Explainable machine learning to predict postoperative ileus after radical cystectomy: an 11-year real-world cohort
An interpretable ML model based on routinely captured peri-operative variables accurately stratifies RC patients at risk for POI as early as postoperative day 0, outperforming existing nomograms and highlighting modifiable factors. Embedding this tool into electronic-health-record workflows could enable real-time alerts and risk-adapted management. Prospective multicentre validation is warranted.
Authors
- Mengxiao Jiang (ORCID: https://orcid.org/0009-0006-0509-0019)
- Man Lu (ORCID: https://orcid.org/0000-0002-3455-834X)
- Zhichao Zheng (ORCID: https://orcid.org/0000-0003-3064-976X)
- Yanxin Luo (ORCID: https://orcid.org/0000-0002-5200-3997)
- Xiaoping Chen (ORCID: https://orcid.org/0000-0001-8992-9286)
- Guolong Chen (ORCID: https://orcid.org/0000-0002-5899-7312)
- Huiming Lu (ORCID: https://orcid.org/0000-0002-2743-7419)
Institutions
- Sun Yat-sen University (CN)
- Sun Yat-sen University Cancer Center (CN)
- State Key Laboratory of Oncology in South China
Publication Details
- Journal
- Frontiers in Artificial Intelligence
- Published
- 2025-11-25
- DOI
- https://doi.org/10.3389/frai.2025.1678292
- Primary Topic
- Bladder and Urothelial Cancer Treatments
- Type
- article
- Field-Weighted Citation Impact
- 0.00