ArticleDigital health
Construction of a prognostic hierarchical model of intermediate-risk acute myeloid leukemia based on machine learning.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- A two-gene prognostic model for lung adenocarcinoma using summary-data-based Mendelian randomization analysis and Cox proportional hazards regression.Future science OA · 2026Article
- Artificial Intelligence for Personalized Management of Acute Myeloid Leukemia.Journal of personalized medicine · 2026Review
- Rewriting Doom: Serial Pathways from eHealth Use to Reduced Cancer Fatalism in Family Cancer History Groups.Journal of cancer education : the official journal of the American Association for Cancer Education · 2026Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: To refine prognostic stratification for intermediate-risk acute myeloid leukemia (IR-AML) by leveraging machine learning to integrate clinical and genomic features and relate them to survival outcomes. Methods: We conducted a two-cohort study comprising a single-center development cohort from Beijing Tsinghua Changgung Hospital ( Results: In internal cross-validation, tree-based learners showed strong discrimination (best GBDT AUROC 0.98, 95% confidence interval (CI) 0.91-1.00). On the external TCGA cohort, GBDT achieved AUROC 0.73 (95% CI 0.62-0.83). Model-agnostic explanations (Shapley additive explanations) consistently highlighted white blood cell count, age, transplantation, and Conclusion: An interpretable machine learning framework built from accessible clinical and genomic variables provided quantitative risk discrimination for IR-AML across development and external test cohorts, supporting individualized risk assessment and informing refinement of prognostic stratification.
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