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

NCT04957407 · Status: UNKNOWN · Type: OBSERVATIONAL · Enrollment: 5000

Last updated 2021-07-12

No results posted yet for this study

Summary

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.

Conditions

Interventions

OTHER

pepsinogen

diagnostic value of pepsinogen for severe atrophy and gastric cancer

Sponsors & Collaborators

  • Second Affiliated Hospital, School of Medicine, Zhejiang University

    lead OTHER

Principal Investigators

  • Yuling Tong, Dr. · 2nd affiliated hospital of Zhejiang University, school of medicine

Eligibility

Min Age
25 Years
Max Age
75 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2019-01-01
Primary Completion
2022-05-31
Completion
2022-12-31

Countries

  • China

Study Locations

More Related Trials

Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT04957407 on ClinicalTrials.gov