Based on the gastric cancer database established earlier, this project explored the PG standard suitable for Chinese people, and further explored the establishment of machine learning model to stratify gastric cancer risk in the population, guide the frequency of gastroscopy screening, and extract important gastric cancer risk factors from it.Establish electronic health records of gastric organs, track the development and outcome of gastric diseases through deep learning method, in order to predict the development and outcome of gastric diseases;Then, the simulation hypothesis deductive method is used to compare the outcomes that may be caused by different lifestyles with the help of deep learning model, so as to guide patients to develop a better lifestyle and explore the establishment of health management paths for gastric cancer patients and high-risk groups in China.
Study Type
OBSERVATIONAL
Enrollment
5,000
diagnostic value of pepsinogen for severe atrophy and gastric cancer
Zhejiang Provincial Hospital of Traditional Chinese Medicine
Hangzhou, China
RECRUITINGNingbo cadres health center
Ningbo, China
RECRUITINGpepsinogen value for precanceous lesion and Gastric cancer
pepsinogen value for precanceous lesion and Gastric cancer
Time frame: 1 year
Yi Zhao, Master
CONTACT
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