This paper is in the following e-collection/theme issue:

Decision Support for Health Professionals (1185) Clinical Informatics (1022) Machine Learning (1465) Clinical Information and Decision Making (1407)

Published on in Vol 10, No 2 (2022): February

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/29978, first published .
Reduction of Platelet Outdating and Shortage by Forecasting Demand With Statistical Learning and Deep Neural Networks: Modeling Study

Reduction of Platelet Outdating and Shortage by Forecasting Demand With Statistical Learning and Deep Neural Networks: Modeling Study

Reduction of Platelet Outdating and Shortage by Forecasting Demand With Statistical Learning and Deep Neural Networks: Modeling Study

Authors of this article:

Maximilian Schilling1 Author Orcid Image ;   Lennart Rickmann1 Author Orcid Image ;   Gabriele Hutschenreuter2 Author Orcid Image ;   Cord Spreckelsen3 Author Orcid Image
Article Authors Cited by (8) Tweetations (2) Metrics

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  2. Evans H, Murphy M, Foy R, Dhiman P, Green L, Kotze A, von Neree L, Palmer A, Robinson S, Shah A, Tomini F, Trompeter S, Warnakulasuriya S, Wong W, Stanworth S. Harnessing the potential of data‐driven strategies to optimise transfusion practice. British Journal of Haematology 2024;204(1):74 View
  3. Li N, Pham T, Cheng C, McElfresh D, Metcalf R, Russell W, Birch R, Yurkovich J, Montemayor-Garcia C, Lane W, Tobian A, Roubinian N, Seheult J, Goel R. Blood Demand Forecasting and Supply Management: An Analytical Assessment of Key Studies Utilizing Novel Computational Techniques. Transfusion Medicine Reviews 2023;37(4):150768 View
  4. Maynard S, Farrington J, Alimam S, Evans H, Li K, Wong W, Stanworth S. Machine learning in transfusion medicine: A scoping review. Transfusion 2024;64(1):162 View
  5. Engelke M, Schmidt C, Baldini G, Parmar V, Hosch R, Borys K, Koitka S, Turki A, Haubold J, Horn P, Nensa F. Optimizing platelet transfusion through a personalized deep learning risk assessment system for demand management. Blood 2023;142(26):2315 View
  6. Chideme C, Chikobvu D, Makoni T. The Impact of Age Group in Hierarchical Forecasting of Monthly Blood Donations in Zimbabwe. Risk Management and Healthcare Policy 2024;Volume 17:311 View
  7. Pérez‐Aliaga A, Ayerra I, Sánchez‐Guillén J, López F, Puente F, Aranda A, Domingo J, Garcés C. Routine results of an algorithm for managing the production of blood components. Vox Sanguinis 2024;119(6):541 View
  8. Miri-Moghaddam E, Bizhaem S, Moezzifar Z, Salmani F. Long-term prediction of Iranian blood product supply using LSTM: a 5-year forecast. BMC Medical Informatics and Decision Making 2024;24(1) View