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Quantitative analysis method for onset risk factors of old-age lung cancer

A risk factor and quantitative analysis technology, applied in the field of medical biological information processing, can solve problems such as unclear quantitative correlations, and achieve the effects of high calculation accuracy, high accuracy, and simple operation

Pending Publication Date: 2022-01-14
中国医学科学院医学信息研究所
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Problems solved by technology

[0004] The purpose of the present invention is to solve the problem that the quantitative correlation between the incidence of elderly lung cancer and various risk factors is not yet clear, and propose a quantitative analysis method for the risk factors of lung cancer incidence in the elderly. risk factors

Method used

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  • Quantitative analysis method for onset risk factors of old-age lung cancer
  • Quantitative analysis method for onset risk factors of old-age lung cancer
  • Quantitative analysis method for onset risk factors of old-age lung cancer

Examples

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Embodiment 1

[0033] This example describes the determination of the risk factors of the internally lung cancer in the epidural lung cancer based on deep neural network quantitative analysis of the risk factors of elderly lung cancer, combined figure 1 Includes the following steps:

[0034] Step 1, obtain the survey data of the elderly, integrated formation of senile survey data in many fields, specifically investigating data, meteorological and environmental data, meteorology and environmental data, integrated across domain data sources M;

[0035] In part of the 1996-2017 adult investigation data as a part of the model, the elderly accounted for 35%, while integrating meteorological data, environmental data and survey data together according to the corresponding date of the survey data, forming cross-domain data source M, Communist as an input of the risk factor for the pathogenesis of elderly lung cancer;

[0036] Step 2, on the basis of step 1 to obtain data pretreatment, the specific proce...

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Abstract

The invention relates to a quantitative analysis method for morbidity risk factors of old-age lung cancer, and belongs to the technical field of medical biological information processing. The method integrates relevant data of the investigated object, such as demographic data, smoking habits, disease history, radiation exposure and behavioral risk relevant data; aiming at the problem that the actual number of patients suffering from the lung cancer is far lower than the number of non-patients, data imbalance processing is carried out, and then the data is preprocessed and layered; a deep neural network method is used to train models of the senile hierarchical data, respective risk factors are recognized, and quantitative analysis of the risk factors of the senile lung cancer is carried out. The method has the advantages of high precision and high calculation speed, and can be used for high-speed calculation of large-scale data; the method has the advantages of quantitative analysis and high accuracy, and is simple to operate.

Description

Technical field [0001] The present invention relates to a quantitative analysis method of risk factors in elderly lung cancer, belonging to the technical field of medical bob information processing. Background technique [0002] Lung cancer has become a malignant tumor with the fastest growing rate and mortality growth, especially in the elderly, and has a great impact on their quality of life, and also brings huge economic pressure to the country and family. At the same time, senile lung cancer Hidden, clinical manifestations are non-specific, and it is prone to misdiagnosis and missed diagnosis, and the age is large, and it is easy to accompany or coexist with multiple system diseases, resulting in an increase in subsequent treatment. In recent years, with the growing aging of my country's aging, it is increasingly urgent to conduct active and effective prevention and control of lung cancer against the elderly. However, lung cancer is a complex process involving a combination o...

Claims

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Application Information

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IPC IPC(8): G16H50/20G16H50/30G06F30/27G06F119/02
CPCG16H50/20G16H50/30G06F30/27G06F2119/02
Inventor 陈松景吴思竹
Owner 中国医学科学院医学信息研究所
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