An AI-Driven Predictive Modelling Framework to Analyze and Visualize Blood Product Transactional Data for Reducing Blood Products’ Discards

Jaber Rad, Calvino Cheng, Jason G. Quinn, Samina Abidi, Robert Liwski, Syed Sibte Raza Abidi

Producción científica: Capítulo en Libro/Reporte/Acta de conferenciaContribución a la conferencia

5 Citas (Scopus)

Resumen

Maintaining an equilibrium between shortage and wastage in blood inventories is challenging due to the perishable nature of blood products. Research in blood product inventory management has predominantly focused on reducing wastage due to outdates (i.e. expiry of the blood product), whereas wastage due to discards, related to the lifecycle of a blood product, is not well investigated. In this study, we investigate machine learning methods to analyze blood product transition sequences in a large real-life transactional dataset of Red Blood Cells (RBC) to predict potential blood product discard. Our prediction models are able to predict with 79% accuracy potential discards based on the blood product’s current transaction data. We applied advanced data visualizations methods to develop an interactive blood inventory dashboard to help laboratory managers to probe blood units’ lifecycles to identify discard causes.

Idioma originalEnglish
Título de la publicación alojadaArtificial Intelligence in Medicine - 18th International Conference on Artificial Intelligence in Medicine, AIME 2020, Proceedings
EditoresMartin Michalowski, Robert Moskovitch
EditorialSpringer Science and Business Media Deutschland GmbH
Páginas192-202
Número de páginas11
ISBN (versión impresa)9783030591366
DOI
EstadoPublished - 2020
Evento18th International Conference on Artificial Intelligence in Medicine, AIME 2020 - Minneapolis, United States
Duración: ago. 25 2020ago. 28 2020

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen12299 LNAI
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conference

Conference18th International Conference on Artificial Intelligence in Medicine, AIME 2020
País/TerritorioUnited States
CiudadMinneapolis
Período8/25/208/28/20

Nota bibliográfica

Funding Information:
This research is supported by the Blood Efficiency Accelerator Award by Canadian Blood Services. We thank the NSHA Central Zone Blood Transfusion Services for providing us the dataset and supporting the project.

Funding Information:
Acknowledgements. This research is supported by the Blood Efficiency Accelerator Award by Canadian Blood Services. We thank the NSHA Central Zone Blood Transfusion Services for providing us the dataset and supporting the project.

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

ASJC Scopus Subject Areas

  • Theoretical Computer Science
  • General Computer Science

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