Search Results

From demand forecasting to inventory ordering decisions for red blood cells through integrating machine learning, statistical modeling, and inventory optimization.

• Authors : Li, Na; Arnold, Donald M.; Down, Douglas G.

Subjects: ERYTHROCYTES; DEMAND forecasting; STATISTICAL models

• Source: Transfusion; Jan2022, Vol. 62 Issue 1, p87-99, 13p

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A robust autonomous method for blood demand forecasting.

• Authors : Turkulainen EV; Research and Development, Finnish Red Cross Blood Service, Helsinki, Finland.; Wemelsfelder ML

Subjects: Forecasting*; Finland ; Humans

• Source: Transfusion [Transfusion] 2022 Jun; Vol. 62 (6), pp. 1261-1268. Date of Electronic Publication: 2022 Apr 05.Publisher: American Association Of Blood Banks Country of Publication: United States NLM ID: 0417360 Publication Model: Print-Electronic Cited Medium:

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An unsupervised learning approach to identify immunoglobulin utilization patterns using electronic health records.

• Authors : Riazi, Kiarash; Ly, Mark; Barty, Rebecca

Subjects: ELECTRONIC health records; K-means clustering; DEMAND forecastingCALGARY (Alta.)

• Source: Transfusion; Dec2023, Vol. 63 Issue 12, p2234-2247, 14p

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A data‐informed system to manage scarce blood product allocation in a randomized controlled trial of convalescent plasma.

• Authors : Li, Na (AUTHOR); Zeller, Michelle P. (AUTHOR); Shih, Andrew W. (AUTHOR)

Subjects: CONVALESCENT plasma; BLOOD products; RANDOMIZED controlled trialsQUEBEC (Quebec); CANADA

• Source: Transfusion. Dec2022, Vol. 62 Issue 12, p2525-2538. 14p.

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Trends in IVIG use at a tertiary care Canadian center and impact of provincial use mitigation strategies: 10-year retrospective study with interrupted time series analysis.

• Authors : Murphy, Malia S.Q.; Tinmouth, Alan; Goldman, Mindy

Subjects: TIME series analysis; TERTIARY care; BLOOD productsONTARIO; CANADA

• Source: Transfusion; Jun2019, Vol. 59 Issue 6, p1988-1996, 9p, 3 Charts, 2 Graphs

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Better than 'normal': Describing the distribution of and outliers in RBC usage.

• Authors : Murphy, Colin; Silva de Leonardi, Noah

• Source: Transfusion; Apr2022, Vol. 62 Issue 4, p817-825, 9p

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A data-driven approach to determine daily platelet order quantities at hospitals.

• Authors : Mirjalili M; Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario, Canada.; Abouee-Mehrizi H

Subjects: Blood Platelets* ; Hospitals*; Humans

• Source: Transfusion [Transfusion] 2022 Oct; Vol. 62 (10), pp. 2048-2056. Date of Electronic Publication: 2022 Sep 05.Publisher: American Association Of Blood Banks Country of Publication: United States NLM ID: 0417360 Publication Model: Print-Electronic Cited Medium:

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Adapting to supply-and-demand emerging trends for antigen-negative red blood cell units.

• Authors : Trépanier P; Héma-Québec, Medical Affairs and Innovation, Québec City, Québec, Canada.; Chevrier MC

Subjects: Donor Selection*/Donor Selection*/Donor Selection*/methods ; Erythrocyte Transfusion*/Erythrocyte Transfusion*/Erythrocyte Transfusion*/methods; Blood Group Antigens/Blood Group Antigens/Blood Group Antigens/*blood

• Source: Transfusion [Transfusion] 2021 May; Vol. 61 (5), pp. 1489-1494. Date of Electronic Publication: 2021 Jan 29.Publisher: American Association Of Blood Banks Country of Publication: United States NLM ID: 0417360 Publication Model: Print-Electronic Cited Medium:

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Proceedings of the 2022 NHLBI and OASH state of the science in transfusion medicine symposium.

• Authors : Custer, Brian; Bloch, Evan M.; Bryant, Barbara J.

Subjects: BLOOD transfusion; DONOR blood supply; MEDICAL personnelUNITED States. Dept. of Health & Human Services; NATIONAL Institutes of Health (U.S.)

• Source: Transfusion; May2023, Vol. 63 Issue 5, p1074-1091, 18p

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Machine learning in transfusion medicine: A scoping review.

• Authors : Maynard S; Medical Sciences Division, Radcliffe Department of Medicine, University of Oxford, Oxford, UK.; NIHR Blood and Transplant Research Unit in Data Driven Transfusion Practice, Nuffield Division of Clinical Laboratory Sciences, Radcliffe Department of Medicine, University of Oxford, Oxford, UK.

Subjects: Transfusion Medicine*; Humans ; Machine Learning

• Source: Transfusion [Transfusion] 2024 Jan; Vol. 64 (1), pp. 162-184. Date of Electronic Publication: 2023 Nov 10.Publisher: American Association Of Blood Banks Country of Publication: United States NLM ID: 0417360 Publication Model: Print-Electronic Cited Medium:

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