A web-based tool for predicting gastric ulcers in Chinese elderly adults based on machine learning algorithms and noninvasive predictors: A national cross-sectional and cohort study
We developed MyGutRisk, built on optimal machine learning models, relatively accurately predicts gastric ulcer risk in elderly adults using noninvasive factors like diet and lifestyle. It supports self-assessment via a public link and clinical screening in community health settings to guide preventive measures. However, as a prototype, it requires further validation to ensure accuracy and generalizability across diverse populations and real-world applications.
Authors
- Zongyuan Ge (ORCID: https://orcid.org/0000-0002-5880-8673)
- Hailei Zhao (ORCID: https://orcid.org/0000-0001-5564-3677)
- Xingjian Xiao
- Suxian Liu (ORCID: https://orcid.org/0000-0003-0541-5267)
- Hualing Song
- Xinming Yang
- Tiantian Liang (ORCID: https://orcid.org/0000-0003-3954-0373)
- Xianglong Xu (ORCID: https://orcid.org/0000-0003-1873-4734)
- Zumin Shi (ORCID: https://orcid.org/0000-0002-3099-3299)
- Bo Sun (ORCID: https://orcid.org/0000-0003-3172-7811)
- Xiaohan Yi
Institutions
- Shanghai University of Traditional Chinese Medicine (CN)
- Longhua Hospital Shanghai University of Traditional Chinese Medicine (CN)
- Monash University (AU)
- Qatar University (QA)
Publication Details
- Journal
- Digital Health
- Published
- 2025-04-01
- DOI
- https://doi.org/10.1177/20552076251336951
- Primary Topic
- Helicobacter pylori-related gastroenterology studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Shanghai Municipal Health Commission
- Shanghai Pudong New Area Health Commission