High temporal resolution prediction of mortality risk for single AML patient via deep learning

Acute myeloid leukemia (AML) is highly heterogeneous, necessitating personalized prognosis prediction and treatment strategies. Many of the current patient classifications are based on molecular features. Here, we classified the primary AML patients by predicted death risk curves and investigated the survival-directly-related molecular features. We developed a deep learning model to predict 5-year continuous-time survival probabilities for each patient and converted them to death risk curves. This method captured disease progression dynamics with high temporal resolution and identified seven patient groups with distinct risk peak timing. Based on clusters of death risk curves, we identified two robust AML prognostic biomarkers and discovered a subgroup within the European LeukemiaNet (ELN) 2017 Favorable category with an extremely poor prognosis. Additionally, we developed a web tool, De novo AML Prognostic Prediction (DAPP), for individualized prognosis prediction and expression perturbation simulation. This study utilized deep learning-based continuous-time risk modeling coupled with clustering-predicted risk distributions, facilitating dissecting time-specific molecular features of disease progression.