Identifying Data-Driven Clinical Subgroups for Cervical Cancer Prevention With Machine Learning: Population-Based, External, and Diagnostic Validation Study

This study underscores the potential of leveraging machine learning algorithms and large-scale routine electronic health records to enhance CCP strategies. By identifying key determinants of CIN2+/CIN3+ risk and classifying 5 distinct subgroups, our study provides a robust, data-driven foundation for the proposed triple strategy. This approach prioritizes tailored prevention efforts for subgroups with varying risks, offering a novel and scalable tool to complement existing cervical cancer screening guidelines. Future work should focus on independent external and prospective validation to maximize the global impact of this strategy.

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Publication Details

Journal
JMIR Public Health and Surveillance
Published
2025-03-19
DOI
https://doi.org/10.2196/67840
Citations
4
Primary Topic
Cervical Cancer and HPV Research
Type
article
Field-Weighted Citation Impact
5.42
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article

Identifying Data-Driven Clinical Subgroups for Cervical Cancer Prevention With Machine Learning: Population-Based, External, and Diagnostic Validation Study

Bingyi Wang, Juntao Wang, Weijie Zhang, Shuxia Xu et al.
4 citations
JMIR Public Health and Surveillance
Cervical Cancer and HPV Research
5.42
article

Identifying Data-Driven Clinical Subgroups for Cervical Cancer Prevention With Machine Learning: Population-Based, External, and Diagnostic Validation Study

Bingyi Wang, Juntao Wang, Weijie Zhang, Shuxia Xu, Pengming Sun, Binhua Dong, Xunyuan Tuo, Xinxin Huang, Huachun Zou, Leiwen Fu, Junfeng Wang, Zhen Lu, Tian Tian, Zheng Zheng, Siyang Liu, Huifeng Xue, Hongning Cai, Shanshan Lin, Tianjie Yang
article en
4 citations

Abstract

This study underscores the potential of leveraging machine learning algorithms and large-scale routine electronic health records to enhance CCP strategies. By identifying key determinants of CIN2+/CIN3+ risk and classifying 5 distinct subgroups, our study provides a robust, data-driven foundation for the proposed triple strategy. This approach prioritizes tailored prevention efforts for subgroups with varying risks, offering a novel and scalable tool to complement existing cervical cancer screening guidelines. Future work should focus on independent external and prospective validation to maximize the global impact of this strategy.

JMIR Public Health and SurveillanceVol. 11
Fujian Medical University (CN), Xinjiang Medical University (CN), Sun Yat-sen University (CN), Capital Medical University (CN), Utrecht University (NL), Chinese Academy of Medical Sciences & Peking Union Medical College (CN), Fudan University (CN), Peking Union Medical College Hospital (CN), Fujian Women and Children Hospital (CN), Foshan Maternity and Child Health Care Hospital (CN), The First People's Hospital of Shunde (CN), Shenzhen Maternity and Child Healthcare Hospital (CN), Beijing Chest Hospital (CN), Gansu Provincial Maternal and Child Health Hospital (CN), Hubei Provincial Women and Children's Hospital (CN), Fuzhou Maternity and Child Health Care Hospital (CN), Guangdong Provincial Center for Disease Control and Prevention (CN)
Good health and well-being
Openalex Percentile: Top 5%
Cervical Cancer and HPV Research
5.42
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