An Ensemble Model for Acute Myeloid Leukemia Risk Stratification Recommendations by Combining Machine Learning with Clinical Guidelines

Acute Myeloid Leukemia (AML) is a complex disease requiring accurate risk stratification for effective treatment planning. This study introduces an innovative ensemble machine learning model integrated with the European LeukemiaNet (ELN) 2022 recommendations to enhance AML risk stratification. The model demonstrated superior performance by utilizing a comprehensive dataset of 1,213 patients from National Taiwan University Hospital (NTUH) and an external cohort of 2,113 patients from UK-NCRI trials. On the external cohort, it improved a concordance index (c-index) from 0.61 to 0.64 and effectively distinguished three different risk levels with median hazard ratios ranging from 18% to 50% improved. Key insights were gained from the discovered significant features influencing risk prediction, including age, genetic mutations, and hematological parameters. Notably, the model identified specific cytogenetic and molecular alterations like TP53 , IDH2 , SRSF2 , STAG2 , KIT , TET2 , and karyotype (-5, -7, -15, inv(16)), alongside age and platelet counts. Additionally, the study explored variations in the effectiveness of hematopoietic stem cell transplantation (HSCT) across different risk levels, offering new perspectives on treatment effects. In summary, this study develops an ensemble model based on the NTUH cohort to deliver improved performance in AML risk stratification, showcasing the potential of integrating machine learning techniques with medical guidelines to enhance patient care and personalized medicine. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study did not receive any funding. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Research ethics committee of National Taiwan University Hospital gave ethical approval for this work. Written informed consent was obtained from all participants in accordance with the Declaration of Helsinki, under the approval number 201709072RINC. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The data generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request. The UK-NCRI dataset in the present study is available online at https://www.nature.com/articles/s41467-022-32103-8#Sec1