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.
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A JavaScript library for building user interfaces (https://reactjs.org).
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Data-Driven Documents, a JavaScript library for manipulating documents based on data (https://d3js.org).
References
Guan, L., et al.: Big data modeling to predict platelet usage and minimize wastage in a tertiary care system. Proc. Natl. Acad. Sci. U. S. A. 114(43), 11368–11373 (2017). https://doi.org/10.1073/pnas.1714097114
Quinn, J., et al.: The successful implementation of an automated institution-wide assessment of hemoglobin and ABO typing to dynamically estimate red blood cell inventory requirements. Transfusion 59(7), 2203–2206 (2019). https://doi.org/10.1111/trf.15272
van Dijk, N., Haijema, R., van Der Wal, J., Sibinga, C.S.: Blood platelet production: a novel approach for practical optimization. Transfusion 49(3), 411–420 (2009)
Haijema, R., Van Dijk, N., Van Der Wal, J., Sibinga, C.S.: Blood platelet production with breaks: optimization by SDP and simulation. Int. J. Prod. Econ. 121(2), 464–473 (2009). https://doi.org/10.1016/j.ijpe.2006.11.026
Stanger, S.H.W., Yates, N., Wilding, R., Cotton, S.: Blood inventory management: hospital best practice. Transfus. Med. Rev. 26(2), 153–163 (2012)
Bertsimas, D., Kallus, N.: From predictive to prescriptive analytics. Manage. Sci. (2019). https://doi.org/10.1287/mnsc.2018.3253
Altman, N.S.: An introduction to kernel and nearest-neighbor nonparametric regression. Am. Stat. 46(3), 175–185 (1992). https://doi.org/10.2307/2685209
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001). https://doi.org/10.1023/A:101093340324
Cheng, C.K., Trethewey, D., Sadek, I.: Comprehensive survey of red blood cell unit life cycle at a large teaching institution in eastern Canada. Transfusion 50(1), 160–165 (2010). https://doi.org/10.1111/j.1537-2995.2009.02375.x
Pedregosa, F., et al.: Scikit-learn: machine learning in {P}ython. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Bengio, S., Vinyals, O., Jaitly, N., Shazeer, N.: Scheduled sampling for sequence prediction with recurrent neural networks. In: Cortes, C., Lawrence, N.D., Lee, D.D., Sugiyama, M., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 28, pp. 1171–1179. Curran Associates, Inc. (2015)
Villegas, R., Yang, J. Hong, S. Lin, X., Lee, H.: Decomposing motion and content for natural video sequence prediction. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (2019)
Qiao, Y., Si, Z., Zhang, Y., Abdesslem, F.B., Zhang, X., Yang, J.: A hybrid Markov-based model for human mobility prediction. Neurocomputing 278, 99–109 (2018). https://doi.org/10.1016/j.neucom.2017.05.101
Pitkow, J., Pirolli, P.: Mining longest repeating subsequences to predict world wide web surfing. In: The 2nd USENIX Symposium on Internet Technologies & System (1999)
Deshpande, M., Karypis, G.: Selective Markov models for predicting web page accesses. ACM Trans. Internet Technol. 4(2), 163–184 (2004)
Gueniche, T., Fournier-Viger, P., Tseng, V.S.: Compact prediction tree: a lossless model for accurate sequence prediction. In: Motoda, H., Wu, Z., Cao, L., Zaiane, O., Yao, M., Wang, W. (eds.) ADMA 2013. LNCS (LNAI), vol. 8347, pp. 177–188. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-53917-6_16
Gueniche, T., Fournier-Viger, P., Raman, R., Tseng, V.S.: CPT+: decreasing the time/space complexity of the compact prediction tree. In: Cao, T., Lim, E.P., Zhou, Z.H., Ho, T.B., Cheung, D., Motoda, H. (eds.) PAKDD 2015. LNCS (LNAI), vol. 9078, pp. 625–636. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-18032-8_49
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.
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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. In: Michalowski, M., Moskovitch, R. (eds) Artificial Intelligence in Medicine. AIME 2020. Lecture Notes in Computer Science(), vol 12299. Springer, Cham. https://doi.org/10.1007/978-3-030-59137-3_18
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