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TRUVACE RECORD VERSION
record: TRV-2026-0577
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-07-27T06:09:17.804254Z
status: published
lens: trace
sector: health
headline: Research and application of machine learning models based on multimodal big data for precise transfusion management in acute myeloid leukaemia
dek: 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 li…
gain_title: Machine learning models integrating multimodal big data improved precision transfusion management for AML patients by predicting transfusion demand and assessing transfusion reaction risks.
problem_title: Deployment of multimodal machine learning for AML transfusion management is limited by data privacy protection, data standardisation across platforms, and model interpretability for clinical adoption.
trace_subject: machine learning models based on multimodal big data for precision transfusion management in acute myeloid leukaemia
gain_reading: Machine learning models integrating multimodal big data improved precision transfusion management for AML patients by predicting transfusion demand and assessing transfusion reaction risks.
gain_evidence: Machine learning models have shown promising performance in predicting transfusion demand and assessing transfusion reaction risks | Multimodal big data demonstrates substantial value in optimising transfusion strategies for AML patients
problem_reading: Deployment of multimodal machine learning for AML transfusion management is limited by data privacy protection, data standardisation across platforms, and model interpretability for clinical adoption.
problem_evidence: major challenges persist, including data privacy protection, data standardisation across platforms and model interpretability for clinical adoption
quick_read: This peer-reviewed review published July 24, 2026 synthesized recent progress on machine learning models that integrate multimodal big data such as electronic health records, genomic and proteomic data to guide transfusion support for acute myeloid leukaemia, a highly heterogeneous malignancy where transfusion is essential.

The synthesis matters because it moves transfusion practice from empirically guided approaches toward individualized prediction of demand and risk, but the source itself flags that privacy protection, data standardisation, and interpretability remain unresolved for clinical adoption, with federated learning and explainable AI proposed as next steps.
limitation: Clinical adoption is constrained by unresolved issues of data privacy protection, cross-platform data standardisation, and model interpretability.
tag: Automated dual reading
key_points: Review focused on multimodal data integration including electronic health records, genomic, proteomic and other high-dimensional datasets for AML transfusion management. | Analysed algorithms included decision trees, random forests and neural networks applied to transfusion demand prediction and reaction risk assessment. | Representative clinical case studies were cited as demonstrating practical utility of the models. | Authors identified future directions involving federated learning and explainable artificial intelligence to address current limitations.
rundown: The review systematically examined literature on integrating electronic health records, genomic, proteomic and other high-dimensional datasets, and analysed decision trees, random forests and neural networks for transfusion demand prediction and reaction risk assessment in AML.

While case studies were reported to support practical utility and improved transfusion safety and clinical outcomes, the authors noted persistent barriers around privacy, standardisation, and interpretability, pointing to federated learning and explainable AI as future directions.
sources:
- peer_reviewed | Transfusion Medicine | https://doi.org/10.1111/tme.70105 | 2026-07-24
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