ASH 2024 | Using machine learning to improve risk-stratification of AML: assessing age as a prognostic factor
Jan-Niklas Eckardt, MSc, MHBA, Technical University Dresden, Dresden, Germany, comments on the importance of considering age as a modulator of risk in acute myeloid leukemia (AML) patients, highlighting that conventional risk markers may not be universally applicable across different age groups. Dr Eckardt emphasizes that age-stratified game theory-informed machine learning of molecular alterations highlighted age as a significant factor; however, a more nuanced understanding of patient biology is needed to assess risk accurately. This interview took place at the 66th ASH Annual Meeting and Exposition, held in San Diego, CA.
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