The Research of Constructing a Risk Assessment Model for Gastric Cancer Based on Machine Learning
试验速览
- 阶段
- 不适用
- 发起方
- 入组人数
- 5,000
- 试验地点
- 2
- 主要终点
- pepsinogen value for precanceous lesion and Gastric cancer
研究概览
简要总结
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 25 Years 至 75 Years(Adult, Older Adult)
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •1) intention to undergo gastroscopy during health checkup examination; and 2) 25-75 years of age
排除标准
- •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.
结局指标
主要结局
pepsinogen value for precanceous lesion and Gastric cancer
时间窗: 1 year
pepsinogen value for precanceous lesion and Gastric cancer
次要结局
未报告次要终点
