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December 8, 2025BloodOpen Access

Machine learning using bayesian networks to predict response in patients with newly diagnosed Acute Myeloid Leukemia

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Authors

OCOnyee ChanNANajla Al AliSYSeongseok Yun

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Overview

This predictive model estimates treatment response in AML patients using machine learning, highlighting genetic interactions and clinical features.

Key Points

  • To develop a model predicting response to frontline therapy in newly diagnosed AML patients using Bayesian networks.
  • Conducted a retrospective cohort analysis of AML patients treated at Moffitt Cancer Center.
  • Developed a Bayesian network model from 80% of the dataset, testing on the remaining 20%.
  • Applied feature selection integrating Random Forest rankings and expert knowledge to select key features.
  • Identified 651 patients treated with either intensive chemotherapy or hypomethylating agent with venetoclax.
  • The model achieved an AUC of 0.74 with 70% accuracy in predicting treatment response.
  • Ten key features were retained, including age, cytogenetic risk, and critical mutations.

Cite This Study

Chan et al. (2025) studied this question.

synapsesocial.com/papers/69362f6e4fa91c937236e13dhttps://doi.org/10.1182/blood-2025-4357
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Machine Learning for Predicting Therapeutic Outcomes in Acute Myeloid Leukemia Patients2024 · 2 citations
  2. 2Cytogenetic and Molecular Genetic Driven Prediction of Response to First-Treatment and Prognosis in Acute Myeloid Leukemia: A Retrospective Cohort Study.2025
  3. 3AI-derived prediction of response and relapse to venetoclax plus hypomethylating agent based therapy in Acute Myeloid Leukemia2025
  4. 4Multi-drug algorithm to accurately predict best first-line treatments in newly-diagnosed acute myeloid leukemia (AML).2024
  5. 5Construction and evaluation of a prognostic model for patients with Acute Myeloid Leukemia2025