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Classification method for processing electronic medical record hybrid data based on crowd network

A technology of electronic medical records and mixed data, applied in text database clustering/classification, medical data mining, patient-specific data, etc., can solve the problem of few methods for processing symbolic data

Active Publication Date: 2019-08-23
芽米科技(广州)有限公司
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Problems solved by technology

There are currently few methods for dealing with symbolic data

Method used

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  • Classification method for processing electronic medical record hybrid data based on crowd network
  • Classification method for processing electronic medical record hybrid data based on crowd network
  • Classification method for processing electronic medical record hybrid data based on crowd network

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Embodiment Construction

[0050] In order to make the object, technical solution and effect of the present invention clearer and clearer, the present invention will be further described in detail in conjunction with the accompanying drawings. It should be understood that the specific implementations described here are only used to explain the present invention, not to limit the present invention.

[0051] The present invention provides an implementation flowchart of a classification method for processing mixed data of electronic medical records based on Crowdwisdom Network, as shown in figure 1 As shown, the schematic diagram of the electronic medical record is figure 2 , the process includes:

[0052] Step 1. Extract the original electronic medical record data set in the electronic medical record database in Zhongzhi Network, and perform data cleaning on the data set;

[0053] Step 2. Judgment of symbolic data on the cleaned electronic medical record data set, if there is symbolic data, conversion ...

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Abstract

The invention relates to a classification method for processing electronic medical record hybrid data based on a crowd network, wherein an existing numerical data processing method can be effectivelyutilized for improving classification efficiency of the hybrid data in the electronic medical record, and improving diagnosis quality and efficiency of a doctor. The method comprises the steps of 1, extracting an original electronic medical record dataset in an original electronic medical record database; 2, performing symbol type data determining on the cleaned electronic medical record dataset,and performing numerical data conversion on the symbol type data; 3, extracting a key characteristic field in the original electronic medical record dataset, and training a classification diagnosis model; and 4, extracting a key characteristic field in the to-be-diagnosed electronic medical record, inputting into the trained model, and outputting a classification result and a disease suffering probability. The method can effectively mine valuable information in the electronic medical record for helping the doctor in quick condition diagnosis, thereby realizing an important theoretical meaningand high application value.

Description

technical field [0001] The invention relates to a crowd-wisdom network and the fields of electronic medical records and big data. It is an analysis method for processing mixed data, which can classify and process mixed data of massive electronic medical records. Background technique [0002] Crowd Network is a large-scale simulation and experiment platform built to explore crowd intelligence activities in a large-scale online environment such as Crowd Intelligence Science. In the Wisdom Network, people, machines, objects, etc. are heterogeneously interconnected, and there are different types of data in the network. Electronic Medical Record (EMR, Electronic Medical Record) is also called a computerized medical record system or a computer-based patient record. It is a digital medical record that is saved, managed, transmitted and reproduced by electronic devices (computers, health cards, etc.) to replace handwritten paper medical records. Its content includes all the infor...

Claims

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Application Information

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IPC IPC(8): G16H10/60G16H50/70G06F16/215G06F16/35
CPCG16H10/60G16H50/70G06F16/215G06F16/355G06N3/045
Inventor 李建强王延安李鹏智
Owner 芽米科技(广州)有限公司
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