ArticleScientifica2026
Explainable Survival Modeling in Pediatric Hematopoietic Stem Cell Transplantation Using Landmark-Based Machine Learning.
Article in Scientifica, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Machine learning survival models can outperform conventional Cox regression in heterogeneous pediatric HSCT cohorts, but clinical adoption is limited by concerns regarding information leakage, time-dependent structure, and interpretability. We evaluated explainable survival modeling aligned to real-world decision points before transplantation and after early post-transplant complications. Materials and Methods: In a single-center cohort of 839 transplant-naïve children (< 14 years) undergoing HSCT (2010-2016), overall survival was modeled from infusion to death or last follow-up. A baseline model used only pretransplant variables (age at HSCT, primary diagnostic group, stem cell source, and donor HLA type). To prevent leakage from postinfusion events, a day + 100 landmark cohort ( Results: Overall mortality was 28.1% (236/839). Baseline Cox violated proportional hazards (global Conclusion: An information-leakage-free, landmark-based XAI framework allows clinically credible survival prediction in pediatric HSCT, with random survival forests outperforming Cox regression and providing transparent cohort-level and patient-level explanations suitable for clinical communication and risk updating.
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