Development and internal-external validation of a risk prediction model for acute pain after HAIC for patients with liver cancer using logistic regression and XGBoost algorithm
XGBoost model has higher accuracy and stronger robustness in predicting acute moderate-to-severe pain after HAIC in patients with liver cancer, which will facilitate risk assessment and implement precise and early interventions.
Authors
- George Zhang (ORCID: https://orcid.org/0000-0003-2073-7109)
- Yina Gong
- J. Cao
- Chunyan Chen
- Minghui Xie
- Wenjuan Zhao
- Fan Wang
Institutions
- Second Military Medical University (CN)
- Fudan University (CN)
- Fudan University Shanghai Cancer Center (CN)
- Changhai Hospital (CN)
- Zhongshan Hospital (CN)
Publication Details
- Journal
- Asia-Pacific Journal of Oncology Nursing
- Published
- 2026-02-18
- DOI
- https://doi.org/10.1016/j.apjon.2026.100923
- Primary Topic
- Pain Management and Opioid Use
- Type
- article
- Field-Weighted Citation Impact
- 0.00