Method and system for generating DIP comprehensive disease category directory based on big data clustering

A big data and data technology, applied in the field of generating DIP comprehensive disease catalog based on big data clustering, can solve the problems of not publishing the comprehensive catalog and being unable to formulate and use the catalog, so as to improve accuracy, implementation efficiency, and good fitting cost Effect

Pending Publication Date: 2021-05-11
SHAN DONG MSUN HEALTH TECH GRP CO LTD
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  • Summary
  • Abstract
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  • Claims
  • Application Information

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Problems solved by technology

[0005] The inventor found that the current status quo is that the combination of the main catalog formulated by the Medical Insurance Bureau is the core catalog + comprehensive catalog, but currently only the core catalog has been published but not the comprehensive catalog. The plan for generating the catalog, this set of plans needs to take into account the differences in actual data in various places, and the catalog of core diseases will also change accordingly, which makes it impossible to formulate a fixed catalog for use

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  • Method and system for generating DIP comprehensive disease category directory based on big data clustering

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Experimental program
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Embodiment 1

[0033] Such as figure 1 As shown, Embodiment 1 of the present disclosure provides a method for generating a DIP comprehensive disease catalog based on big data clustering, including the following steps:

[0034] S1: Manage the data on the first page of medical records.

[0035] S1.1: Firstly, the data on the front page of the medical records are included in the core disease catalog issued by the state, and the data that cannot be included in the core disease catalog group are taken out for processing and rectification;

[0036] S1.2: Adjust the coding format, and map the main diagnosis of the "National Clinical Edition 2.0 Disease Diagnosis Code (ICD-10)" on the homepage of the medical record to "Medical Insurance Disease Diagnostic Classification and Code (ICD-10)", and "National "Clinical version 2.0 Surgical Operation Code (ICD-9-CM3)" is mapped to "Medical Insurance Surgical Operation Classification and Coding (ICD-9-CM-3)";

[0037] S1.3: Discard the data containing wro...

Embodiment 2

[0054] Embodiment 2 of the present disclosure provides a system for generating a DIP comprehensive disease catalog based on big data clustering, including:

[0055] The data acquisition module is configured to: acquire medical record data, and preprocess the acquired medical record data;

[0056] The data segmentation module is configured to: divide the data into a plurality of sub-data, and assign a corresponding number of Map functions;

[0057] The data clustering module is configured as: the stage of the data Map, clustering the sub-data;

[0058] The data merging module is configured as follows: In the data reduction stage, the Map results of the sub-data are merged to obtain the final DIP comprehensive disease catalog.

[0059] The working method of the system is the same as the method for generating the DIP comprehensive disease catalog based on big data clustering provided in Example 1, and will not be repeated here.

Embodiment 3

[0061] Embodiment 3 of the present disclosure provides a computer-readable storage medium, on which a program is stored. When the program is executed by a processor, the method of generating a DIP comprehensive disease catalog based on big data clustering as described in Embodiment 1 of the present disclosure is realized. Steps in the method, the steps being:

[0062] Obtain medical record data and preprocess the acquired medical record data;

[0063] Divide the data into multiple sub-data, and assign the corresponding number of Map functions;

[0064] In the data Map stage, the sub-data is clustered;

[0065] In the data reduction stage, the Map results of the sub-data are merged to obtain the final DIP comprehensive disease catalog.

[0066] The detailed steps are the same as the method for generating the DIP comprehensive disease catalog based on big data clustering provided in Example 1, and will not be repeated here.

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Abstract

The invention provides a method and system for generating a DIP comprehensive disease category directory based on big data clustering, and the method comprises the steps: obtaining medical record data, and carrying out the preprocessing of the obtained medical record data; dividing the data into a plurality of sub-data, and distributing a corresponding number of Map functions; clustering the sub-data in the data Map stage; in the data Reduce stage, combining the Map results of the sub-data to obtain a final DIP comprehensive disease category directory. The generated DIP comprehensive disease category catalogue can change along with different data of various places, so that the disease category catalogue conforming to disease data characteristics of various places can be formulated, and the accuracy and implementation efficiency of payment according to disease category scores are improved.

Description

technical field [0001] The present disclosure relates to the field of big data technology, in particular to a method and system for generating a DIP comprehensive disease catalog based on big data clustering. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art. [0003] As the National Medical Insurance Bureau continues to issue documents to promote payment by disease type (DIP), a list of multiple payment by point value pilot cities has also been announced, as well as the "National Medical Security Payment by Disease Point (DIP) Technical Specification" and With the release of "DIP Catalog (Version 1.0)", the implementation of DIP is imminent. [0004] However, if you want to actually use DIP to pay, the most important link is to enroll each case according to the surgical code, diagnosis code, age and other characteristics, and then formulate the sc...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16H70/60G06K9/62
CPCG16H70/60G06F18/23
Inventor 吴军高希余李朋飞樊昭磊卢法臣支应然王子阳
Owner SHAN DONG MSUN HEALTH TECH GRP CO LTD
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