is used for carrying out reverse query on the grouping result according to the standard grouping device to obtain a third result sequence when the first result sequence is consistent with the second result sequence, and carrying out forward query on the local medical record data according to the standard grouping device to obtain a fourth result sequence;

the first confirmation module is used for determining whether the third result sequence is consistent with the fourth result sequence, and when the third result sequence is consistent with the fourth result sequence, the grouping result is reversely inquired according to the standard grouping device to obtain a fifth result sequence, and the local medical record data is positively inquired according to the standard grouping device to obtain a sixth result sequence;

And the second confirmation module is used for determining whether the fifth result sequence is consistent with the sixth result sequence, determining that the standard grouping device is a first grouping device when the fifth result sequence is consistent with the sixth result sequence, and grouping the target medical records according to the first grouping device when the target medical records are received, so as to obtain localized data corresponding to the target medical records.

In some optional implementations of this embodiment, the first confirmation module includes:

a first grouping unit, configured to group the local medical record data according to the third result sequence when the third result sequence is inconsistent with the fourth result sequence, so as to obtain a first subsequence;

the first selecting unit is used for calculating the first sub-information entropy of each first sub-sequence and selecting a sequence field of the first sub-sequence corresponding to the minimum first sub-information entropy as a first preferred sub-field;

and the first writing unit is used for reversely writing the field content of the first preferred subfield into the standard grouping device as knowledge base data to obtain a second grouping device, and grouping the target medical records data according to the second grouping device when the target medical records data are received to obtain localized data corresponding to the target medical records data. In some optional implementations of this embodiment, the second confirmation module includes:

The second grouping unit is used for grouping the local medical record data according to the fifth result sequence when the fifth result sequence is inconsistent with the sixth result sequence, so as to obtain a second subsequence;

the second selecting unit is used for calculating the second sub-information entropy of each second sub-sequence and selecting the sequence field of the second sub-sequence corresponding to the minimum second sub-information entropy as a second preferred sub-field;

and the second writing unit is used for reversely writing the field content of the second preferred subfield into the standard grouping device as knowledge base data to obtain a third grouping device, and grouping the target medical records data according to the third grouping device when the target medical records data are received to obtain localized data corresponding to the target medical records data.

The medical data localization device provided by the embodiment improves the localization efficiency and accuracy of the medical data, enables adaptive data extension to be performed according to the localized medical data, and further improves the data processing efficiency.

In order to solve the technical problems, the embodiment of the application also provides computer equipment. Referring specifically to fig. 4, fig. 4 is a basic structural block diagram of a computer device according to the present embodiment.

The computer device 6 comprises a memory 61, a processor 62, a network interface 63 communicatively connected to each other via a system bus. It should be noted that only the computer device 6 having components 61-63 is shown in fig. 4, but it should be understood that not all of the illustrated components are required to be implemented and that more or fewer components may alternatively be implemented. It will be appreciated by those skilled in the art that the computer device herein is a device capable of automatically performing numerical calculations and/or information processing in accordance with predetermined or stored instructions, the hardware of which includes, but is not limited to, microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASICs), programmable gate arrays (fields-Programmable Gate Array, FPGAs), digital processors (Digital Signal Processor, DSPs), embedded devices, etc.

The computer equipment can be a desktop computer, a notebook computer, a palm computer, a cloud server and other computing equipment. The computer equipment can perform man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch pad or voice control equipment and the like.

The memory 61 includes at least one type of readable storage media including flash memory, hard disk, multimedia card, card memory (e.g., SD or DX memory, etc.), random Access Memory (RAM), static Random Access Memory (SRAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), programmable Read Only Memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the storage 61 may be an internal storage unit of the computer device 6, such as a hard disk or a memory of the computer device 6. In other embodiments, the memory 61 may also be an external storage device of the computer device 6, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card) or the like, which are provided on the computer device 6. Of course, the memory 61 may also comprise both an internal memory unit of the computer device 6 and an external memory device. In this embodiment, the memory 61 is typically used to store an operating system and various application software installed on the computer device 6, such as computer readable instructions of a medical data localization method. Further, the memory 61 may be used to temporarily store various types of data that have been output or are to be output.

The processor 62 may be a central processing unit (Central Processing Unit, CPU), controller, microcontroller, microprocessor, or other data processing chip in some embodiments. The processor 62 is typically used to control the overall operation of the computer device 6. In this embodiment, the processor 62 is configured to execute computer readable instructions stored in the memory 61 or process data, such as computer readable instructions for executing the medical data localization method.

The network interface 63 may comprise a wireless network interface or a wired network interface, which network interface 63 is typically used for establishing a communication connection between the computer device 6 and other electronic devices.

The computer equipment provided by the embodiment improves the localization efficiency and accuracy of the medical record data, so that the data can be adaptively extended according to the localized medical record data, and the data processing efficiency is further improved.

The present application also provides another embodiment, namely, a computer-readable storage medium storing computer-readable instructions executable by at least one processor to cause the at least one processor to perform the steps of a medical data localization method as described above.

The computer readable storage medium provided by the embodiment improves the localization efficiency and accuracy of the medical records data, so that the data can be adaptively extended according to the localized medical records data, and the data processing efficiency is further improved.

From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk), comprising several instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method described in the embodiments of the present application.

It is apparent that the embodiments described above are only some embodiments of the present application, but not all embodiments, the preferred embodiments of the present application are given in the drawings, but not limiting the patent scope of the present application. This application may be embodied in many different forms, but rather, embodiments are provided in order to provide a more thorough understanding of the present disclosure. Although the present application has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described in the foregoing, or equivalents may be substituted for elements thereof. All equivalent structures made by the specification and the drawings of the application are directly or indirectly applied to other related technical fields, and are also within the protection scope of the application.

Claims

1. A method of localizing medical data, comprising the steps of:

the field content of the classification preferred field is used as knowledge base data to be reversely written into the standard packet device to obtain a target packet device, and when target medical record data is received, the target medical record data is grouped according to the target packet device to obtain localized data corresponding to the target medical record data;

The step of grouping the local medical records according to the first result sequence to obtain a grouping sequence comprises the following steps:

determining whether the number of the medical records is smaller than or equal to the preset threshold value, taking a grouping sequence of which the number of the medical records is smaller than or equal to the preset threshold value as an abnormal sequence, and deleting the abnormal sequence;

after the step of determining whether the first result sequence and the second result sequence are identical, further comprising:

2. The method of localization of medical data of claim 1, wherein the step of calculating the total information entropy of the sequence of packets comprises:

3. The method of claim 1, wherein the step of reversely writing field contents of the classification preference field as knowledge base data to the standard packetizer to obtain the target packetizer comprises:

4. The method of localization of medical data of claim 1, further comprising, after the step of determining whether the third result sequence and the fourth result sequence are identical:

5. The method of localization of medical data of claim 1, further comprising, after the step of determining whether the fifth result sequence and the sixth result sequence are identical:

6. A medical data localization apparatus, comprising:

the second grouping module is used for reversely writing field contents of the classification preferred fields into the standard grouping device as knowledge base data to obtain a target grouping device, and grouping the target medical records according to the target grouping device when receiving the target medical records to obtain localized data corresponding to the target medical records;

the grouping the local medical records data according to the first result sequence, and obtaining a grouping sequence includes:

After said determining if said first result sequence and said second result sequence are identical, further comprising:

7. A computer device comprising a memory having stored therein computer readable instructions which when executed by the processor implement the steps of the medical data localization method of any one of claims 1 to 5.

8. A computer readable storage medium having stored thereon computer readable instructions which when executed by a processor implement the steps of the medical data localization method according to any one of claims 1 to 5.