CN107992511A - Index establishing method, device, storage medium and the electronic equipment of medical data table - Google Patents
Index establishing method, device, storage medium and the electronic equipment of medical data table Download PDFInfo
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/22—Indexing; Data structures therefor; Storage structures
- G06F16/2228—Indexing structures
- G06F16/2272—Management thereof
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/22—Indexing; Data structures therefor; Storage structures
- G06F16/2282—Tablespace storage structures; Management thereof
Abstract
This disclosure relates to a kind of index establishing method of medical data table, device, storage medium and electronic equipment, the described method includes：According to the medical bodies disaggregated model pre-established, medical bodies type identification is carried out to the data element in medical data table, obtains medical bodies type corresponding with the medical data table；The medical bodies type obtained is added in indexed set as index entry, and it is associated with the medical data table.Pass through above-mentioned technical proposal, the medical data table of various criterion can be managed collectively, realize the data interchange between different platform, staff is avoided when carrying out Data Integration, need manually to browse the complicated processes of each medical data table, the workload of staff can be reduced, effectively improves the work efficiency of staff.It in addition, being based on the indexed set, can be inquired about by way of inverted index, meet the use demand of user, improve indexed search efficiency.
Description
Technical Field
The present disclosure relates to the field of data table management, and in particular, to a method and an apparatus for establishing an index of a medical data table, a storage medium, and an electronic device.
Background
In the business systems of all medical institutions in the same region, the medical data tables do not have a unified standard, and in the background of regional medical integration, when workers integrate data among different data platforms, the workers usually need to manually browse the medical data tables, so that the workload of the workers is increased, the working efficiency is low, and the data among the business systems in the region are difficult to uniformly manage.
Disclosure of Invention
In order to solve the above problem, the present disclosure provides an index establishing method and apparatus for a medical data table, a storage medium, and an electronic device.
In order to achieve the above object, according to a first aspect of the present disclosure, there is provided an index building method for a medical data table, the method including:
according to a pre-established medical entity classification model, performing medical entity type identification on data elements in a medical data table to obtain a medical entity type corresponding to the medical data table;
adding the obtained medical entity type as an index item to an index set and associating with the medical data table.
Optionally, the method further comprises:
receiving a query keyword input by a user;
matching the query key words with index items in the index set;
and outputting the medical data table associated with the index item successfully matched with the query keyword.
Optionally, the outputting the medical data table associated with the index item successfully matched with the query keyword includes:
determining the priority of each medical data table associated with the index item successfully matched with the query keyword;
and outputting the medical data tables associated with the index items successfully matched with the query keywords according to the sequence from high priority to low priority.
Optionally, the determining the priority of each medical data table associated with the index item successfully matched with the query keyword comprises:
determining a score for each of the medical data tables by the following formula:
wherein, the coarse m A score representing the mth sheet of the medical data table;
I m representing the total number of fields in the mth medical data table;
n mi indicating the number of index items successfully matched with the query keyword contained in the ith field in the mth medical data table, and when the number is zero, n mi ＝1；
And determining the priority of the medical data table according to the score of the medical data table, wherein the smaller the score of the medical data table is, the higher the priority is.
Optionally, the medical entity type comprises at least one of:
address, medical institution name, drug category, surgical category, professional category, department category.
Optionally, the index set further comprises at least one of the following index items:
table name, field name, data element, field comment, table comment.
According to a second aspect of the present disclosure, there is provided an index building apparatus for a medical data table, the apparatus comprising:
the entity type identification module is used for identifying the medical entity type of the data elements in the medical data table according to a pre-established medical entity classification model to obtain the medical entity type corresponding to the medical data table;
and the processing module is used for adding the obtained medical entity type as an index item into an index set and associating the index item with the medical data table.
Optionally, the apparatus further comprises:
the receiving module is used for receiving query keywords input by a user;
the matching module is used for matching the query key words with the index items in the index set;
and the output module is used for outputting the medical data table associated with the index item successfully matched with the query keyword.
Optionally, the output module includes:
the determining sub-module is used for determining the priority of each medical data table associated with the index item successfully matched with the query keyword;
and the output sub-module is used for outputting each medical data table associated with the index item successfully matched with the query keyword according to the sequence of the priority from high to low.
Optionally, the determining sub-module determines the priority of each medical data table associated with the index item successfully matched with the query keyword by:
determining a score for each of the medical data tables by:
wherein, good m A score representing the mth sheet of the medical data table;
I m representing the total number of fields in the mth medical data table;
n mi indicating the number of index items successfully matched with the query keyword contained in the ith field in the mth medical data table, and when the number is zero, n mi ＝1；
And determining the priority of the medical data table according to the score of the medical data table, wherein the smaller the score of the medical data table is, the higher the priority is.
Optionally, the medical entity type comprises at least one of:
address, medical institution name, drug category, surgical category, professional category, department category.
Optionally, the index set further comprises at least one of the following index items:
table name, field name, data element, field comment, table comment.
According to a third aspect of the present disclosure, there is provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the method of the first aspect.
According to a fourth aspect of the present disclosure, there is provided an electronic apparatus comprising:
the computer-readable storage medium of the third aspect; and
one or more processors to execute the program in the computer-readable storage medium.
In the technical scheme, through a medical entity classification model, medical entity type recognition is performed on data elements in a medical data table one by one to obtain a medical entity type corresponding to the medical data table, and then the medical entity type is used as an index item to be added to an index set. Therefore, the medical data tables of different standards can be managed in a unified mode, data intercommunication among different platforms is achieved, the complex process that workers need to manually browse the medical data tables when data integration is carried out is avoided, the workload of the workers can be reduced, and the working efficiency of the workers is effectively improved. In addition, in practical application, a user searches the medical data table through the content in the medical data table, and based on the index set, an inverted index mode can be adopted, and the index items are matched in the index set through the medical entity types input by the user, so that the medical data table is determined, the use requirements of the user are met, and the index searching efficiency is improved.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure without limiting the disclosure. In the drawings:
FIG. 1 is a flow chart of a method for index building of a medical data table provided according to one embodiment of the present disclosure;
FIG. 2 is a flow chart of a method for index building of a medical data table provided in accordance with another embodiment of the present disclosure;
FIG. 3 is a flow diagram of an example implementation of outputting a medical data table associated with an index item successfully matched to the query keyword;
FIG. 4A is a fee service table;
FIG. 4B is a diagnostic services table;
FIG. 4C is an order service table;
FIG. 5 is a block diagram of an index building apparatus for a medical data table provided in accordance with one embodiment of the present disclosure;
FIG. 6 is a block diagram of an index building apparatus for a medical data table provided in accordance with another embodiment of the present disclosure;
FIG. 7 is a block diagram illustrating an electronic device in accordance with an exemplary embodiment;
FIG. 8 is a block diagram of an electronic device shown in accordance with an example embodiment.
Detailed Description
The following detailed description of specific embodiments of the present disclosure is provided in connection with the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the present disclosure, are given by way of illustration and explanation only, not limitation.
Fig. 1 is a flowchart illustrating a method for building an index of a medical data table according to an embodiment of the present disclosure, where the method includes:
in S11, according to a pre-established medical entity classification model, performing medical entity type identification on data elements in a medical data table to obtain a medical entity type corresponding to the medical data table.
Optionally, the medical entity types include, but are not limited to, at least one of:
address, medical institution name, drug category, surgical category, professional category, department category.
Wherein the medical entity classification model can be obtained by means of statistics and machine learning. Illustratively, a large amount of medical data with medical entity type information may be accumulated in advance and stored in a database; and then, learning the medical data in a machine learning mode to obtain a medical entity classification model. For example, the medical entity classification model can identify that the medical entity type corresponding to "wang-chi" is "name" and the medical entity type corresponding to "clinical laboratory" is "department category".
For example, when the medical entity type identification is performed on the data elements in the medical data table according to the medical entity classification model, the medical entity type identification can be performed according to the identification sequence of the occurrence frequency of each entity type in the medical data table, such as the prior identification of a name, the subsequent identification of an address and a medical institution name, and the subsequent identification of a medicine type, an operation type, an occupation type, a department type, and the like, so that the entity type identification efficiency can be effectively improved. In addition, the identification sequence may also be modified according to specific use cases, which is not limited in this disclosure.
In S12, the obtained medical entity type is added as an index entry to an index set and associated with the medical data table.
The index set is a set of index items created according to medical entity types corresponding to the medical data tables in the database. The obtained medical entity type is used as an index item to be added into an index set and is associated with the medical data table, so that when the index item is determined according to the input of a user, the medical data table corresponding to the index item is directly determined, and the searching efficiency is effectively improved.
In the technical scheme, through a medical entity classification model, medical entity type recognition is performed on data elements in a medical data table one by one to obtain a medical entity type corresponding to the medical data table, and then the medical entity type is used as an index item to be added to an index set. Therefore, unified management can be carried out on the medical data tables of different standards, data intercommunication among different platforms is realized, the complex process that workers need to manually browse each medical data table when carrying out data integration is avoided, the workload of the workers can be reduced, and the working efficiency of the workers is effectively improved. In addition, in practical application, a user searches the medical data table through the content in the medical data table, and based on the index set, an inverted index mode can be adopted, and the index items are matched in the index set through the medical entity types input by the user, so that the medical data table is determined, the use requirements of the user are met, and the index searching efficiency is improved.
Optionally, the index set further comprises at least one of the following index entries:
table name, field name, data element, field comment, table comment.
In an embodiment, when the medical entity type corresponding to the obtained data element is added to the index set as an index item, the data element may also be added to the index set as an index item.
In another embodiment, when the obtained medical entity type corresponding to the data element is added to the index set as an index entry, the data element, the field corresponding to the data element and the field comment corresponding to the field, the table name of the medical data table where the data element is located, and the table comment corresponding to the table may also be added to the index set as index entries.
In the technical scheme, the data elements and the related information thereof are added to the index set as the index items, so that the application range of the index set can be increased, the diversity of the input query keywords when a user queries based on the index set is increased, and the use experience of the user is improved.
Optionally, as shown in fig. 2, the method further includes:
in S21, a query keyword input by a user is received.
The query keyword input by the user may be a medical entity type, a data element, or an input combining various data, for example, a combination of a medical entity type and a data element, such as "zhang, department category".
For example, the user may empirically input data elements that may appear in the medical data table that the user wants to query as query keywords. In this case, the user can preferentially input the query keyword with the largest search range, so as to narrow down the medical data table layer by layer to determine the medical data table.
In S22, the query keyword is matched with an index item in the index set.
In one embodiment, the query key may be matched with all index items in the index set to obtain index items successfully matched with the query key.
In another embodiment, the type of the query keyword may be determined first, for example, the type of the query keyword input by the user is a medical entity type, and then the query keyword may be directly matched with the index items of the medical entity types in the index set to obtain the index items successfully matched with the query keyword, so that the query efficiency may be improved.
In S23, the medical data table associated with the index item successfully matched with the query keyword is output.
Optionally, as shown in fig. 3, in S23, an example implementation manner of outputting the medical data table associated with the index item successfully matched with the query keyword is that, as shown in fig. 3, this step includes:
in S31, the priority of each medical data table associated with the index item successfully matched with the query keyword is determined.
Optionally, the score of each of the medical data tables is determined by the following formula:
wherein, the coarse m A score representing the mth sheet of the medical data table;
I m representing the total number of fields in the mth medical data table;
n mi indicating the number of index items successfully matched with the query keyword contained in the ith field in the mth medical data table, and when the number is zero, n mi ＝1；
And determining the priority of the medical data table according to the score of the medical data table, wherein the smaller the score of the medical data table is, the higher the priority is.
Illustratively, if the query keyword input by the user is "wang, zhao, pan, liu, check", three medical data tables are queried according to the query keyword, as shown in fig. 4A, 4B, and 4C, respectively. Fig. 4A is a fee service table, fig. 4B is a diagnosis service table, and fig. 4C is an advice service table.
Calculating the score, coarse, of the fee service schedule shown in FIG. 4A according to the above formula 1 ：
The fee service table has 5 fields, i.e. I 1 ＝5；
According to the query key words input by the user, 5 index items which are successfully matched with the query key words can be determined to be contained in the 4 th field of 'patient name' field of the expense business table, namely n 14 =5, other fields do not contain index entry matching successfully with query key, i.e. n 11 ＝n 12 ＝n 13 ＝n 15 =1, then:
calculating the score, coarse, of the diagnostic services Table of FIG. 4B according to the above formula 2 ：
The diagnosis service table has 6 fields, i.e. I 2 ＝6；
According to the query key words input by the user, 5 index items successfully matched with the query key words can be determined to be contained in a 3 rd field 'department' field corresponding to the diagnosis service table, 1 index item successfully matched with the query key words is contained in a 4 th field 'doctor examination name' field, 5 index items successfully matched with the query key words are contained in a 5 th field 'patient name' field, 5 index items successfully matched with the query key words are contained in a 6 th field 'examination list name' field, and other fields do not contain index items successfully matched with the query key words, namely n 23 ＝n 25 ＝n 26 ＝5，n 21 ＝n 22 ＝n 24 =1, then:
calculating the score, coarse, of the order service Table shown in FIG. 4C according to the above formula 3 ：
The medical advice business table has 6 fields, namely I 3 ＝6；
According to the query key words input by the user, 5 index items successfully matched with the query key words can be determined to be contained in the 2 nd field 'doctor name' field corresponding to the medical advice business table, 2 index items successfully matched with the query key words are contained in the 6 th field 'department' field, and other fields do not contain the index items successfully matched with the query key words, namely n 32 ＝5，n 36 ＝2，n 31 ＝n 33 ＝n 34 ＝n 35 =1, then:
then coarse 2 <goal 3 <goal 1 Namely, the three medical data tables are sorted into a diagnosis service table, a medical advice service table and a charge service table according to the sequence of the priority from high to low.
In S32, the medical data tables associated with the index items successfully matched with the query keyword are output in the order of priority from high to low.
In the above example, the priority of the three queried medical data tables is determined to be the diagnosis service table, the medical advice service table and the cost service table from high to low, respectively, and when each data table is output, the diagnosis service table is preferentially output, the medical advice service table is next, and the cost service table is last.
In the above technical solution, when a plurality of medical data tables are determined according to the query keyword input by the user, the priority levels of the plurality of medical data tables may be determined, and then each medical data table may be output according to the priority level of each medical data table. Through the technical scheme, the medical data table with the highest correlation with the query keywords input by the user can be preferentially output, the use requirements of the user are fitted, and the use experience of the user is improved.
The present disclosure further provides an index creating device for a medical data table, as shown in fig. 5, the device 10 includes:
the entity type identification module 100 is configured to perform medical entity type identification on data elements in a medical data table according to a pre-established medical entity classification model, and obtain a medical entity type corresponding to the medical data table;
and the processing module 200 is used for adding the obtained medical entity type as an index item into an index set and associating the index item with the medical data table.
Optionally, as shown in fig. 6, the apparatus 10 further includes:
a receiving module 300, configured to receive a query keyword input by a user;
a matching module 400, configured to match the query keyword with an index entry in the index set;
and the output module 500 is configured to output the medical data table associated with the index item successfully matched with the query keyword.
Optionally, the output module 500 includes:
the determining sub-module is used for determining the priority of each medical data table associated with the index item successfully matched with the query keyword;
and the output sub-module is used for outputting each medical data table associated with the index item successfully matched with the query keyword according to the sequence of the priority from high to low.
Optionally, the determining submodule determines the priority of each medical data table associated with the index item successfully matched with the query keyword by:
determining a score for each of the medical data tables by:
wherein, good m A score representing the mth medical data table;
I m representing the total number of fields in the mth medical data table;
n mi indicating the number of index items successfully matched with the query keyword contained in the ith field in the mth medical data table, and when the number is zero, n mi ＝1；
And determining the priority of the medical data table according to the score of the medical data table, wherein the smaller the score of the medical data table is, the higher the priority is.
Optionally, the medical entity type comprises at least one of:
address, medical institution name, drug category, surgical category, professional category, department category.
Optionally, the index set further comprises at least one of the following index items:
table name, field name, data element, field comment, table comment.
With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.
Fig. 7 is a block diagram illustrating an electronic device 700 in accordance with an example embodiment. As shown in fig. 7, the electronic device 700 may include: a processor 701, a memory 702, multimedia components 703, input/output (I/O) interfaces 704, and communication components 705.
The processor 701 is configured to control the overall operation of the electronic device 700, so as to complete all or part of the steps in the above-mentioned method for establishing an index of a medical data table. The memory 702 is used to store various types of data to support operation at the electronic device 700, such as instructions for any application or method operating on the electronic device 700 and application-related data, such as contact data, transmitted and received messages, pictures, audio, video, and the like. The Memory 702 may be implemented by any type or combination of volatile and non-volatile Memory devices, such as Static Random Access Memory (SRAM), electrically Erasable Programmable Read-Only Memory (EEPROM), erasable Programmable Read-Only Memory (EPROM), programmable Read-Only Memory (PROM), read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic or optical disk. The multimedia components 703 may include screen and audio components. Wherein the screen may be, for example, a touch screen and the audio component is used for outputting and/or inputting audio signals. For example, the audio component may include a microphone for receiving an external audio signal. The received audio signal may further be stored in the memory 702 or transmitted through the communication component 705. The audio assembly also includes at least one speaker for outputting audio signals. The I/O interface 704 provides an interface between the processor 701 and other interface modules, such as a keyboard, mouse, buttons, and the like. These buttons may be virtual buttons or physical buttons. The communication component 705 is used for wired or wireless communication between the electronic device 700 and other devices. Wireless Communication, such as Wi-Fi, bluetooth, near Field Communication (NFC for short), 2G, 3G, or 4G, or a combination of one or more of them, and thus the corresponding Communication component 705 may include: wi-Fi module, bluetooth module, NFC module.
In an exemplary embodiment, the electronic Device 700 may be implemented by one or more Application Specific Integrated Circuits (ASICs), digital Signal Processors (DSPs), digital Signal Processing Devices (DSPDs), programmable Logic Devices (PLDs), field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components, for performing the above-mentioned index establishing method for the medical data table.
In another exemplary embodiment, a computer readable storage medium comprising program instructions, such as the memory 702 comprising program instructions, which are executable by the processor 701 of the electronic device 700 to perform the above-mentioned index establishing method for the medical data table is also provided.
Fig. 8 is a block diagram illustrating an electronic device 800 in accordance with an example embodiment. For example, the electronic device 800 may be provided as a server. Referring to fig. 8, the electronic device 800 includes a processor 822, which may be one or more in number, and a memory 832 for storing computer programs executable by the processor 822. The computer programs stored in memory 832 may include one or more modules that each correspond to a set of instructions. Further, the processor 822 may be configured to execute the computer program to perform the index establishing method of the medical data table described above.
Additionally, the electronic device 800 may also include a power component 826 and a communication component 850, the power component 826 may be configured to perform power management of the electronic device 800, and the communication component 850 may be configured to enable communication, e.g., wired or wireless communication, of the electronic device 800. The electronic device 800 may also include input/output (I/O) interfaces 858. The electronic device 800 may operate based on an operating system, such as Windows Server, mac OS XTM, unixTM, linuxTM, etc., stored in the memory 832.
In another exemplary embodiment, a computer readable storage medium, such as the memory 832, including program instructions executable by the processor 822 of the electronic device 800 to perform the above-described index building method for a medical data table is also provided.
The preferred embodiments of the present disclosure are described in detail above with reference to the accompanying drawings, however, the present disclosure is not limited to the specific details in the above embodiments, and various simple modifications may be made to the technical solution of the present disclosure within the technical idea of the present disclosure, and these simple modifications all belong to the protection scope of the present disclosure.
It should be noted that the various features described in the above embodiments may be combined in any suitable manner without departing from the scope of the invention. In order to avoid unnecessary repetition, various possible combinations will not be separately described in this disclosure.
In addition, any combination of various embodiments of the present disclosure may be made, and the same should be considered as the disclosure of the present disclosure, as long as it does not depart from the spirit of the present disclosure.
Claims (10)
1. An index building method for a medical data table, the method comprising:
according to a pre-established medical entity classification model, performing medical entity type identification on data elements in a medical data table to obtain a medical entity type corresponding to the medical data table;
adding the obtained medical entity type as an index item to an index set and associating with the medical data table.
2. The method of claim 1, further comprising:
receiving a query keyword input by a user;
matching the query key words with index items in the index set;
and outputting the medical data table associated with the index item successfully matched with the query keyword.
3. The method of claim 2, wherein outputting the medical data table associated with the index item successfully matched with the query keyword comprises:
determining the priority of each medical data table associated with the index item successfully matched with the query keyword;
and outputting the medical data tables associated with the index items successfully matched with the query keywords according to the sequence from high priority to low priority.
4. The method of claim 3, wherein the determining the priority of each medical data table associated with the index item successfully matched with the query keyword comprises:
determining a score for each of the medical data tables by:
wherein, good m A score representing the mth medical data table;
I m representing the total number of fields in the mth medical data table;
n mi indicates the AND contained in the ith field of the m-th medical data tableThe number of index items successfully matched by the query keyword is zero, and n is mi ＝1；
And determining the priority of the medical data table according to the score of the medical data table, wherein the smaller the score of the medical data table is, the higher the priority is.
5. An index building apparatus for a medical data table, the apparatus comprising:
the entity type identification module is used for identifying the medical entity type of the data elements in the medical data table according to a pre-established medical entity classification model to obtain the medical entity type corresponding to the medical data table;
and the processing module is used for adding the obtained medical entity type as an index item into an index set and associating the index item with the medical data table.
6. The apparatus of claim 5, further comprising:
the receiving module is used for receiving the query key words input by the user;
the matching module is used for matching the query keyword with the index items in the index set;
and the output module is used for outputting the medical data table associated with the index item successfully matched with the query keyword.
7. The apparatus of claim 6, wherein the output module comprises:
the determining sub-module is used for determining the priority of each medical data table associated with the index item successfully matched with the query keyword;
and the output sub-module is used for outputting each medical data table associated with the index item successfully matched with the query keyword according to the sequence of the priority from high to low.
8. The apparatus of claim 7, wherein the determining sub-module determines the priority of each medical data table associated with the index item successfully matched with the query keyword by:
determining a score for each of the medical data tables by:
wherein, good m A score representing the mth medical data table;
I m representing the total number of fields in the mth medical data table;
n mi indicating the number of index items successfully matched with the query keyword contained in the ith field in the mth medical data table, and when the number is zero, n mi ＝1；
And determining the priority of the medical data table according to the score of the medical data table, wherein the smaller the score of the medical data table is, the higher the priority is.
9. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 4.
10. An electronic device, comprising:
the computer-readable storage medium recited in claim 9; and
one or more processors to execute the program in the computer-readable storage medium.
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| CN109558461A (en) * | 2018-10-23 | 2019-04-02 | 平安医疗健康管理股份有限公司 | A kind of medical data classification storage method and apparatus |
| CN109582672A (en) * | 2018-11-21 | 2019-04-05 | 金色熊猫有限公司 | Data processing method, device and medium |
| CN111899822A (en) * | 2020-06-28 | 2020-11-06 | 广州万孚生物技术股份有限公司 | Medical institution database construction method, query method, device, equipment and medium |
| CN113569012A (en) * | 2021-07-28 | 2021-10-29 | 卫宁健康科技集团股份有限公司 | Medical data query method, device, equipment and storage medium |
| US11188527B2 (en) | 2017-09-29 | 2021-11-30 | Apple Inc. | Index-based deidentification |
| US11587650B2 (en) | 2017-09-29 | 2023-02-21 | Apple Inc. | Techniques for managing access of user devices to third-party resources |
| US11636163B2 (en) | 2017-09-29 | 2023-04-25 | Apple Inc. | Techniques for anonymized searching of medical providers |
| US11636927B2 (en) | 2017-09-29 | 2023-04-25 | Apple Inc. | Techniques for building medical provider databases |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102222076A (en) * | 2010-04-16 | 2011-10-19 | 无锡比比网信息有限公司 | Method and device for information comparison |
| US20160364466A1 (en) * | 2015-06-15 | 2016-12-15 | The Medical College Of Wisconsin, Inc. | Methods and apparatus for enhanced data storage based on analysis of data type and domain |
| CN106407183A (en) * | 2016-09-28 | 2017-02-15 | 医渡云（北京）技术有限公司 | Method and device for generating medical named entity recognition system |
| CN107016001A (en) * | 2016-01-28 | 2017-08-04 | 阿里巴巴集团控股有限公司 | A kind of data query method and device |
-
2017
- 2017-10-18 CN CN201710973388.7A patent/CN107992511A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102222076A (en) * | 2010-04-16 | 2011-10-19 | 无锡比比网信息有限公司 | Method and device for information comparison |
| US20160364466A1 (en) * | 2015-06-15 | 2016-12-15 | The Medical College Of Wisconsin, Inc. | Methods and apparatus for enhanced data storage based on analysis of data type and domain |
| CN107016001A (en) * | 2016-01-28 | 2017-08-04 | 阿里巴巴集团控股有限公司 | A kind of data query method and device |
| CN106407183A (en) * | 2016-09-28 | 2017-02-15 | 医渡云（北京）技术有限公司 | Method and device for generating medical named entity recognition system |
Cited By (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11188527B2 (en) | 2017-09-29 | 2021-11-30 | Apple Inc. | Index-based deidentification |
| US11822371B2 (en) | 2017-09-29 | 2023-11-21 | Apple Inc. | Normalization of medical terms |
| US11636927B2 (en) | 2017-09-29 | 2023-04-25 | Apple Inc. | Techniques for building medical provider databases |
| US11636163B2 (en) | 2017-09-29 | 2023-04-25 | Apple Inc. | Techniques for anonymized searching of medical providers |
| US11587650B2 (en) | 2017-09-29 | 2023-02-21 | Apple Inc. | Techniques for managing access of user devices to third-party resources |
| CN109144999A (en) * | 2018-08-02 | 2019-01-04 | 东软集团股份有限公司 | A kind of data positioning method, device and storage medium, program product |
| CN109558461A (en) * | 2018-10-23 | 2019-04-02 | 平安医疗健康管理股份有限公司 | A kind of medical data classification storage method and apparatus |
| CN109558461B (en) * | 2018-10-23 | 2023-08-18 | 深圳平安医疗健康科技服务有限公司 | Medical data classified storage method and device |
| CN109271392A (en) * | 2018-10-30 | 2019-01-25 | 长威信息科技发展股份有限公司 | Quick discrimination and the method and apparatus for extracting relevant database entity and attribute |
| CN109471863A (en) * | 2018-11-12 | 2019-03-15 | 北京懿医云科技有限公司 | Information query method and device, electronic equipment based on distributed data base |
| CN109582672A (en) * | 2018-11-21 | 2019-04-05 | 金色熊猫有限公司 | Data processing method, device and medium |
| CN111899822A (en) * | 2020-06-28 | 2020-11-06 | 广州万孚生物技术股份有限公司 | Medical institution database construction method, query method, device, equipment and medium |
| CN111899822B (en) * | 2020-06-28 | 2024-01-30 | 广州万孚生物技术股份有限公司 | Medical institution database construction method, query method, device, equipment and medium |
| CN113569012A (en) * | 2021-07-28 | 2021-10-29 | 卫宁健康科技集团股份有限公司 | Medical data query method, device, equipment and storage medium |
| CN113569012B (en) * | 2021-07-28 | 2023-12-26 | 卫宁健康科技集团股份有限公司 | Medical data query method, device, equipment and storage medium |
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