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

doi:10.1007/978-3-030-59137-3_18

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

Medicine

Producción científica: Capítulo en Libro/Reporte/Acta de conferencia › Contribución 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 original	English
Título de la publicación alojada	Artificial Intelligence in Medicine - 18th International Conference on Artificial Intelligence in Medicine, AIME 2020, Proceedings
Editores	Martin Michalowski, Robert Moskovitch
Editorial	Springer Science and Business Media Deutschland GmbH
Páginas	192-202
Número de páginas	11
ISBN (versión impresa)	9783030591366
DOI	https://doi.org/10.1007/978-3-030-59137-3_18
Estado	Published - 2020
Evento	18th International Conference on Artificial Intelligence in Medicine, AIME 2020 - Minneapolis, United States Duración: ago. 25 2020 → ago. 28 2020

Serie de la publicación

Nombre	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen	12299 LNAI
ISSN (versión impresa)	0302-9743
ISSN (versión digital)	1611-3349

Conference

Conference	18th International Conference on Artificial Intelligence in Medicine, AIME 2020
País/Territorio	United States
Ciudad	Minneapolis
Período	8/25/20 → 8/28/20

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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10.1007/978-3-030-59137-3_18

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Rad, J., Cheng, C., Quinn, J. G., Abidi, S., Liwski, R., & Abidi, S. S. R. (2020). An AI-Driven Predictive Modelling Framework to Analyze and Visualize Blood Product Transactional Data for Reducing Blood Products’ Discards. En M. Michalowski, & R. Moskovitch (Eds.), Artificial Intelligence in Medicine - 18th International Conference on Artificial Intelligence in Medicine, AIME 2020, Proceedings (pp. 192-202). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12299 LNAI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-59137-3_18

An AI-Driven Predictive Modelling Framework to Analyze and Visualize Blood Product Transactional Data for Reducing Blood Products’ Discards. / Rad, Jaber; Cheng, Calvino; Quinn, Jason G. et al.
Artificial Intelligence in Medicine - 18th International Conference on Artificial Intelligence in Medicine, AIME 2020, Proceedings. ed. / Martin Michalowski; Robert Moskovitch. Springer Science and Business Media Deutschland GmbH, 2020. p. 192-202 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12299 LNAI).

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

Rad, J, Cheng, C, Quinn, JG, Abidi, S, Liwski, R & Abidi, SSR 2020, An AI-Driven Predictive Modelling Framework to Analyze and Visualize Blood Product Transactional Data for Reducing Blood Products’ Discards. En M Michalowski & R Moskovitch (eds.), Artificial Intelligence in Medicine - 18th International Conference on Artificial Intelligence in Medicine, AIME 2020, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12299 LNAI, Springer Science and Business Media Deutschland GmbH, pp. 192-202, 18th International Conference on Artificial Intelligence in Medicine, AIME 2020, Minneapolis, United States, 8/25/20. https://doi.org/10.1007/978-3-030-59137-3_18

Rad J, Cheng C, Quinn JG, Abidi S, Liwski R, Abidi SSR. An AI-Driven Predictive Modelling Framework to Analyze and Visualize Blood Product Transactional Data for Reducing Blood Products’ Discards. En Michalowski M, Moskovitch R, editores, Artificial Intelligence in Medicine - 18th International Conference on Artificial Intelligence in Medicine, AIME 2020, Proceedings. Springer Science and Business Media Deutschland GmbH. 2020. p. 192-202. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-59137-3_18

Rad, Jaber ; Cheng, Calvino ; Quinn, Jason G. et al. / An AI-Driven Predictive Modelling Framework to Analyze and Visualize Blood Product Transactional Data for Reducing Blood Products’ Discards. Artificial Intelligence in Medicine - 18th International Conference on Artificial Intelligence in Medicine, AIME 2020, Proceedings. editor / Martin Michalowski ; Robert Moskovitch. Springer Science and Business Media Deutschland GmbH, 2020. pp. 192-202 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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

Resumen

Serie de la publicación

Conference

Nota bibliográfica

ASJC Scopus Subject Areas

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