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

Medicine

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

5 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	Artificial Intelligence in Medicine - 18th International Conference on Artificial Intelligence in Medicine, AIME 2020, Proceedings
Editors	Martin Michalowski, Robert Moskovitch
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	192-202
Number of pages	11
ISBN (Print)	9783030591366
DOIs	https://doi.org/10.1007/978-3-030-59137-3_18
Publication status	Published - 2020
Event	18th International Conference on Artificial Intelligence in Medicine, AIME 2020 - Minneapolis, United States Duration: Aug 25 2020 → Aug 28 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12299 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	18th International Conference on Artificial Intelligence in Medicine, AIME 2020
Country/Territory	United States
City	Minneapolis
Period	8/25/20 → 8/28/20

Bibliographical note

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

Access to Document

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

Cite this

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. In 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).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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. in 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. In Michalowski M, Moskovitch R, editors, 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

Abstract

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Bibliographical note

ASJC Scopus Subject Areas

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