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Multi -dimensional data detection methods, devices, equipment and media

A technology of outlier detection and multi-dimensional data, which is applied in the computer field, can solve problems such as influence and large differences, and achieve the effects of avoiding differences, comprehensive detection, and good adaptability

Active Publication Date: 2022-08-05
福建亿能达信息技术股份有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

Moreover, the difference between the two is also quite large. The length of hospitalization is generally about 10 days, while the hospitalization fee is in the thousands or ten thousand yuan and is affected by prices.

Method used

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  • Multi -dimensional data detection methods, devices, equipment and media
  • Multi -dimensional data detection methods, devices, equipment and media
  • Multi -dimensional data detection methods, devices, equipment and media

Examples

Experimental program
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Embodiment 1

[0040] like figure 1As shown, this embodiment provides an outlier detection method for multi-dimensional data, including the following steps:

[0041] S1. Extract the multi-dimensional data of different dimensions that need to be involved in the calculation of the detection target;

[0042] S2. Check whether the data in each dimension obeys the normal distribution. If not, perform data conversion so that the converted data in each dimension obeys the normal distribution; if the data obeys the normal distribution, there is no need for data conversion;

[0043] S3. Calculate the initial Mahalanobis distance of the multi-dimensional data that has obeyed the normal distribution based on the average value of each dimensional data; the initial Mahalanobis distance M is calculated by formula (1):

[0044]

[0045] in, is the vector mean, μ 1 , μ 2 ,…μ n are the mean of the 1st, 2nd,..., n-dimensional data respectively, S is a multi-dimensional vector The covariance matrix ...

Embodiment 2

[0103] like figure 2 As shown, in this embodiment, an apparatus for detecting outliers of multi-dimensional data is provided, including:

[0104] The data extraction module is used to extract the multi-dimensional data of the detection target that needs to participate in the calculation, and each dimension of data represents data of one dimension;

[0105] The test and conversion module is used to test whether the data of each dimension obeys the normal distribution, if not, perform data conversion to make the converted data of each dimension obey the normal distribution;

[0106] The Mahalanobis distance calculation module is used to calculate the initial Mahalanobis distance of the multi-dimensional data that has obeyed the normal distribution based on the average value of each dimensional data;

[0107] an adjustment module, configured to adjust and calculate the initial Mahalanobis distance through an adjustment coefficient to obtain the adjusted Mahalanobis distance;

[...

Embodiment 3

[0134] This embodiment provides an electronic device, such as image 3 As shown, a memory, a processor, and a computer program stored in the memory and running on the processor are included. When the processor executes the computer program, any implementation manner of the first embodiment can be implemented.

[0135] Since the electronic device introduced in this embodiment is the device used to implement the method in the first embodiment of the present application, based on the method introduced in the first embodiment of the present application, those skilled in the art can understand the electronic device in this embodiment. The specific implementation manner and various modifications thereof, so how the electronic device implements the methods in the embodiments of the present application will not be described in detail here. As long as the devices used by those skilled in the art to implement the methods in the embodiments of the present application fall within the scop...

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Abstract

The present invention provides a method, device, equipment and medium for detecting outliers of multi-dimensional data. The method includes: S1, extracting multi-dimensional data of different dimensions that the detection target needs to participate in calculation; S2, checking whether each dimension data obeys a normal distribution, If not, perform data transformation to make the transformed data of each dimension obey the normal distribution; S3. Calculate the initial Mahalanobis distance of the multi-dimensional data that has obeyed the normal distribution based on the average value of the data in each dimension; S4. Pass The adjustment coefficient is used to adjust and calculate the initial Mahalanobis distance to obtain the adjusted Mahalanobis distance; S5, determine the outlier value of the adjusted Mahalanobis distance according to a predetermined rule. The invention can consider multi-dimensional data at the same time, avoid differences caused by single one-dimensional data, so that the detection of outliers is more comprehensive, scientific, accurate and systematic, and high and low outliers can be distinguished according to the results.

Description

technical field [0001] The present invention relates to the field of computer technology, and in particular, to a method, apparatus, device and medium for outlier detection of multidimensional data of different dimensions. Background technique [0002] In the field of medical and health management, in order to make hospital cases comparable, disease comparison based on disease diagnosis group (DRG, Diagnosis-related Group) is a common method at present. DRG was developed by Professor Robert B. Fetter of Yale University and his team after more than ten years. The DRG classifies cases with similar clinical processes and / or equivalent resource consumption into one category, and combines them into several groups. Different "weights" are formulated between groups to reflect the characteristics of each group. Thus, cases in the same group can be directly compared, respectively, based on the cost of hospitalization and the length of hospitalization, which are currently commonly us...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G16H10/00G06F17/18
CPCG16H10/00G06F17/18
Inventor 林曙光黄家昌邱道椿王应明
Owner 福建亿能达信息技术股份有限公司
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