aceConvolutional neural network for red blood cell transfusion prediction: A machine learning based study
Background Subjective, inconsistent clinician judgement on red blood cell (RBC) transfusion demand causes unnecessary blood wastage, increased perioperative safety risks, and poorly balanced hospital blood inventory. To tackle these persistent clinical limitations, we developed a workflow-adapted clinical decision support tool: a one-dimensional convolutional neural network (1D CNN) trained on routinely acquired haematological and clinical variables to stratify inpatient RBC transfusion volume categories. Method…
In a retrospective single-hospital cohort, the model forecast routine transfusion volume categories from 19 pre-transfusion variables, correctly identifying the dominant 2-unit class.
The model under-detected high-volume transfusion events, with weak performance in the minority classes of 3 units and 5 or more units, which limits clinical reliability for these cases.
The model was developed and tested in a single-centre retrospective cohort and under-detected high-volume transfusions, so generalisability and usefulness for rare, large transfusions remain unproven.
Evidence
- Peer-reviewedTransfusion Medicine2026-10-07
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Truvace Impact Record TRV-2026-1784, v1: “Convolutional neural network for red blood cell transfusion prediction: A machine learning based study.” Truvace, 2026-10-18. /record/TRV-2026-1784 (accessed at citation time). sha256 0c815903b3a2e3ab…
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