Explainable transformer-based deep survival analysis in childhood acute lymphoblastic leukemia.
Journal:
Computers in biology and medicine
PMID:
40198985
Abstract
BACKGROUND: Acute lymphoblastic leukemia (ALL) is the most common type of leukemia among children and adolescents and can be life-threatening. The incidence of new cases has been increasing in recent years. Developing a predictive model to forecast the risk of death can help improve survival rates by enabling clinicians to provide timely and effective treatments. Traditional statistical survival models are limited by predefined assumptions, while current deep survival models, despite their flexibility, struggle with capturing complex and dynamic feature dependencies. Transformers provide a promising solution by using self-attention and multi-head attention mechanisms to overcome these challenges. Moreover, building on recent work in interpretable medical AI, the combination of Transformers and explainable methods can quantify the contributions of each feature to the survival probability prediction.