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. 2024 Jan;64(1):162-184.
doi: 10.1111/trf.17582. Epub 2023 Nov 10.

Machine learning in transfusion medicine: A scoping review

Suzanne Maynard  1   2   3 , Joseph Farrington  4 , Samah Alimam  5 , Hayley Evans  2 , Kezhi Li  4 , Wai Keong Wong  6 , Simon J Stanworth  1   2   3
Affiliations

Machine learning in transfusion medicine: A scoping review

Suzanne Maynard et al. Transfusion. 2024 Jan.
No abstract available

Keywords: artificial intelligence; machine learning; patient blood management; transfusion.

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Conflict of interest statement

The authors have disclosed no conflicts of interest.

Figures

FIGURE 1
FIGURE 1
A typical supervised machine learning workflow. Supervised learning requires a labeled dataset in which we already know the outcome we are trying to predict, the output label, for each example. To estimate how well the model will perform on future, unseen, examples, we hold back a portion of the data (the test set) from the training process and use this dataset to evaluate the model's performance. If the test dataset comes from the same site(s) as the training dataset, then it may be described as an internal validation set. If the test dataset comes from one or more different sites or settings, then it may be described as an external validation set. [Color figure can be viewed at wileyonlinelibrary.com]
FIGURE 2
FIGURE 2
Modified Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) flow chart of the search results. [Color figure can be viewed at wileyonlinelibrary.com]
FIGURE 3
FIGURE 3
Histogram with cumulative frequency line of papers by publication year. [Color figure can be viewed at wileyonlinelibrary.com]
FIGURE 4
FIGURE 4
Bar charts of papers included after full‐text review: (A) split by group and (B) split by subgroup within predicting transfusion. [Color figure can be viewed at wileyonlinelibrary.com]
See this image and copyright information in PMC

References

    1. Tinegate H, Pendry K, Murphy M, Babra P, Grant‐Casey J, Hopkinson C, et al. Where do all the red blood cells (RBCs) go? Results of a survey of RBC use in England and North Wales in 2014. Transfusion. 2016;56(1):139–145. 10.1111/trf.13342 - DOI - PubMed
    1. European Committee on Blood Transfusion . The collection, testing and use of blood and blood components in Europe. 2017, 2018 and 2019 report. Cited 2023 July 24. Available from: https://www.edqm.eu/en/reports-blood
    1. Delaney M, Wendel S, Bercovitz RS, Cid J, Cohn C, Dunbar NM, et al. Transfusion reactions: prevention, diagnosis, and treatment. Lancet. 2016;388(10061):2825–2836. 10.1016/S0140-6736(15)01313-6 - DOI - PubMed
    1. Desborough MJR, Hockley B, Sekhar M, Burroughs AK, Stanworth SJ, Jairath V, et al. Patterns of blood component use in cirrhosis: a nationwide study. Liver Int. 2016;36(4):522–529. 10.1111/liv.12999 - DOI - PubMed
    1. Bruun MT, Pendry K, Georgsen J, Manzini P, Lorenzi M, Wikman A, et al. Patient Blood Management in Europe: surveys on top indications for red blood cell use and Patient Blood Management organization and activities in seven European university hospitals. Vox Sang. 2016;111(4):391–398. 10.1111/vox.12435 - DOI - PubMed

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