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Random-forest-algorithm-based method and system for identifying disease diagnosis complication

A random forest algorithm and disease diagnosis technology, applied in the field of disease diagnosis and complication identification method and system based on random forest algorithm, can solve problems such as lack of analysis, neglect of medical resource allocation, etc., and achieve the effect of improving scientificity

Inactive Publication Date: 2018-08-07
天津艾登科技有限公司
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Problems solved by technology

This artificially designated list of important complications has certain limitations, ignoring the different manifestations of complications and different medical resource allocations in different regions, and lack of analysis based on specific data

Method used

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  • Random-forest-algorithm-based method and system for identifying disease diagnosis complication
  • Random-forest-algorithm-based method and system for identifying disease diagnosis complication
  • Random-forest-algorithm-based method and system for identifying disease diagnosis complication

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Embodiment Construction

[0020] Embodiments of the present invention are described in detail below, examples of which are shown in the drawings, wherein the same or similar reference numerals designate the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the figures are exemplary and are intended to explain the present invention and should not be construed as limiting the present invention.

[0021] The present invention proposes a disease diagnosis complication identification method and system based on a random forest algorithm, which can distinguish important complications based on statistical analysis of data.

[0022] like figure 1 As shown, the method for identifying complications of disease diagnosis based on random forest algorithm in the embodiment of the present invention includes the following steps:

[0023] In step S1, the diseases are classified according to the human anatomical system to form a pluralit...

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Abstract

The invention provides a random-forest-algorithm-based method and system for identifying a disease diagnosis complication. The method comprises: diseases are classified according to a human anatomy system to form a plurality of disease diagnosis group anatomical categories; according to disease diagnosis and operation, each disease diagnosis group anatomical category is subdivided to form a plurality of disease diagnosis grouping operation categories; and complications in the disease diagnosis grouping operation categories are subdivided by using a random forest algorithm to form a final disease diagnosis standard group. According to the invention, on the basis of specific case data, complications with similar clinical processes and similar resource consumption are classified into one category by using the random forest algorithm so as to realize automatic grouping of cases, so that the support is provided for medical insurance payment and medical service performance evaluation. Therefore, the scientific, stable and accurate performances of medical resource management are improved.

Description

technical field [0001] The invention relates to the field of computer application technology, in particular to a method and system for identifying complications of disease diagnosis based on a random forest algorithm. Background technique [0002] In disease diagnosis-related groupings, for a group of diseases with similar clinical processes, it is necessary to distinguish complications of different degrees of importance based on the consumption of medical resources. In the previous disease grouping operations, the distinction of complications of different importance was mainly based on the artificially (using clinical knowledge) designated list of important complications. This artificially designated list of important complications has certain limitations, ignoring the different manifestations of complications and different allocation of medical resources in different regions, and lack of analysis based on specific data. Contents of the invention [0003] The aim of the ...

Claims

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Application Information

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IPC IPC(8): G16H50/20
CPCG16H50/20
Inventor 孙广阳程岚苏倩
Owner 天津艾登科技有限公司
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