The Research of Constructing a Risk Assessment Model for Gastric Cancer Based on Machine Learning

Zhejiang University

Status

Unknown

Conditions

Gastric Cancer

Mechine Learning

Precancerous Lesion

Treatments

Other: pepsinogen

Study type

Observational

Funder types

Other

Identifiers

NCT04957407

71804161

Details and patient eligibility

About

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.

Enrollment

5,000 estimated patients

Sex

All

Ages

25 to 75 years old

Volunteers

No Healthy Volunteers

Inclusion criteria

1） intention to undergo gastroscopy during health checkup examination; and 2) 25-75 years of age

Exclusion criteria

1. a history of gastric ulcer, gastric polyp, or GC; 2) a history of gastrectomy; 3) treatment with a proton pump inhibitor in the last month; 4) contraindications to gastroscopy; 5) a history of Hp eradication; 6) a history of abdominal pain, abdominal distention, belching, acid reflux, nausea and other digestive tract symptoms within 1 month or 67) incomplete data.