Research and application of machine learning models based on multimodal big data for precise transfusion management in acute myeloid leukaemia.

Abstract

Acute myeloid leukaemia (AML) is a highly heterogeneous haematologic malignancy in which transfusion support represents an essential component of comprehensive patient care. This review aims to provide an updated synthesis of recent progress in the development and clinical application of machine learning models based on multimodal big data for precision transfusion management in AML, addressing the persistent limitations of conventional, empirically guided transfusion practices. We systematically reviewed the literature on multimodal data integration-including electronic health records, genomic, proteomic and other high-dimensional datasets-in the context of AML transfusion management. The applications of machine learning algorithms such as decision trees, random forests and neural networks were analysed in the contexts of transfusion demand prediction and transfusion reaction risk assessment, with reference to representative clinical case studies demonstrating their practical utility. Multimodal big data demonstrates substantial value in optimising transfusion strategies for AML patients. Machine learning models have shown promising performance in predicting transfusion demand and assessing transfusion reaction risks, with clinical case studies supporting their practical utility. However, major challenges persist, including data privacy protection, data standardisation across platforms and model interpretability for clinical adoption. The integration of multimodal big data with advanced machine learning methodologies holds substantial promise for enabling precision, individualised transfusion management in AML. Future directions involving federated learning and explainable artificial intelligence are anticipated to address current limitations, ultimately contributing to improved transfusion safety and clinical outcomes in AML patients.