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Tumor prediction method

A prediction method and tumor technology, applied in the field of tumor diagnosis, can solve problems such as lack of good tumor prediction methods, large malignant tumors, etc., achieve accurate tumor prediction, meet the needs of use, and design reasonable effects

Inactive Publication Date: 2018-03-23
浙江鸿赋堂健康管理有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] Benign tumors do little harm to the human body, while malignant tumors do great harm to the human body. Malignant tumors are divided into early, middle and late stages. Prediction is necessary, but there is no good tumor prediction method

Method used

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Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0020] A tumor prediction method, the specific steps are as follows:

[0021] Step 1, collect historical tumor data from various types of malignant tumor patients, and establish a tumor database;

[0022] Step 2: According to the tumor database, the proportional hazard function used to describe the relationship between the patient and the probability of death is established through Kalman filter, finite element and neural network algorithms, and different impact factors are set for different historical tumor data. Various impact factors The sum is 1;

[0023] Step 3: Obtain the gene expression data, living habits and CT images of the subject, convert the data of the subject, and obtain the converted new test sample data;

[0024] Step 4, filling the new converted test sample with missing values ​​to obtain test sample data without missing values;

[0025] Step 5, substituting the test sample data without missing values ​​into the proportional hazard function to obtain the pr...

Embodiment 2

[0027] A tumor prediction method, the specific steps are as follows:

[0028] Step 1. Collect historical tumor data for various types of malignant tumor patients. Historical tumor data include the patient’s tumor gene expression data, living habits, tumor location, CT images, biopsy pathological examination data, and the patient’s history from onset. Time to death, survival or death data, to establish a tumor database;

[0029] Step 2: According to the tumor database, the proportional hazard function used to describe the relationship between the patient and the probability of death is established through Kalman filter, finite element and neural network algorithms, and different impact factors are set for different historical tumor data. Various impact factors The sum is 1;

[0030] Step 3, obtain the gene expression data, living habits and CT images of the subject, map the data of the subject to a matrix form, and then use Laplace transform to obtain the new test sample data;...

Embodiment 3

[0034] A tumor prediction method, the specific steps are as follows:

[0035] Step 1: Collect historical tumor data for various types of malignant tumor patients, delete obviously special data and establish a tumor database;

[0036] Step 2: According to the tumor database, the proportional hazard function used to describe the relationship between the patient and the probability of death is established through Kalman filter, finite element and neural network algorithms, and different impact factors are set for different historical tumor data. Various impact factors The sum is 1;

[0037] Step 3: Obtain the gene expression data, living habits and CT images of the subject, convert the data of the subject, and obtain the converted new test sample data;

[0038] Step 4, fill the missing value of the converted new detection sample through the pre-defined cosine similarity function, and obtain the detection sample data without missing value;

[0039] Step 5, substituting the test ...

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PUM

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Abstract

The invention discloses a tumor prediction method comprising the following steps: 1, building a tumor database; 2, building a proportion risk function according to the tumor database; 3, obtaining gene expression data, life habits and a CT image of a detected person, converting the data of the detected person, thus obtaining the converted new detection sample data; 4, filling deletion values for the converted new detection samples, thus obtaining detection sample data containing no deletion value; 5, substituting the detection sample data containing no deletion value into the proportion risk function, thus obtaining a tumor predicted risk value. The method gathers history tumor data of different types of malignant tumor patients so as to build the database, further builds the proportion risk function, then converts the data of the detected person and fills the deletion value, and substitutes the filled value into the proportion risk function, thus building the tumor prediction model, and more accurately predicting tumors.

Description

technical field [0001] The invention relates to the field of tumor diagnosis, in particular to a tumor prediction method. Background technique [0002] With the change of people's living habits and people's eating habits, more and more tumors are produced on people's bodies. Tumor refers to the new organism formed by the body under the action of various tumorigenic factors, the cells of the local tissue lose the normal regulation of its growth at the gene level, resulting in abnormal proliferation and differentiation. Once the new organism is formed, it will not stop due to the elimination of the cause Growth, its growth is not regulated by normal body physiology, but destroys normal tissues and organs, because this new organism is mostly in the form of space-occupying blocky protrusions, also known as neoplasms. According to the pathological form, growth mode and degree of harm to patients, tumors are divided into two categories: benign tumors and malignant tumors, and can...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16H50/30
Inventor 吴慧斌吴中中黄芳芳吴骋李军
Owner 浙江鸿赋堂健康管理有限公司