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Depression patient identification system and identification method thereof

An identification system and identification method technology, applied in the identification system of depression patients and their identification field, can solve problems such as difficulties in early screening of depression, and achieve the effect of promoting prevention and improving accuracy

Inactive Publication Date: 2020-11-06
SOUTHEAST UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] Purpose of the invention: In view of the above problems, the present invention provides an identification system for patients with depression and its identification method, which solves the problem of difficulty in the early screening of depression, and corrects the risky behavior of patients in time to avoid depression for early intervention in the future The occurrence and further aggravation laid the foundation for

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  • Depression patient identification system and identification method thereof
  • Depression patient identification system and identification method thereof
  • Depression patient identification system and identification method thereof

Examples

Experimental program
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Effect test

Embodiment 1

[0039] Such as Figure 1-3 As shown, the present invention is a depression patient identification system and its identification method, which specifically includes the following implementation steps:

[0040] S1: Collect and organize the data, construct the original data set, the data is the EWAS analysis result of the DNA methylation of the candidate gene;

[0041] S2: Data preprocessing is performed on the original data set, and the input data set is obtained after discarding missing values;

[0042] S3: Randomly divide the input data set into training data set and test data set according to the ratio of 0.8:0.2;

[0043] S4: Create a deep learning model to identify patients with depression and healthy people, and use the training data set to train the constructed deep learning model;

[0044] S5: Use the test set to evaluate the performance of the trained deep learning model, and continuously optimize the model during the verification evaluation process to obtain the opti...

Embodiment 2

[0058] Such as Figure 4 As shown, based on the method in Example 1, a total of 391 samples of 291 depression patients and 100 healthy controls who met the criteria were tested, and the results were calculated for comparison.

[0059] 1. Perform methylation sequencing of candidate genes on 391 samples that meet the criteria for inclusion. The candidate genes are: HTR1A, HTR1B, S100A10 and BDNF. Quality control and analysis of the sequencing results will result in EWAS analysis of DNA methylation The result is used as the original data set, in which the number of sequencing sites is 449;

[0060] 2. Raw data set for data preprocessing. Considering the reasons for the deletion of some sequencing sites, the filling cannot reflect the real sequencing results, so the missing values ​​are directly discarded. After missing values ​​are processed, the number of corresponding sequencing sites is 406, and the number of samples is 333;

[0061] 3. Divide the input data obtained after ...

Embodiment 3

[0066] Such as Figure 5 As shown, a system for identifying patients with depression based on a deep learning model is proposed, including:

[0067] A: data preprocessing unit, which can preprocess the EWAS analysis results of candidate gene DNA methylation sequencing;

[0068] B: Model creation unit, which can create a deep learning model to identify patients with depression and healthy people

[0069] C: a model optimization unit, which can optimize the deep learning model;

[0070] D: data identification unit, which can effectively identify the methylation sequencing data to be distinguished based on the optimized deep learning model.

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Abstract

The invention discloses a depression patient identification system and an identification method thereof. The method comprises the following steps: collecting and arranging data, and constructing an original data set; performing data preprocessing on the original data set to obtain an input data set; dividing the input data set into a training data set and a test data set according to a certain ratio; establishing a deep learning model for identifying depression patients and healthy people, and training the established deep learning model by utilizing the training data set; and performing performance evaluation on the trained deep learning model by using the test set, and continuously optimizing the model in a verification evaluation process to obtain an optimal model. According to the invention, early identification of depression mental disorder can be realized efficiently and conveniently, and depression treatment accuracy is improved.

Description

technical field [0001] The invention belongs to the field of data identification, in particular to a depression patient identification system and an identification method thereof. Background technique [0002] Clinical identification and diagnosis of depression is mainly based on patient interviews, scales, and doctor's experience in diagnosis and treatment. This method is easily affected by subjective factors such as the degree of cooperation of the patient and the proficiency of the doctor. Therefore, finding a fast, objective and accurate method for identification and diagnosis of depression is of great significance for individual treatment. Contents of the invention [0003] Purpose of the invention: In view of the above problems, the present invention provides an identification system for patients with depression and its identification method, which solves the problem of difficulty in the early screening of depression, and corrects the risky behavior of patients in t...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16H50/20G16B20/30G16B40/00G06N3/04A61B5/16
CPCG16H50/20G16B20/30G16B40/00A61B5/165G06N3/045
Inventor 李健徐治胡云云
Owner SOUTHEAST UNIV
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