An individualized risk prediction tool for ectopic pregnancy within the first 10 weeks of gestation based on machine learning algorithms
This study employed the CatBoost algorithm to develop an individualized risk prediction model by integrating multiple features from the initial visit. This model enhances the detection rate of EP in patients with PUL during early pregnancy. Additionally, we created a web-based tool, offering potential for future clinical applications.
Authors
- Xin Du (ORCID: https://orcid.org/0000-0002-9536-6595)
- Jianjun Zou (ORCID: https://orcid.org/0000-0003-0886-3153)
- Kaizong Huang (ORCID: https://orcid.org/0000-0002-4926-6980)
- Chunya Ji (ORCID: https://orcid.org/0000-0002-7664-0819)
- Mengmeng Lü (ORCID: https://orcid.org/0000-0003-4045-0615)
- Hongjie Ruan (ORCID: https://orcid.org/0000-0001-5318-2037)
- Qianping Chen
- Chen Chen (ORCID: https://orcid.org/0009-0002-9235-2718)
- Zhou Zhou (ORCID: https://orcid.org/0000-0002-7674-9857)
- Jing Hu (ORCID: https://orcid.org/0000-0002-0995-2839)
Institutions
- China Pharmaceutical University (CN)
- Nanjing Maternity and Child Health Care Hospital (CN)
- Nanjing Medical University (CN)
Publication Details
- Journal
- Frontiers in Medicine
- Published
- 2025-12-09
- DOI
- https://doi.org/10.3389/fmed.2025.1726606
- Citations
- 1
- Primary Topic
- Preterm Birth and Chorioamnionitis
- Type
- article
- Field-Weighted Citation Impact
- 1.35
Funders
- Jiangsu Pharmaceutical Association
- National Natural Science Foundation of China